def test_resident():
from memory import resident
mem = resident()
assert (isinstance(mem, float))
assert (mem > -1e-6)
mem = resident(children=True)
assert (isinstance(mem, float))
assert (mem > -1e-6)
def __init__(self):
self.tstart = process_time()
self.tlast = self.tstart
self.mstart = memory.resident(children=True)
self.mlast = self.mstart
self.verbosity = self.verbosity_levels.low
self.indent = 0
def get_memory():
result = {}
f = open("/proc/meminfo")
data = f.readlines()
f.close()
KB = 1024.0
memtotal = int(data[0].split()[1]) / KB
memfree = int(data[1].split()[1]) / KB
result['total'] = memtotal
result['free'] = memfree
result['used'] = memtotal - memfree
result['self-resident'] = memory.resident() / memory.MB
result['self-memory'] = memory.memory() / memory.MB
return result
def get_memory():
result = {}
f = open("/proc/meminfo")
data = f.readlines()
f.close()
KB = 1024.0
memtotal = int(data[0].split()[1]) / KB
memfree = int(data[1].split()[1]) / KB
result['total'] = memtotal
result['free'] = memfree
result['used'] = memtotal-memfree
result['self-resident'] = memory.resident() / memory.MB
result['self-memory'] = memory.memory() / memory.MB
return result
def run_project(self, status=False, status_only=False):
self.log('\nProject starting', n=0)
self.init_cascades()
status_only = status_only or self.status_only
status = status or status_only
if status:
self.write_simulation_status()
if status_only:
return
#end if
#end if
self.log('\nstarting runs:\n' + 30 * '~', n=1)
if self.dependent_modes <= self.stages_set:
if self.monitor:
ipoll = 0
while len(self.progressing_cascades) > 0:
self.dlog('\n\n\n' + 70 * '=', n=1)
self.log('poll',
ipoll,
' memory %3.2f MB' % (memory.resident() / 1e6),
n=1)
Pobj.wrote_something = False
self.dlog('cascades',
self.progressing_cascades.keys(),
n=2)
ipoll += 1
self.machine.query_queue()
self.progress_cascades()
self.machine.submit_jobs()
self.update_process_ids()
self.dlog('sleeping', self.sleep, n=2)
time.sleep(self.sleep)
self.dlog('awake', n=2)
if Pobj.wrote_something:
self.log()
#end if
#end while
elif len(self.progressing_cascades) > 0:
self.machine.query_queue()
self.progress_cascades()
self.machine.submit_jobs()
self.update_process_ids()
#end if
else:
self.progress_cascades()
#end if
self.log('Project finished\n')
def run_project(self, status=False, status_only=False):
self.log('\nProject starting', n=0)
self.init_cascades()
status_only = status_only or nexus_core.status_only
status = status or status_only or nexus_core.status != nexus_core.status_modes.none
if status:
self.write_simulation_status()
if status_only:
NexusCore.write_end_splash()
return
#end if
#end if
self.log('\nstarting runs:\n' + 30 * '~', n=1)
if nexus_core.dependent_modes <= nexus_core.stages_set:
if nexus_core.monitor:
start_time = time.time()
ipoll = 0
while len(self.progressing_cascades) > 0:
elapsed_time = time.time() - start_time
self.log('elapsed time %.1f s' % elapsed_time,
' memory %3.2f MB' %
(memory.resident(children=True) / 1e6),
n=1,
progress=True)
NexusCore.wrote_something = False
ipoll += 1
self.machine.query_queue()
self.progress_cascades()
self.machine.submit_jobs()
self.update_process_ids()
time.sleep(nexus_core.sleep)
if NexusCore.wrote_something:
self.log()
#end if
#end while
elif len(self.progressing_cascades) > 0:
self.machine.query_queue()
self.progress_cascades()
self.machine.submit_jobs()
self.update_process_ids()
#end if
else:
self.progress_cascades()
#end if
self.log('Project finished\n')
NexusCore.write_end_splash()
def run_project(self,status=False,status_only=False):
self.log('\nProject starting',n=0)
self.init_cascades()
status_only = status_only or self.status_only
status = status or status_only
if status:
self.write_simulation_status()
if status_only:
return
#end if
#end if
self.log('\nstarting runs:\n'+30*'~',n=1)
if self.dependent_modes <= self.stages_set:
if self.monitor:
ipoll = 0
while len(self.progressing_cascades)>0:
self.dlog('\n\n\n'+70*'=',n=1)
self.log('poll',ipoll,' memory %3.2f MB'%(memory.resident()/1e6),n=1)
Pobj.wrote_something = False
self.dlog('cascades',self.progressing_cascades.keys(),n=2)
ipoll+=1
self.machine.query_queue()
self.progress_cascades()
self.machine.submit_jobs()
self.update_process_ids()
self.dlog('sleeping',self.sleep,n=2)
time.sleep(self.sleep)
self.dlog('awake',n=2)
if Pobj.wrote_something:
self.log()
#end if
#end while
elif len(self.progressing_cascades)>0:
self.machine.query_queue()
self.progress_cascades()
self.machine.submit_jobs()
self.update_process_ids()
#end if
else:
self.progress_cascades()
#end if
self.log('Project finished\n')
def run_project(self, status=False, status_only=False):
self.log("\nProject starting", n=0)
self.init_cascades()
status_only = status_only or self.status_only
status = status or status_only
if status:
self.write_simulation_status()
if status_only:
return
# end if
# end if
self.log("\nstarting runs:\n" + 30 * "~", n=1)
if self.dependent_modes <= self.stages_set:
if self.monitor:
ipoll = 0
while len(self.progressing_cascades) > 0:
self.dlog("\n\n\n" + 70 * "=", n=1)
self.log("poll", ipoll, " memory %3.2f MB" % (memory.resident() / 1e6), n=1)
Pobj.wrote_something = False
self.dlog("cascades", self.progressing_cascades.keys(), n=2)
ipoll += 1
self.machine.query_queue()
self.progress_cascades()
self.machine.submit_jobs()
self.dlog("sleeping", self.sleep, n=2)
time.sleep(self.sleep)
self.dlog("awake", n=2)
if Pobj.wrote_something:
self.log()
# end if
# end while
elif len(self.progressing_cascades) > 0:
self.machine.query_queue()
self.progress_cascades()
self.machine.submit_jobs()
# end if
else:
self.progress_cascades()
# end if
self.log("Project finished\n")
def __call__(self,msg,level='low',n=0,time=False,mem=False,width=75):
if self.verbosity==self.verbosity_levels.none:
return
elif self.verbosity >= self.verbosity_levels[level]:
if mem or time:
npad = max(0,width-2*(n+self.indent)-len(msg)-36)
if npad>0:
msg += npad*' '
#end if
if mem:
dm = 1e6 # MB
mnow = memory.resident(children=True)
msg += ' (mem add {:6.2f}, tot {:6.2f})'.format((mnow-self.mlast)/dm,(mnow-self.mstart)/dm)
self.mlast = mnow
#end if
if time:
tnow = process_time()
msg += ' (t elap {:7.3f}, tot {:7.3f})'.format(tnow-self.tlast,tnow-self.tstart)
self.tlast = tnow
#end if
#end if
log(msg,n=n+self.indent)
def mem_usage(self):
return int(resident() / 1e6)
def mem_usage(self):
return int(resident()/1e6)
def main():
parser = optparse.OptionParser(prog="sp_pipeline..py", \
version=" Chitrang Patel (May. 12, 2015)", \
usage="%prog INFILE(PsrFits FILE, SINGLEPULSE FILES)", \
description="Create single pulse plots to show the " \
"frequency sweeps of a single pulse, " \
"DM vs time, and SNR vs DM,"\
"in psrFits data.")
parser.add_option('--infile', dest='infile', type='string', \
help="Give a .inf file to read the appropriate header information.")
parser.add_option('--groupsfile', dest='txtfile', type='string', \
help="Give the groups.txt file to read in the groups information.")
parser.add_option('--mask', dest='maskfile', type='string', \
help="Mask file produced by rfifind. (Default: No Mask).", \
default=None)
options, args = parser.parse_args()
if not hasattr(options, 'infile'):
raise ValueError("A .inf file must be given on the command line! ")
if not hasattr(options, 'txtfile'):
raise ValueError("The groups.txt file must be given on the command line! ")
files = get_textfile(options.txtfile)
print_debug("Begining waterfaller... "+strftime("%Y-%m-%d %H:%M:%S"))
Detrendlen = 50
if not args[0].endswith("fits"):
raise ValueError("The first file must be a psrFits file! ")
basename = args[0][:-5]
filetype = "psrfits"
inffile = options.infile
topo, bary = bary_and_topo.bary_to_topo(inffile)
time_shift = bary-topo
inf = infodata.infodata(inffile)
RA = inf.RA
dec = inf.DEC
MJD = inf.epoch
mjd = Popen(["mjd2cal", "%f"%MJD], stdout=PIPE, stderr=PIPE)
date, err = mjd.communicate()
date = date.split()[2:5]
telescope = inf.telescope
N = inf.N
Total_observed_time = inf.dt *N
print_debug('getting file..')
rawdatafile = psrfits.PsrfitsFile(args[0])
print "rawdatafile",memory.resident()/(1024.0**3)
bin_shift = np.round(time_shift/rawdatafile.tsamp).astype('int')
for group in [6,5,4,3,2]:
rank = group+1
if files[group] != "Number of rank %i groups: 0 "%rank:
print_debug(files[group])
values = split_parameters(rank, options.txtfile)
lis = np.where(files == '\tRank: %i.000000'%rank)[0]
for ii in range(len(values)):
#### Array for Plotting DM vs SNR
print "DM, S/N",memory.resident()/(1024.0**3)
print_debug("Making arrays for DM vs Signal to Noise...")
temp_list = files[lis[ii]-6].split()
npulses = int(temp_list[2])
temp_lines = files[(lis[ii]+3):(lis[ii]+npulses+1)]
arr = np.split(temp_lines, len(temp_lines))
dm_list = []
time_list = []
for i in range(len(arr)):
dm_val= float(arr[i][0].split()[0])
time_val = float(arr[i][0].split()[2])
dm_list.append(dm_val)
time_list.append(time_val)
arr_2 = np.array([arr[i][0].split() for i in range(len(arr))], dtype = np.float32)
dm_arr = np.array([arr_2[i][0] for i in range(len(arr))], dtype = np.float32)
sigma_arr = np.array([arr_2[i][1] for i in range(len(arr))], dtype = np.float32)
print "After DM, S/N",memory.resident()/(1024.0**3)
#### Array for Plotting DM vs Time is in show_spplots.plot(...)
#### Setting variables up for the waterfall arrays.
j = ii+1
subdm = dm = sweep_dm= values[ii][0]
integrate_dm = None
sigma = values[ii][1]
sweep_posn = 0.0
topo_start_time = values[ii][2] - topo_timeshift(values[ii][2], time_shift, topo)[0]
sample_number = values[ii][3]
width_bins = values[ii][4]
binratio = 50
scaleindep = False
zerodm = None
downsamp = np.round((values[ii][2]/sample_number/6.54761904761905e-05)).astype('int')
duration = binratio * width_bins * rawdatafile.tsamp * downsamp
start = topo_start_time - (0.25 * duration)
if (start<0.0):
start = 0.0
pulse_width = width_bins*downsamp*6.54761904761905e-05
if sigma <= 10:
nsub = 32
elif sigma >= 10 and sigma < 15:
nsub = 64
else:
nsub = 96
nbins = np.round(duration/rawdatafile.tsamp).astype('int')
start_bin = np.round(start/rawdatafile.tsamp).astype('int')
dmfac = 4.15e3 * np.abs(1./rawdatafile.frequencies[0]**2 - 1./rawdatafile.frequencies[-1]**2)
nbinsextra = np.round((duration + dmfac * dm)/rawdatafile.tsamp).astype('int')
if (start_bin+nbinsextra) > N-1:
nbinsextra = N-1-start_bin
data = rawdatafile.get_spectra(start_bin, nbinsextra)
print "After rawdata",memory.resident()/(1024.0**3)
data = maskdata(data, start_bin, nbinsextra, options.maskfile)
#make an array to store header information for the .npz files
temp_filename = basename+"_DM%.1f_%.1fs_rank_%i"%(subdm, topo_start_time, rank)
# Array for Plotting Dedispersed waterfall plot - zerodm - OFF
print_debug("Running waterfaller with Zero-DM OFF...")
data, Data_dedisp_nozerodm = waterfall_array(start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm, integrate_dm, downsamp, scaleindep, width_bins, rawdatafile, binratio, data)
print "waterfall",memory.resident()/(1024.0**3)
# Add additional information to the header information array
text_array = np.array([args[0], 'Arecibo', RA, dec, MJD, rank, nsub, nbins, subdm, sigma, sample_number, duration, width_bins, pulse_width, rawdatafile.tsamp, Total_observed_time, topo_start_time, data.starttime, data.dt, data.numspectra, data.freqs.min(), data.freqs.max()])
#### Array for plotting Dedispersed waterfall plot zerodm - ON
print_debug("Running Waterfaller with Zero-DM ON...")
data = rawdatafile.get_spectra(start_bin, nbinsextra)
data = maskdata(data, start_bin, nbinsextra, options.maskfile)
zerodm = True
data, Data_dedisp_zerodm = waterfall_array(start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm, integrate_dm, downsamp, scaleindep, width_bins, rawdatafile, binratio, data)
print "waterfall",memory.resident()/(1024.0**3)
####Sweeped without zerodm
print_debug("Running waterfaller for sweeped arrays.")
start = start + (0.25*duration)
start_bin = np.round(start/rawdatafile.tsamp).astype('int')
sweep_duration = 4.15e3 * np.abs(1./rawdatafile.frequencies[0]**2-1./rawdatafile.frequencies[-1]**2)*sweep_dm
nbins = np.round(sweep_duration/(rawdatafile.tsamp)).astype('int')
if ((nbins+start_bin)> (N-1)):
nbins = N-1-start_bin
data = rawdatafile.get_spectra(start_bin, nbins)
data = maskdata(data, start_bin, nbins, options.maskfile)
zerodm = None
dm = None
data, Data_nozerodm = waterfall_array(start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm, integrate_dm, downsamp, scaleindep, width_bins, rawdatafile, binratio, data)
print "waterfall",memory.resident()/(1024.0**3)
text_array = np.append(text_array, sweep_duration)
text_array = np.append(text_array, data.starttime)
# Array to Construct the sweep
if sweep_dm is not None:
ddm = sweep_dm-data.dm
delays = psr_utils.delay_from_DM(ddm, data.freqs)
delays -= delays.min()
delays_nozerodm = delays
freqs_nozerodm = data.freqs
# Sweeped with zerodm-on
zerodm = True
downsamp_temp = 1
data, Data_zerodm = waterfall_array(start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm, integrate_dm, downsamp_temp, scaleindep, width_bins, rawdatafile, binratio, data)
print "waterfall",memory.resident()/(1024.0**3)
# Saving the arrays into the .spd file.
with open(temp_filename+".spd", 'wb') as f:
np.savez_compressed(f, Data_dedisp_nozerodm = Data_dedisp_nozerodm.astype(np.float16), Data_dedisp_zerodm = Data_dedisp_zerodm.astype(np.float16), Data_nozerodm = Data_nozerodm.astype(np.float16), delays_nozerodm = delays_nozerodm, freqs_nozerodm = freqs_nozerodm, Data_zerodm = Data_zerodm.astype(np.float16), dm_arr= map(np.float16, dm_arr), sigma_arr = map(np.float16, sigma_arr), dm_list= map(np.float16, dm_list), time_list = map(np.float16, time_list), text_array = text_array)
print_debug("Now plotting...")
print "Before plot..",memory.resident()/(1024.0**3)
show_spplots.plot(temp_filename+".spd", args[1:], xwin=False, outfile = basename, tar = None)
print "After plot..",memory.resident()/(1024.0**3)
print_debug("Finished plot %i " %j+strftime("%Y-%m-%d %H:%M:%S"))
print_debug("Finished group %i... "%rank+strftime("%Y-%m-%d %H:%M:%S"))
print_debug("Finished running waterfaller... "+strftime("%Y-%m-%d %H:%M:%S"))
def main():
parser = optparse.OptionParser(prog="sp_pipeline..py", \
version=" Chitrang Patel (May. 12, 2015)", \
usage="%prog INFILE(PsrFits FILE, SINGLEPULSE FILES)", \
description="Create single pulse plots to show the " \
"frequency sweeps of a single pulse, " \
"DM vs time, and SNR vs DM,"\
"in psrFits data.")
parser.add_option('--infile', dest='infile', type='string', \
help="Give a .inf file to read the appropriate header information.")
parser.add_option('--groupsfile', dest='txtfile', type='string', \
help="Give the groups.txt file to read in the groups information.")
parser.add_option('--mask', dest='maskfile', type='string', \
help="Mask file produced by rfifind. (Default: No Mask).", \
default=None)
options, args = parser.parse_args()
if not hasattr(options, 'infile'):
raise ValueError("A .inf file must be given on the command line! ")
if not hasattr(options, 'txtfile'):
raise ValueError(
"The groups.txt file must be given on the command line! ")
files = get_textfile(options.txtfile)
print_debug("Begining waterfaller... " + strftime("%Y-%m-%d %H:%M:%S"))
Detrendlen = 50
if not args[0].endswith("fits"):
raise ValueError("The first file must be a psrFits file! ")
basename = args[0][:-5]
filetype = "psrfits"
inffile = options.infile
topo, bary = bary_and_topo.bary_to_topo(inffile)
time_shift = bary - topo
inf = infodata.infodata(inffile)
RA = inf.RA
dec = inf.DEC
MJD = inf.epoch
mjd = Popen(["mjd2cal", "%f" % MJD], stdout=PIPE, stderr=PIPE)
date, err = mjd.communicate()
date = date.split()[2:5]
telescope = inf.telescope
N = inf.N
Total_observed_time = inf.dt * N
print_debug('getting file..')
rawdatafile = psrfits.PsrfitsFile(args[0])
print "rawdatafile", memory.resident() / (1024.0**3)
bin_shift = np.round(time_shift / rawdatafile.tsamp).astype('int')
for group in [6, 5, 4, 3, 2]:
rank = group + 1
if files[group] != "Number of rank %i groups: 0 " % rank:
print_debug(files[group])
values = split_parameters(rank, options.txtfile)
lis = np.where(files == '\tRank: %i.000000' % rank)[0]
for ii in range(len(values)):
#### Array for Plotting DM vs SNR
print "DM, S/N", memory.resident() / (1024.0**3)
print_debug("Making arrays for DM vs Signal to Noise...")
temp_list = files[lis[ii] - 6].split()
npulses = int(temp_list[2])
temp_lines = files[(lis[ii] + 3):(lis[ii] + npulses + 1)]
arr = np.split(temp_lines, len(temp_lines))
dm_list = []
time_list = []
for i in range(len(arr)):
dm_val = float(arr[i][0].split()[0])
time_val = float(arr[i][0].split()[2])
dm_list.append(dm_val)
time_list.append(time_val)
arr_2 = np.array([arr[i][0].split() for i in range(len(arr))],
dtype=np.float32)
dm_arr = np.array([arr_2[i][0] for i in range(len(arr))],
dtype=np.float32)
sigma_arr = np.array([arr_2[i][1] for i in range(len(arr))],
dtype=np.float32)
print "After DM, S/N", memory.resident() / (1024.0**3)
#### Array for Plotting DM vs Time is in show_spplots.plot(...)
#### Setting variables up for the waterfall arrays.
j = ii + 1
subdm = dm = sweep_dm = values[ii][0]
integrate_dm = None
sigma = values[ii][1]
sweep_posn = 0.0
topo_start_time = values[ii][2] - topo_timeshift(
values[ii][2], time_shift, topo)[0]
sample_number = values[ii][3]
width_bins = values[ii][4]
binratio = 50
scaleindep = False
zerodm = None
downsamp = np.round((values[ii][2] / sample_number /
6.54761904761905e-05)).astype('int')
duration = binratio * width_bins * rawdatafile.tsamp * downsamp
start = topo_start_time - (0.25 * duration)
if (start < 0.0):
start = 0.0
pulse_width = width_bins * downsamp * 6.54761904761905e-05
if sigma <= 10:
nsub = 32
elif sigma >= 10 and sigma < 15:
nsub = 64
else:
nsub = 96
nbins = np.round(duration / rawdatafile.tsamp).astype('int')
start_bin = np.round(start / rawdatafile.tsamp).astype('int')
dmfac = 4.15e3 * np.abs(1. / rawdatafile.frequencies[0]**2 -
1. / rawdatafile.frequencies[-1]**2)
nbinsextra = np.round(
(duration + dmfac * dm) / rawdatafile.tsamp).astype('int')
if (start_bin + nbinsextra) > N - 1:
nbinsextra = N - 1 - start_bin
data = rawdatafile.get_spectra(start_bin, nbinsextra)
print "After rawdata", memory.resident() / (1024.0**3)
data = maskdata(data, start_bin, nbinsextra, options.maskfile)
#make an array to store header information for the .npz files
temp_filename = basename + "_DM%.1f_%.1fs_rank_%i" % (
subdm, topo_start_time, rank)
# Array for Plotting Dedispersed waterfall plot - zerodm - OFF
print_debug("Running waterfaller with Zero-DM OFF...")
data, Data_dedisp_nozerodm = waterfall_array(
start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm,
integrate_dm, downsamp, scaleindep, width_bins,
rawdatafile, binratio, data)
print "waterfall", memory.resident() / (1024.0**3)
# Add additional information to the header information array
text_array = np.array([
args[0], 'Arecibo', RA, dec, MJD, rank, nsub, nbins, subdm,
sigma, sample_number, duration, width_bins, pulse_width,
rawdatafile.tsamp, Total_observed_time, topo_start_time,
data.starttime, data.dt, data.numspectra,
data.freqs.min(),
data.freqs.max()
])
#### Array for plotting Dedispersed waterfall plot zerodm - ON
print_debug("Running Waterfaller with Zero-DM ON...")
data = rawdatafile.get_spectra(start_bin, nbinsextra)
data = maskdata(data, start_bin, nbinsextra, options.maskfile)
zerodm = True
data, Data_dedisp_zerodm = waterfall_array(
start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm,
integrate_dm, downsamp, scaleindep, width_bins,
rawdatafile, binratio, data)
print "waterfall", memory.resident() / (1024.0**3)
####Sweeped without zerodm
print_debug("Running waterfaller for sweeped arrays.")
start = start + (0.25 * duration)
start_bin = np.round(start / rawdatafile.tsamp).astype('int')
sweep_duration = 4.15e3 * np.abs(
1. / rawdatafile.frequencies[0]**2 -
1. / rawdatafile.frequencies[-1]**2) * sweep_dm
nbins = np.round(sweep_duration /
(rawdatafile.tsamp)).astype('int')
if ((nbins + start_bin) > (N - 1)):
nbins = N - 1 - start_bin
data = rawdatafile.get_spectra(start_bin, nbins)
data = maskdata(data, start_bin, nbins, options.maskfile)
zerodm = None
dm = None
data, Data_nozerodm = waterfall_array(
start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm,
integrate_dm, downsamp, scaleindep, width_bins,
rawdatafile, binratio, data)
print "waterfall", memory.resident() / (1024.0**3)
text_array = np.append(text_array, sweep_duration)
text_array = np.append(text_array, data.starttime)
# Array to Construct the sweep
if sweep_dm is not None:
ddm = sweep_dm - data.dm
delays = psr_utils.delay_from_DM(ddm, data.freqs)
delays -= delays.min()
delays_nozerodm = delays
freqs_nozerodm = data.freqs
# Sweeped with zerodm-on
zerodm = True
downsamp_temp = 1
data, Data_zerodm = waterfall_array(start_bin, dmfac, duration,
nbins, zerodm, nsub, subdm,
dm, integrate_dm,
downsamp_temp, scaleindep,
width_bins, rawdatafile,
binratio, data)
print "waterfall", memory.resident() / (1024.0**3)
# Saving the arrays into the .spd file.
with open(temp_filename + ".spd", 'wb') as f:
np.savez_compressed(
f,
Data_dedisp_nozerodm=Data_dedisp_nozerodm.astype(
np.float16),
Data_dedisp_zerodm=Data_dedisp_zerodm.astype(
np.float16),
Data_nozerodm=Data_nozerodm.astype(np.float16),
delays_nozerodm=delays_nozerodm,
freqs_nozerodm=freqs_nozerodm,
Data_zerodm=Data_zerodm.astype(np.float16),
dm_arr=map(np.float16, dm_arr),
sigma_arr=map(np.float16, sigma_arr),
dm_list=map(np.float16, dm_list),
time_list=map(np.float16, time_list),
text_array=text_array)
print_debug("Now plotting...")
print "Before plot..", memory.resident() / (1024.0**3)
show_spplots.plot(temp_filename + ".spd",
args[1:],
xwin=False,
outfile=basename,
tar=None)
print "After plot..", memory.resident() / (1024.0**3)
print_debug("Finished plot %i " % j +
strftime("%Y-%m-%d %H:%M:%S"))
print_debug("Finished group %i... " % rank +
strftime("%Y-%m-%d %H:%M:%S"))
print_debug("Finished running waterfaller... " +
strftime("%Y-%m-%d %H:%M:%S"))
def show_mem():
print "memory usage:"
import memory
print memory.memory()/(1024*1024)
print memory.resident()/(1024*1024)
def show_mem():
print "memory usage:"
import memory
print memory.memory() / (1024 * 1024)
print memory.resident() / (1024 * 1024)
