#! /usr/bin/env python # -*- coding: iso-8859-15 -*- """ Created on Fri Oct 2 12:29:36 2015 @author: kervella """ # gravi_visual_class.py import in other directory import sys #sys.path.insert(0, '../../gravi_visual') import gravi_visual_class import matplotlib.pyplot as plt import matplotlib.animation as animation import numpy as np import os from glob import glob from optparse import OptionParser def gravi_movie_rawdata(filename): #filename = "/Users/kervella/Pipelines/python_tools/GRAVITY.2015-09-14T15-03-39.fits" filename = "/Volumes/GRAVITY-1TB/2015-10-15/onsky/GRAVITY.2015-10-16T03-35-01.fits" title_ft = "Achernar (GRAVITY, 2015-10-16)" title_sc = "Achernar (GRAVITY, 2015-10-16)" moviefile_ft = 'gravity-ft-achernar.mp4' moviefile_sc = "gravity-sc-achernar.mp4" plt.close('all') #preproc = gravi_visual_class.Preproc("/Users/kervella/Pipelines/python_tools/gravi_preproc_GRAVITY.2015-09-16T00-50-06-MR.fits") #raw = gravi_visual_class.Rawdata("/Users/kervella/Pipelines/python_tools/GRAVITY.2015-09-14T14-11-18.fits") raw = gravi_visual_class.Rawdata(filename) fig1=plt.figure(figsize=(5,12)) #fig1.tight_layout() plt.title(title_ft) plt.xlabel("Spectral channel",fontsize=15) plt.ylabel("Fringe Tracker output",fontsize=15) ims=[] step = 40 maxl = np.percentile(raw.data_ft,97) minl = np.percentile(raw.data_ft,1) for add in range(0,1000): ims.append((plt.imshow(raw.data_ft[add*step,:,:],aspect='auto',interpolation='none',cmap='afmhot',vmin=minl,vmax=maxl),)) im_ani = animation.ArtistAnimation(fig1, ims, interval=100, repeat_delay=3000, blit=True) im_ani.save(moviefile_ft, dpi=150, bitrate=3000, metadata={'artist':'Pierre Kervella'}) #plt.show() fig2=plt.figure(figsize=(20,10)) plt.title(title_sc) plt.xlabel("Spectral channel",fontsize=15) plt.ylabel("Science Combiner 48 ouptuts",fontsize=15) ims=[] maxl = np.percentile(raw.data_sc,98) minl = np.percentile(raw.data_sc,1) for add in range(0,raw.nframe_sc): ims.append((plt.imshow(raw.data_sc[add,:,:],aspect='auto',interpolation='none',cmap='afmhot',vmin=minl,vmax=maxl),)) #plt.imshow(preproc.spectrum_ft[16,0:5000,:],aspect='auto',interpolation='none') #plt.imshow(raw.data_ft[0,:,:],aspect='auto',interpolation='none') im_ani = animation.ArtistAnimation(fig2, ims, interval=150, repeat_delay=3000, blit=True) im_ani.save(moviefile_sc, dpi=150, bitrate=3000, metadata={'artist':'Pierre Kervella'}) def gravi_movie_preproc(filename): plt.close('all') preproc = gravi_visual_class.Preproc(filename+'.fits') target_name = preproc.header['HIERARCH ESO FT ROBJ NAME'] obs_date = preproc.header['DATE-OBS'] kmag = str(preproc.header['HIERARCH ESO FT ROBJ MAG']) title_ft = target_name+" - K="+kmag+" (GRAVITY FT)"#, "+obs_date+")" title_sc = target_name+" - K="+kmag+" (GRAVITY Science Combiner, "+obs_date+")" moviefile_ft = filename+'-FT.mp4' moviefile_sc = filename+'-SC.mp4' baseline = 5 # To avoid the back line at the end, the last baseline is treated separately #============================================================================== # Fringe tracker #============================================================================== image_rate = 200 # milliseconds per frame step_ft = int(image_rate/(1000*preproc.header['HIERARCH ESO DET3 SEQ1 DIT'])) nimages_ft = int(preproc.nframe_ft/step_ft) if False: if preproc.polarsplit == True: max_ft_spectra = np.zeros((6,nimages_ft)) min_ft_spectra = np.zeros((preproc.spectrum_ft.shape[0],preproc.nwave_ft)) # computation of background per pixel for output in range(0,preproc.spectrum_ft.shape[0]): for wave in range(0,preproc.nwave_ft): min_ft_spectra[output,wave] = np.nanpercentile(preproc.spectrum_ft[output,:,wave],1) for baseline in range(0,6): # max level per baseline for image in range(0,nimages_ft): max_ft_spectra[baseline,image] = np.nanpercentile(preproc.spectrum_ft[baseline*8:(1+baseline)*8,image*step_ft,:],98) for output in range(0,preproc.spectrum_sc.shape[0]): # subtraction of dark and normalization by amplitude per baseline for image in range(0,nimages_ft): preproc.spectrum_ft[output,step_ft*image,:] -= min_ft_spectra[output,:] preproc.spectrum_ft[output,step_ft*image,:] /= (max_ft_spectra[output//8,image]-min_ft_spectra[output,:]) for image in range(0,nimages_ft): # second normalization per wavelength channel for wave in range(0,preproc.nwave_ft): preproc.spectrum_ft[:,step_ft*image,wave] /= np.nanpercentile(preproc.spectrum_ft[:,step_ft*image,wave],98,axis=0) sorted_spectrum_ft = np.zeros((48+5,nimages_ft,preproc.nwave_ft)) # Sort to have ABCD outputs in this order for baseline in range(0,5): sorted_spectrum_ft[0+baseline*9,:,:] = preproc.spectrum_ft[0+baseline*8,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[1+baseline*9,:,:] = preproc.spectrum_ft[1+baseline*8,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[2+baseline*9,:,:] = preproc.spectrum_ft[4+baseline*8,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[3+baseline*9,:,:] = preproc.spectrum_ft[5+baseline*8,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[4+baseline*9,:,:] = preproc.spectrum_ft[2+baseline*8,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[5+baseline*9,:,:] = preproc.spectrum_ft[3+baseline*8,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[6+baseline*9,:,:] = preproc.spectrum_ft[6+baseline*8,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[7+baseline*9,:,:] = preproc.spectrum_ft[7+baseline*8,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[8+baseline*9,:,:] = 0.0 # dark line between baselines baseline = 5 # To avoid the back line at the end, the last baseline is treated separately sorted_spectrum_ft[0+baseline*9,:,:] = preproc.spectrum_ft[0+baseline*8,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[1+baseline*9,:,:] = preproc.spectrum_ft[1+baseline*8,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[2+baseline*9,:,:] = preproc.spectrum_ft[4+baseline*8,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[3+baseline*9,:,:] = preproc.spectrum_ft[5+baseline*8,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[4+baseline*9,:,:] = preproc.spectrum_ft[2+baseline*8,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[5+baseline*9,:,:] = preproc.spectrum_ft[3+baseline*8,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[6+baseline*9,:,:] = preproc.spectrum_ft[6+baseline*8,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[7+baseline*9,:,:] = preproc.spectrum_ft[7+baseline*8,0:step_ft*nimages_ft:step_ft,:] fig1=plt.figure(figsize=(5,12)) #fig1.tight_layout() plt.title(title_ft) plt.xlabel("Spectral channel",fontsize=15) plt.ylabel("Fringe Tracker output",fontsize=15) if preproc.polarsplit == False: max_ft_spectra = np.zeros((6,nimages_ft)) min_ft_spectra = np.zeros((preproc.nregion,preproc.nwave_ft)) # computation of background per pixel for output in range(0,preproc.nregion): for wave in range(0,preproc.nwave_ft): min_ft_spectra[output,wave] = np.nanpercentile(preproc.spectrum_ft[output,:,wave],1) for baseline in range(0,6): # max level per baseline for image in range(0,nimages_ft): max_ft_spectra[baseline,image] = np.nanpercentile(preproc.spectrum_ft[baseline*4:(1+baseline)*4,image*step_ft,:],98) for output in range(0,preproc.nregion): # subtraction of dark and normalization by amplitude per baseline for image in range(0,nimages_ft): preproc.spectrum_ft[output,step_ft*image,:] -= min_ft_spectra[output,:] preproc.spectrum_ft[output,step_ft*image,:] /= (max_ft_spectra[output//4,image]-min_ft_spectra[output,:]) for image in range(0,nimages_ft): # second normalization per wavelength channel for wave in range(0,preproc.nwave_ft): preproc.spectrum_ft[:,step_ft*image,wave] /= np.nanpercentile(preproc.spectrum_ft[:,step_ft*image,wave],98,axis=0) baseline = 5 # To avoid the back line at the end, the last baseline is treated separately # Sort to have ABCD outputs in this order sorted_spectrum_ft = np.zeros((24+5,nimages_ft,preproc.nwave_ft)) for baseline in range(0,5): sorted_spectrum_ft[0+baseline*5,:,:] = preproc.spectrum_ft[0+baseline*4,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[1+baseline*5,:,:] = preproc.spectrum_ft[2+baseline*4,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[2+baseline*5,:,:] = preproc.spectrum_ft[1+baseline*4,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[3+baseline*5,:,:] = preproc.spectrum_ft[3+baseline*4,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[4+baseline*5,:,:] = 0.0 # dark line between baselines baseline = 5 # To avoid the back line at the end, the last baseline is treated separately sorted_spectrum_ft[0+baseline*5,:,:] = preproc.spectrum_ft[0+baseline*4,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[1+baseline*5,:,:] = preproc.spectrum_ft[2+baseline*4,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[2+baseline*5,:,:] = preproc.spectrum_ft[1+baseline*4,0:step_ft*nimages_ft:step_ft,:] sorted_spectrum_ft[3+baseline*5,:,:] = preproc.spectrum_ft[3+baseline*4,0:step_ft*nimages_ft:step_ft,:] fig1=plt.figure(figsize=(4,12)) #fig1.tight_layout() plt.title(title_ft) plt.xlabel("Spectral channel",fontsize=15) plt.ylabel("Fringe Tracker output",fontsize=15) ims=[] maxl = np.percentile(sorted_spectrum_ft,99) minl = np.percentile(sorted_spectrum_ft,1) for add in range(0,nimages_ft): ims.append((plt.imshow(sorted_spectrum_ft[:,add,:],aspect='auto',interpolation='none',cmap='gray',vmin=minl,vmax=maxl),)) # Add time in seconds im_ani = animation.ArtistAnimation(fig1, ims, interval=image_rate, repeat_delay=3000, blit=True) im_ani.save(moviefile_ft, dpi=150, bitrate=3000, metadata={'artist':'Pierre Kervella'}) #============================================================================== # Science combiner #============================================================================== if preproc.polarsplit == True: min_sc_spectra = np.zeros((6,preproc.nframe_sc)) max_sc_spectra = np.zeros((6,preproc.nframe_sc)) for baseline in range(0,6): # Computation of the background and maximum per baseline for image in range(0,preproc.nframe_sc): min_sc_spectra[baseline,image] = np.nanpercentile(preproc.spectrum_sc[baseline*8:(1+baseline)*8,image,:],1) max_sc_spectra[baseline,image] = np.nanpercentile(preproc.spectrum_sc[baseline*8:(1+baseline)*8,image,:],99) for output in range(0,preproc.nregion): for image in range(0,preproc.nframe_sc): preproc.spectrum_sc[output,image,:] -= min_sc_spectra[output//8,image] preproc.spectrum_sc[output,image,:] /= (max_sc_spectra[output//8,image]-min_sc_spectra[output//8,image]) for image in range(0,preproc.nframe_sc): for wave in range(0,preproc.nwave_sc): preproc.spectrum_sc[:,image,wave] /= np.nanmedian(preproc.spectrum_sc[:,image,wave],axis=0) # Sort to have ABCD outputs in this order sorted_spectrum_sc = np.zeros((48+5,preproc.nframe_sc,preproc.nwave_sc)) for baseline in range(0,5): sorted_spectrum_sc[0+baseline*9,:,:] = preproc.spectrum_sc[0+baseline*8,:,:] sorted_spectrum_sc[1+baseline*9,:,:] = preproc.spectrum_sc[1+baseline*8,:,:] sorted_spectrum_sc[2+baseline*9,:,:] = preproc.spectrum_sc[4+baseline*8,:,:] sorted_spectrum_sc[3+baseline*9,:,:] = preproc.spectrum_sc[5+baseline*8,:,:] sorted_spectrum_sc[4+baseline*9,:,:] = preproc.spectrum_sc[2+baseline*8,:,:] sorted_spectrum_sc[5+baseline*9,:,:] = preproc.spectrum_sc[3+baseline*8,:,:] sorted_spectrum_sc[6+baseline*9,:,:] = preproc.spectrum_sc[6+baseline*8,:,:] sorted_spectrum_sc[7+baseline*9,:,:] = preproc.spectrum_sc[7+baseline*8,:,:] sorted_spectrum_sc[8+baseline*9,:,:] = 0.0 # dark line between baselines baseline = 5 # To avoid the back line at the end, the last baseline is treated separately sorted_spectrum_sc[0+baseline*9,:,:] = preproc.spectrum_sc[0+baseline*8,:,:] sorted_spectrum_sc[1+baseline*9,:,:] = preproc.spectrum_sc[1+baseline*8,:,:] sorted_spectrum_sc[2+baseline*9,:,:] = preproc.spectrum_sc[4+baseline*8,:,:] sorted_spectrum_sc[3+baseline*9,:,:] = preproc.spectrum_sc[5+baseline*8,:,:] sorted_spectrum_sc[4+baseline*9,:,:] = preproc.spectrum_sc[2+baseline*8,:,:] sorted_spectrum_sc[5+baseline*9,:,:] = preproc.spectrum_sc[3+baseline*8,:,:] sorted_spectrum_sc[6+baseline*9,:,:] = preproc.spectrum_sc[6+baseline*8,:,:] sorted_spectrum_sc[7+baseline*9,:,:] = preproc.spectrum_sc[7+baseline*8,:,:] fig2=plt.figure(figsize=(15,10)) plt.title(title_sc) plt.xlabel("Spectral channel",fontsize=15) # plt.ylabel("Science Combiner (6 baselines, 48 ouptuts)",fontsize=15) plt.ylabel("Science Combiner (6 baselines, 48 ouptuts)",fontsize=15) if preproc.polarsplit == False: min_sc_spectra = np.zeros((6,preproc.nframe_sc)) max_sc_spectra = np.zeros((6,preproc.nframe_sc)) for baseline in range(0,6): # Computation of the background and maximum per baseline for image in range(0,preproc.nframe_sc): min_sc_spectra[baseline,image] = np.nanpercentile(preproc.spectrum_sc[baseline*4:(1+baseline)*4,image,:],1) max_sc_spectra[baseline,image] = np.nanpercentile(preproc.spectrum_sc[baseline*4:(1+baseline)*4,image,:],99) for output in range(0,preproc.nregion): for image in range(0,preproc.nframe_sc): preproc.spectrum_sc[output,image,:] -= min_sc_spectra[output//4,image] preproc.spectrum_sc[output,image,:] /= (max_sc_spectra[output//4,image]-min_sc_spectra[output//4,image]) for image in range(0,preproc.nframe_sc): for wave in range(0,preproc.nwave_sc): preproc.spectrum_sc[:,image,wave] /= np.nanmedian(preproc.spectrum_sc[:,image,wave],axis=0) # Sort to have ABCD outputs in this order sorted_spectrum_sc = np.zeros((24+5,preproc.nframe_sc,preproc.nwave_sc)) for baseline in range(0,5): sorted_spectrum_sc[0+baseline*5,:,:] = preproc.spectrum_sc[0+baseline*4,:,:] sorted_spectrum_sc[1+baseline*5,:,:] = preproc.spectrum_sc[2+baseline*4,:,:] sorted_spectrum_sc[2+baseline*5,:,:] = preproc.spectrum_sc[1+baseline*4,:,:] sorted_spectrum_sc[3+baseline*5,:,:] = preproc.spectrum_sc[3+baseline*4,:,:] sorted_spectrum_sc[4+baseline*5,:,:] = 0.0 # dark line between baselines baseline = 5 # To avoid the back line at the end, the last baseline is treated separately sorted_spectrum_sc[0+baseline*5,:,:] = preproc.spectrum_sc[0+baseline*4,:,:] sorted_spectrum_sc[1+baseline*5,:,:] = preproc.spectrum_sc[2+baseline*4,:,:] sorted_spectrum_sc[2+baseline*5,:,:] = preproc.spectrum_sc[1+baseline*4,:,:] sorted_spectrum_sc[3+baseline*5,:,:] = preproc.spectrum_sc[3+baseline*4,:,:] fig2=plt.figure(figsize=(20,10)) plt.title(title_sc) plt.xlabel("Spectral channel",fontsize=15) # plt.ylabel("Science Combiner (6 baselines, 48 ouptuts)",fontsize=15) plt.ylabel("Science Combiner (6 baselines, 24 ouptuts)",fontsize=15) minl = 0 maxl = np.percentile(sorted_spectrum_sc,98) # output, time, wavelength ims=[] for add in range(0,preproc.nframe_sc): # gray ims.append((plt.imshow(sorted_spectrum_sc[:,add,:],aspect='auto',interpolation='none',cmap='cubehelix',vmin=minl,vmax=maxl),)) im_ani = animation.ArtistAnimation(fig2, ims, interval=500, repeat_delay=3000, blit=True) im_ani.save(moviefile_sc, dpi=150, bitrate=3000, metadata={'artist':'Pierre Kervella'}) plt.close('all') #============================================================================== # MAIN PROGRAM #============================================================================== # Add options usage = """ usage: %prog """ parser = OptionParser(usage) parser.add_option("-o","--overwrite", action="store_true", dest="overwrite_flag",default=False, help="Overwrite existing PDFs") (argoptions, args) = parser.parse_args() filelist=[] ## If the user specifies a file name or wild cards ("*_0001.fits") if len(sys.argv) > 1 : longnames = [f for files in sys.argv[1:] for f in glob(files)] filelist = [os.path.splitext(f)[0] for f in longnames] ## Processing of the full current directory else : for file in os.listdir("."): if file.endswith(".fits"): filelist.append(os.path.splitext(file)[0]) filelist.sort() # process the files in alphabetical order for filename in filelist: fullname = os.getcwd()+'/'+filename gravi_movie_preproc(fullname) #if __name__ == '__main__': # # #gravi_movie_preproc('/Users/kervella/Pipelines/python_tools/gravi_preproc_GRAVITY.2015-11-05T00-16-53') # #gravi_movie_preproc('/Users/kervella/Pipelines/python_tools/gravi_preproc_GRAVITY.2015-11-05T03-40-02') # #gravi_movie_preproc('/Users/kervella/Pipelines/python_tools/gravi_preproc_GRAVITY.2015-11-05T04-06-21') # #gravi_movie_preproc('/Volumes/GRAVITY-1TB/2016-03-17-GRAVITY-workshop/1_AlfCru_swap/reduced/GRAVI.2016-01-21T07:01:57.790_preproc') # gravi_movie_preproc('/Volumes/GRAVITY-1TB/2016-03-17-GRAVITY-workshop/2_EtaCar_High-Split/reduced/GRAVITY.2016-02-25T02-46-42_preproc')