#! /usr/bin/env python # -*- coding: iso-8859-15 -*- """ Created on Mon May 25 13:51:41 2015 @author: kervella """ # ATTENTION: 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 datetime import numpy as np try: from scipy.signal import welch welchpresent = True except ImportError as e: welchpresent = False pass # module doesn't exist, deal with it. #============================================================================== # Preparation of the report using reportlab #============================================================================== def produce_preproc_report(preproc,filename): # From gravi_visual_class from . import gravi_visual_class from .gravi_visual_class import PdfImage, myFirstPage, myLaterPages, styles from .gravi_visual_class import clipdata, transbarchartout, graphoutaxes, graphoutnoaxis from .gravi_visual_class import graphoutnoxaxes, graphscatteraxes, graphaxesplt from .gravi_visual_class import plotTitle, plotSubtitle from .gravi_visual_class import get_key_withdefault, create_array_from_list, baseline_phases from .gravi_visual_class import mean_angle, std_angle, clean_unwrap_phase, nanaverage from .gravi_visual_class import clean_gdelay_is, clean_gdelay_fft, clean_gdelay_full from .gravi_visual_class import base_list, tel_list, nbase, ntel from .gravi_visual_class import plotReductionSummary, plotSummary # From reportlab from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, Image, PageBreak from reportlab.graphics.shapes import String from reportlab.lib.units import inch, cm, mm from reportlab.lib import colors # import PIL import io from io import StringIO #============================================================================== # Global parameters #============================================================================== pixwidth = 1500 # Number of elements for plots in horizontal direction #============================================================================== # Start Story #============================================================================== Story = [Spacer(1,5*mm)] plotSummary (Story, filename, preproc, onTarget=False) #============================================================================== # Photometry per output #============================================================================== Story.append(Spacer(1,1*cm)) if preproc.spectrum_sc is not None: plotTitle (Story,"SC average photometric flux per output (ADUs)") hsize = 18*cm vsize = 5*cm # Grand average transmission # trans_avg_sc = np.mean(preproc.spectrum_sc) # Average transmission per region normalized to grand median trans_region_sc = np.zeros([preproc.nregion],dtype='d') for region in range(0,preproc.nregion): trans_region_sc[region]=np.mean(preproc.spectrum_sc[region,:,:]) # trans_region_sc[region]=np.mean(preproc.spectrum_sc[region,:,:])/trans_avg_sc # plot the two polarizations separately (alternatively S and P) yminval=np.min(trans_region_sc) ymaxval=np.max(trans_region_sc)+0.1*np.abs(np.max(trans_region_sc)) ystep=(ymaxval-yminval)/10. if preproc.polarsplit == True: transdata = [tuple(clipdata(trans_region_sc[::2],yminval,ymaxval)),tuple(clipdata(trans_region_sc[1::2],yminval,ymaxval))] labelbase = preproc.regname_sc[::2].astype('|S4').tolist() bc = transbarchartout(transdata,labelbase,yminval,ymaxval,ystep,hsize,vsize,colors.aquamarine,colors.cornflower) else: transdata = [tuple(clipdata(trans_region_sc,yminval,ymaxval))] labelbase = preproc.regname_sc.astype('|S4').tolist() bc = transbarchartout(transdata,labelbase,yminval,ymaxval,ystep,hsize,vsize,colors.aquamarine,colors.cornflower) Story.append(bc) Story.append(Spacer(1,5*mm)) if preproc.spectrum_ft is not None: plotTitle (Story,"FT average photometric flux per output (ADUs)") hsize = 18*cm vsize = 5*cm # Grand average transmission # trans_avg_ft = np.mean(preproc.spectrum_ft) # Average transmission per region normalized to grand median trans_region_ft = np.zeros([preproc.nregion],dtype='d') for region in range(0,preproc.nregion): trans_region_ft[region]=np.mean(preproc.spectrum_ft[region,:,:]) # trans_region_ft[region]=np.mean(preproc.spectrum_ft[region,:,:])/trans_avg_ft # plot the two polarizations separately (alternatively S and P) yminval=np.min(trans_region_ft) ymaxval=np.max(trans_region_ft)+0.1*np.abs(np.max(trans_region_ft)) ystep=(ymaxval-yminval)/10. if preproc.polarsplit == True: transdata = [tuple(clipdata(trans_region_ft[::2],yminval,ymaxval)),tuple(clipdata(trans_region_ft[1::2],yminval,ymaxval))] labelbase = preproc.regname_ft[::2].astype('|S4').tolist() bc = transbarchartout(transdata,labelbase,yminval,ymaxval,ystep,hsize,vsize,colors.aquamarine,colors.cornflower) else: transdata = [tuple(clipdata(trans_region_ft,yminval,ymaxval))] labelbase = preproc.regname_ft.astype('|S4').tolist() bc = transbarchartout(transdata,labelbase,yminval,ymaxval,ystep,hsize,vsize,colors.aquamarine,colors.cornflower) Story.append(bc) Story.append(PageBreak()) #============================================================================== # Summary of reduction parameters #============================================================================== plotReductionSummary(Story, preproc) Story.append(PageBreak()) #============================================================================== # SC spectrum zoomed into spectral regions #============================================================================== plotTitle (Story,"SC spectrums (%i regions) zoomed into spectral features"%preproc.nregion) mean_spectrum_sc = np.mean (preproc.spectrum_sc, axis=1).T mean_spectrum_sc /= np.mean (mean_spectrum_sc, axis=0)[None,:] # List of plots wave_limits = [[1.99,2.45],[1.99,2.03],[2.15,2.19],[2.28,2.32]] nplot = len (wave_limits) imgdata = io.StringIO() plt.close('all') plt.rc('xtick', labelsize='x-small') plt.rc('ytick', labelsize='x-small') fig, axarr = plt.subplots (nplot, 1, sharex=False, sharey=False, figsize=(5,8)) for ax,lim in zip (axarr, wave_limits): ax.plot (preproc.wave_sc, mean_spectrum_sc) ax.set_xlim([lim[0], lim[1]]) ax.set_ylim(ymin=0) plt.savefig(imgdata, format='PDF', dpi=250, bbox_inches='tight') Story.append(PdfImage(imgdata, width=20*cm, height=22*cm, kind='bound')) Story.append(PageBreak()) #============================================================================== # Image (waterfall) display of SC and FT spectral signals #============================================================================== if preproc.spectrum_ft is not []: nframes = 5000 # Number of frames to display for i in range(0,preproc.nregion,8): plotTitle (Story,"FT baseline "+preproc.regname_ft[i][0:2]+" flux (ADU, "+str(nframes)+" frames)") plotSubtitle (Story,"Wavelength channel number in horizontal axis, number of frame in sequence in vertical axis.") imgdata = io.StringIO() plt.close('all') plt.rc('xtick', labelsize='x-small') plt.rc('ytick', labelsize='x-small') fig, axarr = plt.subplots(nrows=4, ncols=2, sharex=True, sharey=True, figsize=(5,8)) valmin = np.percentile(preproc.spectrum_ft[i:i+8,0:nframes,:],1) valmax = np.percentile(preproc.spectrum_ft[i:i+8,0:nframes,:],99) for j in range(0,4): im = axarr[j,0].imshow(preproc.spectrum_ft[2*j+i,0:nframes,:],vmin=valmin,vmax=valmax,cmap='cubehelix', interpolation='nearest',aspect='auto') axarr[j,0].set_title('FT Output '+preproc.regname_sc[2*j+i], fontsize=7) im = axarr[j,1].imshow(preproc.spectrum_ft[2*j+1+i,0:nframes,:],vmin=valmin,vmax=valmax,cmap='cubehelix', interpolation='nearest', aspect='auto') axarr[j,1].set_title('FT Output '+preproc.regname_sc[2*j+i+1], 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) #fig.tight_layout() #plt.colorbar() widthfig = 20 * cm heightfig= 22 * cm plt.savefig(imgdata, format='PDF', dpi=250, bbox_inches='tight') pi1 = PdfImage(imgdata, width=widthfig, height=heightfig, kind='bound') plt.close('all') Story.append(pi1) plt.close('all') Story.append(PageBreak()) if preproc.spectrum_sc is not []: for i in range(0,preproc.nregion,8): plotTitle (Story,"SC baseline "+preproc.regname_sc[i][0:2]+" flux (ADU) waterfall view") plotSubtitle (Story,"Wavelength channel number in horizontal axis, number of frame in sequence in vertical axis.") imgdata = io.StringIO() plt.close('all') plt.rc('xtick', labelsize='x-small') plt.rc('ytick', labelsize='x-small') fig, axarr = plt.subplots(nrows=4, ncols=2, sharex=True, sharey=True, figsize=(5,8)) valmin = np.percentile(preproc.spectrum_sc[i:i+8,0:nframes,:],1) valmax = np.percentile(preproc.spectrum_sc[i:i+8,0:nframes,:],99) for j in range(0,4): im = axarr[j,0].imshow(preproc.spectrum_sc[2*j+i,0:nframes,:],vmin=valmin,vmax=valmax,cmap='cubehelix', interpolation='nearest',aspect='auto') axarr[j,0].set_title('SC Output '+preproc.regname_sc[2*j+i], fontsize=7) im = axarr[j,1].imshow(preproc.spectrum_sc[2*j+1+i,0:nframes,:],vmin=valmin,vmax=valmax,cmap='cubehelix', interpolation='nearest', aspect='auto') axarr[j,1].set_title('SC Output '+preproc.regname_sc[2*j+i+1], 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) #fig.tight_layout() #plt.colorbar() widthfig = 20 * cm heightfig= 22 * cm plt.savefig(imgdata, format='PDF', dpi=250, bbox_inches='tight') pi1 = PdfImage(imgdata, width=widthfig, height=heightfig, kind='bound') plt.close('all') Story.append(pi1) plt.close('all') Story.append(PageBreak()) #============================================================================== # Create PDF report from Story #============================================================================== print("Create the PDF") gravi_visual_class.TITLE = "GRAVITY PREPROC Quality Control Report" gravi_visual_class.PAGEINFO = "PREPROC file: "+filename+".fits" reportname = filename+"-PREPROC.pdf" doc = SimpleDocTemplate(reportname) doc.build(Story, onFirstPage=myFirstPage, onLaterPages=myLaterPages) print((" "+reportname)) #============================================================================== # MAIN PROGRAM #============================================================================== if __name__ == '__main__': filename='' #filename='preproc_sample' #filename = 'p2vm_preproc_GRAV.2015-04-30T11-24-45' #filename = 'p2vm_reduce_preproc_5' if len(sys.argv) == 2 : filename=sys.argv[1] if filename == '' : filename=input(" Enter PREPROC file name (without .fits) : ") preproc = gravi_visual_class.Preproc(filename+'.fits') produce_preproc_report(preproc,filename)