def filterLR_window(infile, outfiles, outfile_root, expt_id):
logging.info(
"Inside filterLR_window. Computing HT of dz within a small window in time and each with a separate fw_id."
)
dz_id = pickle.load(open(infile))
expt_id = int(expt_id)
# get num_frames
num_frames = Spectrum_LR.get_num_frames(expt_id)
print num_frames
for oo in outfiles:
os.unlink(oo)
for n, startF in enumerate(range(1, num_frames, 100)):
fw_id = Spectrum_LR.task_DzHilbertTransform(
dz_id,
cache=cacheValue,
rowS=params[expt_id]['rowS'],
rowE=params[expt_id]['rowE'],
colS=params[expt_id]['colS'],
colE=params[expt_id]['colE'],
startF=startF, # startF is the frame where to start the window from
period=2 # length of the window in time
)
if (fw_id is None):
break
print "fw_id", fw_id
for plotcol in [100, 1000]:
outfile = "%s.%d.%d.fw_id" % (outfile_root, n, plotcol)
pickle.dump(fw_id, open(outfile, 'w'))
def filterLR_window(infile,outfiles, outfile_root,expt_id):
logging.info("Inside filterLR_window. Computing HT of dz within a small window in time and each with a separate fw_id.")
dz_id = pickle.load(open(infile))
expt_id = int(expt_id)
# get num_frames
num_frames = Spectrum_LR.get_num_frames(expt_id)
print num_frames
for oo in outfiles:
os.unlink(oo)
for n, startF in enumerate(range(1, num_frames, 100)):
fw_id = Spectrum_LR.task_DzHilbertTransform(dz_id,
cache=cacheValue,
rowS = params[expt_id]['rowS'],
rowE = params[expt_id]['rowE'],
colS = params[expt_id]['colS'],
colE = params[expt_id]['colE'],
startF=startF, # startF is the frame where to start the window from
period=2 # length of the window in time
)
if (fw_id is None):
break
print "fw_id",fw_id
for plotcol in [100, 1000]:
outfile = "%s.%d.%d.fw_id" % (outfile_root, n, plotcol)
pickle.dump(fw_id, open(outfile, 'w'))
def plotWavesVerticalTimeSeries(infile, outfile):
fw_id = pickle.load(open(infile))
Spectrum_LR.filtered_waves_VTS(
fw_id,
600, # column number
.04, # maxmin
plotName = outfile,
)
def energyFluxPlots(infiles, outfiles):
logging.info('Inside EnergyFluxPlots. Making the energy flux plots for every fw_id created')
logging.info(infiles)
print infiles
raw_EF,left_EF,right_EF,t,x,z = pickle.load(open(infiles[0]))
raw_VAEF,left_VAEF,right_VAEF,t = pickle.load(open(infiles[1]))
#print raw_EF.shape,t.shape,raw_VAEF.shape
Spectrum_LR.plot_energy_flux_VTS(raw_EF,left_EF,right_EF,t,z,0.05,outfiles[0])
plotting_functions.sharexy_overlay3plots(raw_VAEF,left_VAEF,right_VAEF,t,0.7,'Raw','Left','Right','Time','Energy flux',outfiles[1])
def plotWavesVerticalTimeSeries(infile, outfile):
fw_id = pickle.load(open(infile))
Spectrum_LR.filtered_waves_VTS(
fw_id,
600, # column number
.04, # maxmin
plotName=outfile,
)
def plotWavesVTSWindow(infile,outfile):
print infile
fw_id = pickle.load(open(infile))
print "&",outfile
print len(outfile)
###", outfile
print "####",fw_id
Spectrum_LR.filtered_waves_VTS(fw_id,
600, # column number
.04, # maxmin
plotName = outfile,
)
def plotWavesVTSWindow(infile, outfile):
print infile
fw_id = pickle.load(open(infile))
print "&", outfile
print len(outfile)
###", outfile
print "####", fw_id
Spectrum_LR.filtered_waves_VTS(
fw_id,
600, # column number
.04, # maxmin
plotName=outfile,
)
def energyFluxPlots(infiles, outfiles):
logging.info(
'Inside EnergyFluxPlots. Making the energy flux plots for every fw_id created'
)
logging.info(infiles)
print infiles
raw_EF, left_EF, right_EF, t, x, z = pickle.load(open(infiles[0]))
raw_VAEF, left_VAEF, right_VAEF, t = pickle.load(open(infiles[1]))
#print raw_EF.shape,t.shape,raw_VAEF.shape
Spectrum_LR.plot_energy_flux_VTS(raw_EF, left_EF, right_EF, t, z, 0.05,
outfiles[0])
plotting_functions.sharexy_overlay3plots(raw_VAEF, left_VAEF, right_VAEF,
t, 0.7, 'Raw', 'Left', 'Right',
'Time', 'Energy flux',
outfiles[1])
def filterLR(infile, outfile):
dz_id = pickle.load(open(infile))
fw_id = Spectrum_LR.task_DzHilbertTransform(dz_id,
cacheValue,
rowS=320,
rowE=860,
colS=60,
colE=1260)
print "fw_id", fw_id
pickle.dump(fw_id, open(outfile, 'w'))
def computeEnergyFlux(infile, outfiles, plotcol):
plotcol = int(plotcol)
logging.info("Computing the energy flux values for every fw_id and storing them in disk as 2 separate files.")
print infile
fw_id = pickle.load(open(infile))
print "& outfile:",outfiles
print "FW_ID #",fw_id
raw_EF,left_EF,right_EF,t,z,x,raw_VAEF,left_VAEF,right_VAEF= Spectrum_LR.compute_vertically_averaged_energy_flux(fw_id, plotcol=plotcol)
pickle.dump((raw_EF,left_EF,right_EF,t,x,z), open(outfiles[0], 'w'))
pickle.dump((raw_VAEF,left_VAEF,right_VAEF,t), open(outfiles[1], 'w'))
def mergeEnergyFlux(infiles, outfile):
logging.info("Merge all the VAEF files in the workflow to create the plot consisting of all fw_id average energy flux values")
# sort by part number increasing numerically
infiles = sorted(infiles, key=lambda s: int(os.path.basename(s).split('.')[1]) )
logging.info(infiles)
logging.info(outfile)
f = open(outfile, 'w')
f.write('t, raw, left, right\n')
for infile in infiles:
raw_VAEF,left_VAEF,right_VAEF,t = pickle.load(open(infile))
print len(raw_VAEF),len(left_VAEF),len(right_VAEF)
a,b,c,d = Spectrum_LR.compute_mergeEF(raw_VAEF,left_VAEF,right_VAEF,t)
f.write( "%f, %f, %f, %f\n" % (d, a,b,c))
f.close()
def computeEnergyFlux(infile, outfiles, plotcol):
plotcol = int(plotcol)
logging.info(
"Computing the energy flux values for every fw_id and storing them in disk as 2 separate files."
)
print infile
fw_id = pickle.load(open(infile))
print "& outfile:", outfiles
print "FW_ID #", fw_id
raw_EF, left_EF, right_EF, t, z, x, raw_VAEF, left_VAEF, right_VAEF = Spectrum_LR.compute_vertically_averaged_energy_flux(
fw_id, plotcol=plotcol)
pickle.dump((raw_EF, left_EF, right_EF, t, x, z), open(outfiles[0], 'w'))
pickle.dump((raw_VAEF, left_VAEF, right_VAEF, t), open(outfiles[1], 'w'))
def mergeEnergyFlux(infiles, outfile):
logging.info(
"Merge all the VAEF files in the workflow to create the plot consisting of all fw_id average energy flux values"
)
# sort by part number increasing numerically
infiles = sorted(infiles,
key=lambda s: int(os.path.basename(s).split('.')[1]))
logging.info(infiles)
logging.info(outfile)
f = open(outfile, 'w')
f.write('t, raw, left, right\n')
for infile in infiles:
raw_VAEF, left_VAEF, right_VAEF, t = pickle.load(open(infile))
print len(raw_VAEF), len(left_VAEF), len(right_VAEF)
a, b, c, d = Spectrum_LR.compute_mergeEF(raw_VAEF, left_VAEF,
right_VAEF, t)
f.write("%f, %f, %f, %f\n" % (d, a, b, c))
f.close()
def plotFilteredLR(infile, outfile):
fw_id = pickle.load(open(infile))
Spectrum_LR.plot_data(fw_id, plotName = outfile)
def filterLR(infile, outfile):
dz_id = pickle.load(open(infile))
fw_id = Spectrum_LR.task_DzHilbertTransform(dz_id, cacheValue,rowS=320,rowE = 860,colS=60,colE=1260)
print "fw_id",fw_id
pickle.dump(fw_id, open(outfile, 'w'))
def plotFilteredLR(infile, outfile):
fw_id = pickle.load(open(infile))
Spectrum_LR.plot_data(fw_id, plotName=outfile)