# -*- coding: utf-8 -*- """ Created on Sat Jan 30 17:31:19 2016 @author: kervella """ 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 os import sys sys.path.insert(0, '~/Pipelines/python_tools/gravi_visual') from . import gravi_visual_class from . import gravi_visual_p2vm import datetime import numpy as np def generate_p2vm_filelist(directory): filelist = [] for file in os.listdir(directory): if file.endswith("_p2vm.fits"): filelist.append(directory+'/'+os.path.splitext(file)[0]) return filelist if __name__ == '__main__': #============================================================================== # User parameters #============================================================================== directory = '.' plot_sc = False specres = 'MEDIUM' polsetup = 'COMBINED' #============================================================================== filelist = generate_p2vm_filelist(directory) plt.close('all') # Key words in the header to be checked for file selection if plot_sc == True: combiner='SC' fits_keys = {'HIERARCH ESO PRO CATG': ['P2VM'],\ 'HIERARCH ESO FT POLA MODE': [polsetup],\ 'HIERARCH ESO INS SPEC RES': [specres]} setup = combiner + '-' + specres + '-' + polsetup else: combiner='FT' fits_keys = {'HIERARCH ESO PRO CATG': ['P2VM'],\ 'HIERARCH ESO FT POLA MODE': [polsetup]} setup = combiner + '-ALL-' + polsetup p2vmlist = gravi_visual_class.P2vmlist(filelist[:],fits_keys) nfiles = p2vmlist.nfiles now = datetime.datetime.now() nbase = 6 ntel = 4 nfiles = p2vmlist.nfiles nregion = p2vmlist.p2vm[0].nregion_sc nwave_sc = p2vmlist.p2vm[0].nwave_sc nwave_ft = p2vmlist.p2vm[0].nwave_ft base_list=["12", "13", "14", "23", "24", "34"] # The time scale of the series is set to start at zero timescale = (np.array(p2vmlist.timescale)-p2vmlist.timescale[0])*24. # in decimal hours p2vmstats = [] coh_sc_avg = np.zeros((nfiles,nbase)) coh_ft_avg = np.zeros((nfiles,nbase)) coh_sc_avg_s = np.zeros((nfiles,nbase)) coh_ft_avg_s = np.zeros((nfiles,nbase)) coh_sc_avg_p = np.zeros((nfiles,nbase)) coh_ft_avg_p = np.zeros((nfiles,nbase)) trans_avg_ft = np.zeros((nfiles,nregion)) trans_avg_sc = np.zeros((nfiles,nregion)) for i in range(0,nfiles): p2vmstats.append(gravi_visual_p2vm.combiner_stats(p2vmlist.p2vm[i],base_list)) coh_sc_avg[i,:] = np.asarray(p2vmstats[i]["coh_sc_avg"]) coh_ft_avg[i,:] = np.asarray(p2vmstats[i]["coh_ft_avg"]) trans_avg_ft[i,:] = np.mean(p2vmlist.p2vm[i].transmission_ft,axis=(1,2)) trans_avg_sc[i,:] = np.mean(p2vmlist.p2vm[i].transmission_sc,axis=(1,2)) if 'SPLIT' in setup: coh_sc_avg_s[i,:] = np.asarray(p2vmstats[i]["coh_sc_avg_s"]) coh_ft_avg_s[i,:] = np.asarray(p2vmstats[i]["coh_ft_avg_s"]) coh_sc_avg_p[i,:] = np.asarray(p2vmstats[i]["coh_sc_avg_p"]) coh_ft_avg_p[i,:] = np.asarray(p2vmstats[i]["coh_ft_avg_p"]) # Standard deviation of coherence and photometry as a function of time per output trans_ft_mean = np.mean(trans_avg_ft,axis=0) trans_sc_mean = np.mean(trans_avg_sc,axis=0) trans_ft_rms = np.std(trans_avg_ft,axis=0) trans_sc_rms = np.std(trans_avg_sc,axis=0) coh_ft_mean = np.mean(coh_ft_avg,axis=0) coh_sc_mean = np.mean(coh_sc_avg,axis=0) coh_ft_rms = np.std(coh_ft_avg,axis=0) coh_sc_rms = np.std(coh_sc_avg,axis=0) if 'SPLIT' in setup: coh_ft_mean_s = np.mean(coh_ft_avg_s,axis=0) coh_sc_mean_s = np.mean(coh_sc_avg_s,axis=0) coh_ft_mean_p = np.mean(coh_ft_avg_p,axis=0) coh_sc_mean_p = np.mean(coh_sc_avg_p,axis=0) coh_ft_rms_s = np.std(coh_ft_avg_s,axis=0) coh_sc_rms_s = np.std(coh_sc_avg_s,axis=0) coh_ft_rms_p = np.std(coh_ft_avg_p,axis=0) coh_sc_rms_p = np.std(coh_sc_avg_p,axis=0) #============================================================================== # Production of the figures #============================================================================== import matplotlib.backends.backend_pdf pdf = matplotlib.backends.backend_pdf.PdfPages('P2VM-trend-'+setup+'.pdf') if plot_sc == False: if 'COMBINED' in setup: plt.figure(figsize=(10,10)) for base in range(0,nbase): labelrms = 'B'+base_list[base]+' %(avgval).1f%% +/- %(rmsval).1f%%' % {"avgval":coh_ft_mean[base]*100.,"rmsval":coh_ft_rms[base]*100.} plt.plot(timescale,coh_ft_avg[:,base], label=labelrms, ls='-', marker='+') plt.title("FT avg coherence transmission per baseline "+setup) plt.xlabel("Time (hours)") plt.ylabel("Coherence transmission") plt.grid() plt.margins(0.2) axes = plt.gca() axes.set_xlim([-50,400]) axes.set_ylim([0.8,1.05]) plt.legend(bbox_to_anchor=(0., 0., 1., .102), loc=3, ncol=3, mode="expand", borderaxespad=1,prop={'size':12}) pdf.savefig() if 'SPLIT' in setup: plt.figure(figsize=(10,10)) for base in range(0,nbase): labelrms = 'B'+base_list[base]+'-S %(avgval).1f%% +/- %(rmsval).1f%%' % {"avgval":coh_ft_mean_s[base]*100.,"rmsval":coh_ft_rms_s[base]*100.} plt.plot(timescale,coh_ft_avg_s[:,base],label=labelrms, ls='-', marker='+') labelrms = 'B'+base_list[base]+'-P %(avgval).1f%% +/- %(rmsval).1f%%' % {"avgval":coh_ft_mean_p[base]*100.,"rmsval":coh_ft_rms_p[base]*100.} plt.plot(timescale,coh_ft_avg_p[:,base],label=labelrms, ls='-', marker='x') plt.title("FT avg coherence transmission per baseline (S and P) "+setup) plt.xlabel("Time (hours)") plt.ylabel("Coherence transmission") plt.grid() axes = plt.gca() axes.set_xlim([-50,400]) axes.set_ylim([0.8,1.05]) plt.legend(bbox_to_anchor=(0., 0., 1., .102), loc=3, ncol=3, mode="expand", borderaxespad=1,prop={'size':12}) pdf.savefig() plt.figure(figsize=(10,10)) for region in range(0,nregion): labelrms = p2vmlist.p2vm[0].regname_ft[region]+' %(avgval).1f%% +/- %(rmsval).1f%%' % {"avgval":trans_ft_mean[region]*100.,"rmsval":trans_ft_rms[region]*100.} plt.plot(timescale,trans_avg_ft[:,region], label=labelrms, ls='-', marker='+') plt.title("FT relative photometric transmission per IO output "+setup) plt.xlabel("Time (hours)") plt.ylabel("Photometric transmission") plt.grid() plt.margins(0.2) axes = plt.gca() axes.set_xlim([-50,400]) axes.set_ylim([0.6,1.3]) plt.legend(bbox_to_anchor=(0., 0., 1., .102), loc=3, ncol=4, mode="expand", borderaxespad=1,prop={'size':8}) pdf.savefig() if plot_sc == True: if 'COMBINED' in setup: plt.figure(figsize=(10,10)) for base in range(0,nbase): labelrms = 'B'+base_list[base]+' %(avgval).1f%% +/- %(rmsval).1f%%' % {"avgval":coh_sc_mean[base]*100.,"rmsval":coh_sc_rms[base]*100.} plt.plot(timescale,coh_sc_avg[:,base], label=labelrms, ls='-', marker='+') plt.title("SC avg coherence transmission per baseline "+setup) plt.xlabel("Time (hours)") plt.ylabel("Coherence transmission") plt.grid() plt.margins(0.2) axes = plt.gca() axes.set_xlim([-50,400]) axes.set_ylim([0.8,1.05]) plt.legend(bbox_to_anchor=(0., 0., 1., .102), loc=3, ncol=3, mode="expand", borderaxespad=1,prop={'size':12}) pdf.savefig() if 'SPLIT' in setup: plt.figure(figsize=(10,10)) for base in range(0,nbase): labelrms = 'B'+base_list[base]+'-S %(avgval).1f%% +/- %(rmsval).1f%%' % {"avgval":coh_sc_mean_s[base]*100.,"rmsval":coh_sc_rms_s[base]*100.} plt.plot(timescale,coh_sc_avg_s[:,base],label=labelrms, ls='-', marker='+') labelrms = 'B'+base_list[base]+'-P %(avgval).1f%% +/- %(rmsval).1f%%' % {"avgval":coh_sc_mean_p[base]*100.,"rmsval":coh_sc_rms_p[base]*100.} plt.plot(timescale,coh_sc_avg_p[:,base],label=labelrms, ls='-', marker='x') plt.title("SC avg coherence transmission per baseline (S and P) "+setup) plt.xlabel("Time (hours)") plt.ylabel("Coherence transmission") plt.grid() axes = plt.gca() axes.set_xlim([-50,400]) axes.set_ylim([0.8,1.05]) plt.legend(bbox_to_anchor=(0., 0., 1., .102), loc=3, ncol=3, mode="expand", borderaxespad=1,prop={'size':12}) pdf.savefig() plt.figure(figsize=(10,10)) for region in range(0,nregion): labelrms = p2vmlist.p2vm[0].regname_sc[region]+' %(avgval).1f%% +/- %(rmsval).1f%%' % {"avgval":trans_sc_mean[region]*100.,"rmsval":trans_sc_rms[region]*100.} plt.plot(timescale,trans_avg_sc[:,region], label=labelrms, ls='-', marker='+') plt.title("SC relative photometric transmission per IO output "+setup) plt.xlabel("Time (hours)") plt.ylabel("Photometric transmission") plt.grid() plt.margins(0.2) axes = plt.gca() axes.set_xlim([-50,400]) axes.set_ylim([0.6,1.3]) plt.legend(bbox_to_anchor=(0., 0., 1., .102), loc=3, ncol=4, mode="expand", borderaxespad=1,prop={'size':8}) pdf.savefig() pdf.close()