else: # ======================================== #reset time as previous time, reset output paths as previous path name #reset cross-correlation dictionaries # ======================================== print PART_PICKLE = pickle_list[int(res)-1] OUTFILESPATH = PART_PICKLE[:-12] out_basename = os.path.basename(OUTFILESPATH) print "Opening {} partial file for restart ... ".format(out_basename) # re-initialising .part.pickle collection of cross-correlations xc = pscrosscorr.load_pickled_xcorr(PART_PICKLE) for key in xc.keys(): for key2 in xc[key].keys(): #help(xc[key][key2]) #print xc[key][key2].endday a=5 #most recent day last endday of list #read in metadata to find latest time slot. Then assign this to FIRSTDAY METADATA_PATH = '{}metadata.pickle'.format(OUTFILESPATH.\ replace(os.path.basename(OUTFILESPATH), "")) metadata = pscrosscorr.load_pickled_xcorr(METADATA_PATH)
raise Exception("You must choose one a number betwen {} and {}"\ .format(1, len(pickle_list))) else: PICKLE_PATH = pickle_list[int(res) - 1] OUTFILESPATH = PICKLE_PATH[:-7] out_basename = os.path.basename(OUTFILESPATH) OUTPATH = os.path.dirname(OUTFILESPATH) OUTFOLDERS = os.path.join(OUTPATH, 'XCORR_PLOTS') print "\nOpening {} file to process ... ".format(OUTFOLDERS) print out_basename print "\nOpening {} file to process ... ".format(out_basename) # re-initialising .part.pickle collection of cross-correlations xc = pscrosscorr.load_pickled_xcorr(PICKLE_PATH) # optimizing time-scale: max time = max distance / vmin (vmin = 2.5 km/s) maxdist = max([xc[s1][s2].dist() for s1, s2 in xc.pairs()]) maxt = max(CROSSCORR_TMAX, maxdist / 2.5) if plot_distance: #for central_freq in central_frequencies: #plot distance plot of cross-correlations #plot distance plot of cross-correlations xc.plot(plot_type='distance', xlim=(-maxt, maxt), outfile=out_basename + '_linear'\ + '.png', showplot=False, stack_type='linear', fill=True) xc.plot(plot_type='distance', xlim=(-maxt, maxt),
replace(os.path.basename(OUTFILESPATH), "")) else: # ======================================== #reset time as previous time, reset output paths as previous path name #reset cross-correlation dictionaries # ======================================== print PART_PICKLE = pickle_list[int(res) - 1] OUTFILESPATH = PART_PICKLE[:-12] out_basename = os.path.basename(OUTFILESPATH) print "Opening {} partial file for restart ... ".format(out_basename) # re-initialising .part.pickle collection of cross-correlations xc = pscrosscorr.load_pickled_xcorr(PART_PICKLE) for key in xc.keys(): for key2 in xc[key].keys(): #help(xc[key][key2]) #print xc[key][key2].endday a = 5 #most recent day last endday of list #read in metadata to find latest time slot. Then assign this to FIRSTDAY METADATA_PATH = '{}metadata.pickle'.format(OUTFILESPATH.\ replace(os.path.basename(OUTFILESPATH), "")) metadata = pscrosscorr.load_pickled_xcorr(METADATA_PATH) #print "metadata: ", metadata[-5:] #re-assign FIRSTDAY variable to where the data was cut off
del f #create list of pickle files to process FTAN for if not res or res == "0": pickle_files = [f for f in pickle_list if f[-1] != '~'] else: pickle_files = [pickle_list[int(i)-1] for i in res.split()] #usersuffix = raw_input("\nEnter suffix to append: [none]\n").strip() usersuffix = "" # processing each set of cross-correlations for pickle_file in pickle_files: print "\nOpening pickle file ... " #+ os.path.basename(pickle_file) file_opent0 = dt.datetime.now() xc = pscrosscorr.load_pickled_xcorr(pickle_file) delta = (dt.datetime.now() - file_opent0).total_seconds() print "\nThe file took {:.1f} seconds to open.".format(delta) # copying the suffix of cross-correlations file # (everything between 'xcorr_' and the extension) suffix = os.path.splitext(os.path.basename(pickle_file))[0].replace('xcorr_', '') if usersuffix: suffix = '_'.join([suffix, usersuffix]) # Performing the two-step FTAN, exporting the figures to a # pdf file (one page per cross-correlation) and the clean # dispersion curves to a binary file using module pickle. # # The file are saved in dir *FTAN_DIR* (defined in configuration file) as: # <prefix>_<suffix>.pdf and <prefix>_<suffix>.pickle
raise Exception("You must choose one a number betwen {} and {}"\ .format(1, len(pickle_list))) else: PICKLE_PATH = pickle_list[int(res)-1] OUTFILESPATH = PICKLE_PATH[:-7] out_basename = os.path.basename(OUTFILESPATH) OUTPATH = os.path.dirname(OUTFILESPATH) OUT_SNR = os.path.join(OUTPATH, 'SNR_PLOTS') print "\nOpening {} file to process ... ".format(OUT_SNR) # re-initialising .part.pickle collection of cross-correlations xc = pscrosscorr.load_pickled_xcorr(PICKLE_PATH) # optimizing time-scale: max time = max distance / vmin (vmin = 2.5 km/s) maxdist = max([xc[s1][s2].dist() for s1, s2 in xc.pairs()]) maxt = min(CROSSCORR_TMAX, maxdist / 2.5) #plot distance plot of cross-correlations #xc.plot(plot_type='distance', xlim=(-maxt, maxt), #outfile="/home/boland/Desktop/something1342.png", showplot=False) #plot individual cross-correlations #xc.plot(plot_type='classic', xlim=(-maxt, maxt), # outfile="/home/boland/Desktop/something1342.png", showplot=False) #xc.plot_SNR(plot_type='all', outfile=OUT_SNR,
del f #create list of pickle files to process FTAN for if not res or res == "0": pickle_files = [f for f in pickle_list if f[-1] != '~'] else: pickle_files = [pickle_list[int(i)-1] for i in res.split()] #usersuffix = raw_input("\nEnter suffix to append: [none]\n").strip() usersuffix = "" # processing each set of cross-correlations for pickle_file in pickle_files: print "\nOpening pickle file ... " #+ os.path.basename(pickle_file) file_opent0 = dt.datetime.now() global xc xc = pscrosscorr.load_pickled_xcorr(pickle_file) delta = (dt.datetime.now() - file_opent0).total_seconds() print "\nThe file took {:.1f} seconds to open.".format(delta) del delta # copying the suffix of cross-correlations file # (everything between 'xcorr_' and the extension) suffix = os.path.splitext(os.path.basename(pickle_file))[0].replace('xcorr_', '') if usersuffix: suffix = '_'.join([suffix, usersuffix]) # Performing the two-step FTAN, exporting the figures to a # pdf file (one page per cross-correlation) and the clean # dispersion curves to a binary file using module pickle. # # The file are saved in dir *FTAN_DIR* (defined in configuration file) as: # <prefix>_<suffix>.pdf and <prefix>_<suffix>.pickle