"beta = {5}, lambda = {6} ({7} paths)")
title = title.format(period, GRID_STEPS[1], MINPECTSNRS[1],
CORR_LENGTHS[1], ALPHAS[1],
BETAS[1], LAMBDAS[1], len(v.paths))
fig = v.plot(title=title, showplot=False)
# appending fig to figures of period
periodfigs.append(fig)
# exporting figs to pdf
for fig in periodfigs:
pdf.savefig(fig)
plt.close(fig)
# closing pdf file
pagenbs = range(pdf.get_pagecount())
pdf.close()
# merging pages of pdf with similar period
key = lambda pagenb: int(pagenb / 3) # grouping pages 0-1-2, then 3-4-5 etc.
#pagesgroups = psutils.groupbykey(pagenbs, key=key)
#print "\nMerging pages of pdf..."
#psutils.combine_pdf_pages(pdfname, pagesgroups, verbose=True)
# exporting final maps (using pickle) as a dict:
# {period: instance of pstomo.VelocityMap}
print "\nExporting final velocity maps to file: " + picklename
f = psutils.openandbackup(picklename, 'wb')
pickle.dump(vmaps, f, protocol=2)
f.close()
#print "FTAN outputs 1: ", cleanvgcurves
cleanvgcurves = list(cleanvgcurves)
#print "FTAN outputs 2: ", cleanvgcurves
# convert FTAN_outputs into numpy array
cleanvgcurves = np.asarray(cleanvgcurves)
FTAN_delta = (dt.datetime.now() - FTAN_t0).total_seconds()
print "\nIt took {:.1f} seconds to process {} station pairs' FTANS in {}"\
.format(FTAN_delta, len(pairs), process_type)
print "\nFTAN outputs: ", np.asarray(cleanvgcurves)
#print "FTAN outputs type: ", type(cleanvgcurves)
#print "FTAN outputs shape: ", cleanvgcurves.shape
print "\nExporting FTANs ... "
# exporting vg curves to pickle file
f = psutils.openandbackup(outputpath + '.pickle', mode='wb')
pickle.dump(cleanvgcurves, f, protocol=2)
f.close()
print "\nFTAN calculations completed and saved.\n"
# delete xc and cleanvgcurves in an attempt to free up memory
del xc
del cleanvgcurves
title = title.format(period, GRID_STEPS[1], MINPECTSNRS[1],
CORR_LENGTHS[1], ALPHAS[1], BETAS[1],
LAMBDAS[1], len(v.paths))
fig = v.plot(title=title, showplot=False)
# appending fig to figures of period
periodfigs.append(fig)
# exporting figs to pdf
for fig in periodfigs:
pdf.savefig(fig)
plt.close(fig)
# closing pdf file
pagenbs = range(pdf.get_pagecount())
pdf.close()
# merging pages of pdf with similar period
key = lambda pagenb: int(pagenb / 3
) # grouping pages 0-1-2, then 3-4-5 etc.
#pagesgroups = psutils.groupbykey(pagenbs, key=key)
#print "\nMerging pages of pdf..."
#psutils.combine_pdf_pages(pdfname, pagesgroups, verbose=True)
# exporting final maps (using pickle) as a dict:
# {period: instance of pstomo.VelocityMap}
print "\nExporting final velocity maps to file: " + picklename
f = psutils.openandbackup(picklename, 'wb')
pickle.dump(vmaps, f, protocol=2)
f.close()
cleanvgcurves = np.asarray(cleanvgcurves)
# remove None types from cleanvgcurves
cleanvgcurves = cleanvgcurves[cleanvgcurves != np.array(None)]
print cleanvgcurves
FTAN_delta = (dt.datetime.now() - FTAN_t0).total_seconds()
print "\nIt took {:.1f} seconds to process {} station pairs' FTANS in {}"\
.format(FTAN_delta, len(pairs), process_type)
print "\nFTAN outputs: ", np.asarray(cleanvgcurves)
#print "FTAN outputs type: ", type(cleanvgcurves)
#print "FTAN outputs shape: ", cleanvgcurves.shape
print "\nExporting FTANs ... "
# exporting vg curves to pickle file
f = psutils.openandbackup(outputpath + '.pickle', mode='wb')
pickle.dump(cleanvgcurves, f, protocol=2)
f.close()
print "\nFTAN calculations completed and saved.\n"
# delete xc and cleanvgcurves in an attempt to free up memory
del xc
del cleanvgcurves
