def main():
progname = 'find_peak.py'
args = get_opt(progname)
# in_files = ['/Users/ferdman/Work/pulsar/0737-3039A/profs/add_campaigns/0737-3039A.20??.??.add.asc']
# in_files = ['/Users/ferdman/Work/pulsar/1756-2251/profs/add_epochs/*.add.asc']
# in_files = ['/Users/ferdman/Work/pulsar/1756-2251/profs/template/gasp/1756-2251.1400.2048_bins.std']
# in_files = ['/Users/ferdman/Work/pulsar/1756-2251/profs/singleprofs/gasp/TemplateTestRot.asc']
#phase_cut = []
# number of points on *each* side of the profile in fit, so there are
# 2*n_poly_fit + 1 points to choose from
# matplotlib.rc('font', size=22)
input_files = []
for file_name in args.profiles:
# For some reason this works and ".append()" doesn't:
input_files[len(input_files):] = glob.glob(file_name)
n_files = len(input_files)
print "Number of input profiles: ", n_files, '\n'
# print "Input files = ", input_files
if(len(args.phasecut) > 0):
print "Will separate profiles into separate phase ranges before processing."
if(len(args.phasecut) ==1):
print " Profile will be split along phase ", args.phasecut[0]
else:
print " Profile will be split along phases ", args.phasecut
print " "
mjd_obs = np.zeros(len(input_files))
# A = np.zeros((len(input_files), len(phase_cut)+1))
# pdf_width = np.zeros((len(input_files), len(phase_cut)+1), n_hist_bins)
# x_width = np.zeros((len(input_files), len(phase_cut)+1), n_hist_bins)
for i_prof in np.arange(n_files):
prof_data = read_asc_prof(input_files[i_prof])
prof_head = read_asc_header(input_files[i_prof])
psr_name = prof_head['psrname']
mjd_obs[i_prof] = prof_head['imjd'] + prof_head['smjd']/86400.
n_bins = len(prof_data['i'])
phase_cut_bins = np.append(0., args.phasecut)*n_bins
phase_cut_bins = np.append(phase_cut_bins, n_bins)
phase_cut_bins = phase_cut_bins.astype(int)
#print "phase_cut_bins = ", phase_cut_bins
phase_cuts = phase_cut_bins.astype(float)/n_bins
#print "arange = ", np.arange(len(phase_cut_bins) - 1)
# print "File ", input_files[i_prof]
print " "
mjdtext = 'MJD {0:8.2f}'.format(mjd_obs[i_prof])
datetext = '{0:10s}'.format(mjd.mjdtodate(mjd_obs[i_prof], dateformat="%Y-%b-%d"))
print mjdtext, ' ', datetext
prof_in = prof_data.copy()
for i_phase in np.arange(len(phase_cut_bins) - 1):
plt.figure()
print " Phase range {0:.2f} to {1:.2f}".format(prof_data['phase'][phase_cut_bins[i_phase]], \
prof_data['phase'][phase_cut_bins[i_phase+1]-1])
print " "
prof_in['i'] = prof_data['i'][phase_cut_bins[i_phase]:phase_cut_bins[i_phase+1]-1]
prof_in['phase'] = prof_data['phase'][phase_cut_bins[i_phase]:phase_cut_bins[i_phase+1]-1]
x_peak, y_peak = get_peak(prof_in, n_pts_fit=12, n_order=5, n_test_fit=4)
if(len(args.phasecut) > 0):
plt.savefig('peak_fit.'+'{0:5.0f}'.format(mjd_obs[i_prof])+
'.phase_{0:d}'.format(i_phase)+'.png')
else:
plt.savefig('peak_fit.'+'{0:5.0f}'.format(mjd_obs[i_prof])+
'.png')
if(x_peak < 0):
print 'Could not choose one. Exiting: '
exit()
print 'Peak phase: ', x_peak[0]
print 'Peak height: ', y_peak[0]
def main():
# Input asp-style ascii profile will be first argument.
# (command-line options to come later)
prog_name = argv[0].strip(".py")
# Scale factor for plotting difference plots
scale = 2.0
# ref_file = argv[len(argv)-1]
ref_file = argv[1]
# arg = get_opt(prog_name)
print "n_args = ", len(argv) - 1
# prof_file = argv[1]
input_files = []
for file in argv[1:]:
# For some reason this works and ".appen()" doesn't:
input_files[len(input_files) :] = glob.glob(file)
# input_files.append(glob.glob(file))
input_files.reverse()
if input_files.count(ref_file) > 0:
input_files.remove(ref_file)
# First, read in profile data file, and assign each column to a separate
# numpy array
# prof_file = input_files[0]
# prof_header = read_asc_header(prof_file)
# prof_data[0] = read_asc_prof(prof_file)
ref_header = read_asc_header(ref_file)
ref_data = read_asc_prof(ref_file)
# ref_data['i'] = norm(ref_data['i'], duty)
# if(len(input_files) > 1):
# for prof_file in input_files:
prof_data = []
date_text = []
nobs = []
for i_prof in np.arange(len(input_files)):
prof_header = read_asc_header(input_files[i_prof])
duty = get_duty(prof_header["psrname"])
nobs.append(prof_header["obscode"])
print "NOBS = ", nobs
prof_data_temp = read_asc_prof(input_files[i_prof])
# prof_data_temp['i'] = norm(prof_data_temp['i'], duty)
# prof_data_temp['i'] = prof_data_temp['i'] + i_prof
diff_prof = remove_base((prof_data_temp["i"] - ref_data["i"]), duty)
prof_data_temp["i"] = scale * (diff_prof) + i_prof + 1.2
print "Index = ", i_prof, ", Min = ", np.min(prof_data_temp["i"]), ", Max = ", np.max(prof_data_temp["i"])
prof_data.append(prof_data_temp)
# Set up labelling for each profile:
prof_date = mjd.mjdtodate(prof_header["imjd"], dateformat="%Y %b %d")
date_text.append((0.8, i_prof + 0.25, prof_date))
print "Date = ", date_text
# Make first prof in list for plot to be the ref profile
prof_data.append(ref_data)
ref_date = mjd.mjdtodate(ref_header["imjd"], dateformat="%Y %b %d")
date_text.append((0.5, 0.20, ref_date))
nobs.append(ref_header["obscode"])
nobs_unique = list(set(nobs))
clr = []
for i_nobs in range(len(nobs)):
if nobs_unique <= 1:
clr.append("black")
else:
clr.append(cm.gist_heat(float(nobs_unique.index(nobs[i_nobs])) / float(len(nobs_unique))))
# Do this just to make the ordering such that the first alphanumerically
# is at the top...
# prof_data.reverse()
# date_text.reverse()
print "LENGTH of PROF DATA = ", len(prof_data)
print "LENGTH of colour = ", len(clr)
plot_prof(
prof_data,
yticks=False,
canvassize=(8, 10),
hgrid=False,
vgrid=False,
ylim=(np.min(prof_data[0]["i"]) - 0.1, len(input_files) + 0.1 + 1.0),
figtext=date_text,
linecolour=clr,
)
# meaningThe following means that the 2nd argument is the
# desired output plot file name
# plot_file = 'multi_profile.png'
plot_file = "diff_profile.pdf"
# plt.show()
plt.savefig(plot_file)
def main():
progname = 'find_widths.py'
args = get_opt(progname)
# Cast command-line arguments into separate variables:
in_files = args.profiles
percent_height = args.percent
phase_cut = args.phasecut
n_poly_fit = args.npolyfit
n_poly_order = args.npolyorder
if(args.poisson):
n_pts_omit = 0
else:
n_pts_omit = n_poly_fit - 1
n_bootstrap = args.niter
n_hist_bins = args.nhistbins
n_pts_test = args.ntestphase
peak_phase = args.peakphase
peak_height = args.peakheight
n_peak_bins = int(args.npeakbins/2.) # making it to be number of points on either side of data peak
if(args.nobase):
no_base=True
else:
no_base=False
## in_files = ['/Users/ferdman/Work/pulsar/0737-3039A/profs/add_campaigns/0737-3039A.20??.??.add.asc']
### in_files = ['/Users/ferdman/Work/pulsar/1756-2251/profs/add_epochs/*.add.asc']
## in_files = ['/Users/ferdman/Work/pulsar/1756-2251/profs/singleprofs/gasp/TemplateTestRot.asc']
## percent_height = float(argv[1])
# percent_height = 25.
# phase_cut = [0.5]
## phase_cut = []
# number of points on *each* side of the profile in fit, so there are
# 2*n_poly_fit + 1 points to choose from
## n_poly_fit = 12
## n_poly_fit = 12
# number of iterations of fit
## n_bootstrap = 32768
## n_hist_bins = 48
## n_hist_bins = 32
# number of points to omit for each iteration of fit
# (= half total # of points -1)
## n_pts_omit = n_poly_fit-1
# number of points to use around expected phase to do root finding
# for getting exact phase
## n_pts_test = 4
# order of polynomial in fit
## n_poly_order = 6
## n_poly_order = 10
##### peak_phase = 0.00321002 # known from finding peak in high-S/N template which we assume is aligned
# n_poly_fit = 16
# n_bootstrap = 32768
# n_hist_bins = 48
# n_pts_omit = n_poly_fit
# n_pts_test = 6
# n_poly_order = 8
# n_poly_order = 2*n_poly_fit - n_pts_omit -
## n_subplot_rows = 3 # 0737A
## n_subplot_cols = 5 # 0737A
n_subplot_rows = 6
n_subplot_cols = 6
# matplotlib.rc('font', size=22)
input_files = []
for file_name in in_files:
# For some reason this works and ".append()" doesn't:
input_files[len(input_files):] = glob.glob(file_name)
n_files = len(input_files)
# Determine number of rows and columns for subplotting
# bootstrap results histograms. Always have it so n_cols >= n_rows
n_subplot_rows, n_subplot_cols = get_rows_cols(n_files)
print "n_profs = ", n_files
print "Input files = ", input_files
print " "
print "Percent height = ", percent_height
print "Num rows = ", n_subplot_rows
print "Num cols = ", n_subplot_cols
if(len(phase_cut) > 0):
print "Will separate profiles into separate phase ranges before processing."
if(len(phase_cut) ==1):
print " Profile will be split along phase ", phase_cut[0]
else:
print " Profile will be split along phases ", phase_cut
print " "
mjd_width = np.zeros(len(input_files))
width = np.zeros((len(input_files), len(phase_cut)+1))
width_err = np.zeros((len(input_files), len(phase_cut)+1))
# A = np.zeros((len(input_files), len(phase_cut)+1))
# pdf_width = np.zeros((len(input_files), len(phase_cut)+1), n_hist_bins)
# x_width = np.zeros((len(input_files), len(phase_cut)+1), n_hist_bins)
for i_prof in np.arange(n_files):
prof_data = read_asc_prof(input_files[i_prof], ionly=True) # Stokes I only for now
prof_head = read_asc_header(input_files[i_prof])
psr_name = prof_head['psrname']
mjd_width[i_prof] = prof_head['imjd'] + prof_head['smjd']/86400.
n_bins = len(prof_data['i'])
phase_cut_bins = np.append(0., phase_cut)*n_bins
phase_cut_bins = np.append(phase_cut_bins, n_bins)
phase_cut_bins = phase_cut_bins.astype(int)
#print "phase_cut_bins = ", phase_cut_bins
phase_cuts = phase_cut_bins.astype(float)/n_bins
#print "arange = ", np.arange(len(phase_cut_bins) - 1)
# print "File ", input_files[i_prof]
print " "
mjdtext = 'MJD {0:8.2f}'.format(mjd_width[i_prof])
datetext = '{0:10s}'.format(mjd.mjdtodate(mjd_width[i_prof], dateformat="%Y-%b-%d"))
print mjdtext, ' ', datetext
prof_in = prof_data.copy()
# x_peak, y_peak = get_peak(prof_in, n_pts_fit=10, n_order=19, n_test_fit=4)
# plt.savefig('peak_fit.'+'{0:5.0f}'.format(mjd_width[i_prof])+'.png')
for i_phase in np.arange(len(phase_cut_bins) - 1):
print " Phase range {0:.2f} to {1:.2f}".format(prof_data['phase'][phase_cut_bins[i_phase]], \
prof_data['phase'][phase_cut_bins[i_phase+1]-1])
print " "
prof_in['i'] = prof_data['i'][phase_cut_bins[i_phase]:phase_cut_bins[i_phase+1]-1]
prof_in['phase'] = prof_data['phase'][phase_cut_bins[i_phase]:phase_cut_bins[i_phase+1]-1]
width[i_prof,i_phase], width_err[i_prof,i_phase], A, pdf_width, x_width = \
get_width(prof_in, psr_name, percent_height,
x_peak=peak_phase, y_peak=peak_height,
nobase=no_base,
n_pts_fit=n_poly_fit, n_order=n_poly_order,
n_omit=n_pts_omit, n_test_fit=n_pts_test,
n_boot=n_bootstrap, hist_bins=n_hist_bins,
n_peak_bins=n_peak_bins, poisson=args.poisson,
return_more=True)
# Plot gaussian fit to bootstrap histograms as we go along as a grid of subplots:
fig = plt.figure(i_phase)
print 'N_ROWS = ', n_subplot_rows
print 'N_COLS = ', n_subplot_cols
print 'I_PROF = ', i_prof
ax = fig.add_subplot(n_subplot_rows, n_subplot_cols, i_prof+1)
# plt.subplot(3, 4, i_prof)
ax.plot(x_width*360., pdf_width, linestyle='steps-mid', linewidth=1.4, color='blue')
ax.plot(x_width*360., gaussian(x_width, A, width[i_prof, i_phase], width_err[i_prof, i_phase]), color='red')
# Set y limits here since we can't retrieve them later:
ax.set_ylim(0., 1.1*np.amax(np.append(pdf_width, A)))
# set the number of ticks, putting them at nice locations, pruning off edge labels if they
# happen on the plot corners:
ax.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(4, prune='both'))
ax.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(4, prune='both'))
# No y ticklabels:
ax.yaxis.set_ticklabels([])
# Add date as text to the top left corner of each subplot
ax.text(0.04, 0.96, datetext, horizontalalignment='left', verticalalignment='top', \
fontsize='6', transform=ax.transAxes)
print " Width = ", width[i_prof, i_phase], "+/-", width_err[i_prof, i_phase]
print ""
print ""
# plt.show()
# Set up formatter for scaling xtick labels:
# major_formatter = FuncFormatter(tick_scale_formatter)
print "len(phase_cut_bins) = ", len(phase_cut_bins)
# print "phase_cuts = ", phase_cuts
for i_phase in np.arange(len(phase_cut_bins) - 1):
width_data = {'mjd':mjd_width, 'width':width[:, i_phase], 'werr':width_err[:, i_phase]}
if(args.outbase==None):
outfile_root = '{0}.w{1:.1f}.{2:3.1f}_{3:3.1f}'.format(psr_name, percent_height, phase_cuts[i_phase], phase_cuts[i_phase+1])
else:
outfile_root = args.outbase
# Plot widths we just calculated
plot_widths(width_data, yunits='deg')
plt.savefig(outfile_root+'.png')
print np.std(width_data['width'])*360. # std dev in degrees
print np.mean(width_data['werr'])*360. # Mean error
print np.median(width_data['werr'])*360. # Mean error
# Output plot of bootstrap histogram
fig = plt.figure(i_phase)
# Find overall lowest and highest x values, and calculate differnce
x_lo = 360.0*np.amin((width_data['width'] - 6.0*width_data['werr']))
x_hi = 360.0*np.amin((width_data['width'] + 6.0*width_data['werr']))
x_span = np.abs(x_hi - x_lo)
# Run through each profile and adjust limits to be consistent and show variation, and fix up labels
for i_prof in np.arange(n_files):
ax = fig.add_subplot(n_subplot_rows, n_subplot_cols, i_prof+1)
# Centre on histogram, but set limits so that all subplots are on same scale:
ax.set_xlim(360.*(width_data['width'][i_prof]) - 0.5*x_span, \
360.0*(width_data['width'][i_prof]) + 0.5*x_span)
# Set text size for tick labels, and get rid of all labels other than lower x axis:
ax.tick_params(axis='x', labelsize=8, top='off')
ax.tick_params(axis='y', left='off', right='off')
# Rescale tick labels to some reasonable numbers by removing the lowest x value of all width hists
major_formatter = FuncFormatter(lambda x, pos: ('%.1f')%(x - x_lo))
ax.xaxis.set_major_formatter(major_formatter)
# x_tick_labels = ax.get_xticklabels()
# tick_label.set_label(str( float(tick_label.get_text())/x_base_val) )
# x_tick_vals = [str( float(x_tick_labels[i_tick])/x_base_val ) for i_tick in len(x_ticks)]
# ax.xticks(x_tick_locs, x_tick_labels)
# ax.ticklabel_format(useOffset=0.6475, axis='x')
# for tick in ax.xaxis.get_major_ticks():
# tick.label1.set_fontsize(8)
# for tick in ax.yaxis.get_major_ticks():
# tick.label1.set_fontsize(8)
# Set common labels by setting text on current plot (easiest way I found) -- x label only here:
fig.text(0.5, 0.04, \
'{0:2d}% profile width (- {1:.1f} degrees)'.format(int(percent_height), x_lo), \
ha='center', va='center')
# Output bootstrap histogram plots:
plt.savefig(outfile_root+'.boot_fit.png')
# Now output widths to file.
outfile = '{0}.w{1:.1f}.{2:3.1f}_{3:3.1f}'.format(psr_name,
percent_height, phase_cuts[i_phase], phase_cuts[i_phase+1])+'.dat'
f_out = open(outfile, 'w')
# First include a header line containing PSR name, percent height,
# and phase range (which will usually be 0.0 to 1.0)
f_out.write('# {0} w{1:.1f} {2:3.1f} {3:3.1f}\n'.format(psr_name,
percent_height, phase_cuts[i_phase], phase_cuts[i_phase+1]))
np.savetxt(outfile_root+'.dat',
np.transpose((width_data['mjd'], width_data['width'], width_data['werr'])),
fmt='%-23.15f %14.10f %14.10f') # x,y,z equal sized 1D arrays
def main():
# Input asp-style ascii profile will be first argument.
# (command-line options to come later)
prog_name = argv[0].strip('.py')
# Scale factor for plotting difference plots
scale = 2.0
# arg = get_opt(prog_name)
# prof_file = argv[1]
# ref_file = argv[1]
ref_file = argv[len(argv)-1]
input_files = []
for file in argv[1:]:
# For some reason this works and ".append()" doesn't:
input_files[len(input_files):] = glob.glob(file)
# input_files.append(glob.glob(file))
input_files.reverse()
# Remove reference file from list if it is in the plotting list
if(input_files.count(ref_file) > 0):
input_files.remove(ref_file)
duty = get_duty('0737-3039A')
# Read in reference profile
ref_header = read_asc_header(ref_file)
ref_data = read_asc_prof(ref_file)
# ref_data['i'] = norm(ref_data['i'], duty)
# Make first prof in list for plot to be the ref profile
prof_data = [ref_data]
ref_date = mjd.mjdtodate(ref_header['imjd'], \
dateformat='%Y %b %d')
date_text = [(0.5, 0.20, ref_date)]
nobs = []
# Now calculate difference profiles and append to plotting list
for i_prof in np.arange(len(input_files)):
prof_header = read_asc_header(input_files[i_prof])
nobs.append(prof_header['obscode'])
print 'NOBS = ', nobs
prof_data_temp = read_asc_prof(input_files[i_prof])
# prof_data_temp['i'] = norm(prof_data_temp['i'], duty)
diff_prof = remove_base((prof_data_temp['i'] - ref_data['i']), duty)
prof_data_temp['i'] = scale*(diff_prof) + i_prof + 1.2
print "Index = ", i_prof
prof_data.append(prof_data_temp)
# Set up labelling for each profile:
prof_date = mjd.mjdtodate(prof_header['imjd'], \
dateformat='%Y %b %d')
date_text.append((0.5, i_prof+1.4+scale*0.02, prof_date))
print "Date = ", date_text
nobs_unique = list(set(nobs))
clr=[]
for i_nobs in range(len(nobs)):
if(nobs_unique <= 1):
clr.append('black')
else:
clr.append(cm.gist_heat(float(nobs_unique.index(nobs[i_nobs]))/float(len(nobs_unique))))
# Do this just to make the ordering such that the first alphanumerically
# is at the top...
# prof_data.reverse()
# date_text.reverse()
plot_prof(prof_data, yticks=False, canvassize=(8,10), vgrid=False, \
ylim=(np.min(prof_data[0]['i'])-0.1, len(input_files)+1 +0.1), \
figtext=date_text, linecolour=clr)
# meaningThe following means that the 2nd argument is the
# desired output plot file name
# plot_file = 'diff_profile.png'
plot_file = 'diff_profile.pdf'
plt.savefig(plot_file)
def main():
# Input asp-style ascii profile will be first argument.
# (command-line options to come later)
prog_name = argv[0].strip('.py')
# arg = get_opt(prog_name)
# prof_file = argv[1]
input_files = []
for file in argv[1:]:
# For some reason this works and ".appen()" doesn't:
input_files[len(input_files):] = glob.glob(file)
# input_files.append(glob.glob(file))
input_files.reverse()
prof_data = []
# prof_date = []
date_text = []
# First, read in profile data file, and assign each column to a separate
# numpy array
# prof_file = input_files[0]
# prof_header = read_asc_header(prof_file)
# prof_data[0] = read_asc_prof(prof_file)
duty = get_duty('0737-3039A')
# if(len(input_files) > 1):
# for prof_file in input_files:
for i_prof in np.arange(len(input_files)):
prof_header = read_asc_header(input_files[i_prof])
prof_data_temp = read_asc_prof(input_files[i_prof])
prof_data_temp['i'] = norm(prof_data_temp['i'], duty)
prof_data_temp['i'] = prof_data_temp['i'] + i_prof
print "Index = ", i_prof, \
", Min = ", np.min(prof_data_temp['i']), \
", Max = ", np.max(prof_data_temp['i'])
prof_data.append(prof_data_temp)
# Set up labelling for each profile:
prof_date = mjd.mjdtodate(prof_header['imjd'], \
dateformat='%Y %b %d')
date_text.append((0.5, i_prof+0.16, prof_date))
print "Date = ", date_text
# Do this just to make the ordering such that the first alphanumerically
# is at the top...
# prof_data.reverse()
# date_text.reverse()
plot_prof(prof_data, yticks=False, canvassize=(8,10), \
hgrid=False, vgrid=False, \
ylim=(np.min(prof_data[0]['i'])-0.1, len(input_files)+0.1), \
figtext=date_text)
# meaningThe following means that the 2nd argument is the
# desired output plot file name
# plot_file = 'multi_profile.png'
plot_file = 'multi_profile.eps'
plt.savefig(plot_file)
def main():
# Input asp-style ascii profile will be first argument.
# (command-line options to come later)
prog_name = argv[0].strip('.py')
# arg = get_opt(prog_name)
print 'n_args = ', len(argv) - 1
# prof_file = argv[1]
input_files = []
for file in argv[1:]:
# For some reason this works and ".appen()" doesn't:
input_files[len(input_files):] = glob.glob(file)
# input_files.append(glob.glob(file))
input_files.reverse()
prof_data = []
# prof_date = []
date_text = []
# First, read in profile data file, and assign each column to a separate
# numpy array
# prof_file = input_files[0]
# prof_header = read_asc_header(prof_file)
# prof_data[0] = read_asc_prof(prof_file)
# if(len(input_files) > 1):
# for prof_file in input_files:
nobs = []
for i_prof in np.arange(len(input_files)):
prof_header = read_asc_header(input_files[i_prof])
duty = get_duty(prof_header['psrname'])
nobs.append(prof_header['obscode'])
print 'NOBS = ', nobs
prof_data_temp = read_asc_prof(input_files[i_prof], ionly=True)
prof_data_temp['i'] = norm(prof_data_temp['i'], duty)
prof_data_temp['i'] = prof_data_temp['i'] + i_prof
print "Index = ", i_prof, \
", Min = ", np.min(prof_data_temp['i']), \
", Max = ", np.max(prof_data_temp['i'])
prof_data.append(prof_data_temp)
# Set up labelling for each profile:
prof_date = mjd.mjdtodate(prof_header['imjd'], \
dateformat='%Y %b %d')
date_text.append((0.8, i_prof+0.25, prof_date))
print "Date = ", date_text
nobs_unique = list(set(nobs))
clr=[]
for i_nobs in range(len(nobs)):
if(nobs_unique <= 1):
clr.append('black')
else:
clr.append(cm.gist_heat(float(nobs_unique.index(nobs[i_nobs]))/float(len(nobs_unique))))
# Do this just to make the ordering such that the first alphanumerically
# is at the top...
# prof_data.reverse()
# date_text.reverse()
plot_prof(prof_data, yticks=False, canvassize=(8,10), \
hgrid=False, vgrid=False, \
ylim=(np.min(prof_data[0]['i'])-0.1, len(input_files)+0.1),
figtext=date_text, linecolour=clr)
# meaningThe following means that the 2nd argument is the
# desired output plot file name
# plot_file = 'multi_profile.png'
plot_file = 'multi_profile.png'
# plt.show()
plt.savefig(plot_file)
def xte_chan2energy(ph_mjd, ph_chan, ph_pcuid, return_chans=False):
# Set up start and stop dates for channel-energy table
start_date = [datetime(1800,1,1,0,0,0), # just pick a time far in the past here
datetime(1996, 3, 21, 18, 33, 0),
datetime(1996, 4, 15, 23, 5, 0),
datetime(1999, 3, 22, 17, 37, 0),
datetime(2000, 5, 13, 0, 0, 0)]
stop_date = [datetime(1996, 3, 21, 18, 33, 0),
datetime(1996, 4, 15, 23, 5, 0),
datetime(1999, 3, 22, 17, 37, 0),
datetime(2000, 5, 13, 0, 0, 0),
datetime(3000, 1, 1, 0, 0, 0)] # just pick a time far in the future here
n_date = len(start_date)
# Set chan_list to lower limit of channel range instead of a string with dashes...
chan_list = np.loadtxt(chan_table_file, dtype=str, skiprows=13, usecols=(0,))
#chan_list = np.append(chan_list, 256)
for i_chan in range(len(chan_list)):
n_dash = chan_list[i_chan].count('-')
if(n_dash > 0):
chan_list[i_chan] = chan_list[i_chan][0:chan_list[i_chan].index('-')]
chan_list = np.array(map(int, chan_list))
# Now fill in 'in between' indices and map them to energy index.
energy_ind = []
for i_chan in range(len(chan_list)): # Not counting the last element, in which case I will append, not insert
if(i_chan < len(chan_list)-1):
n_diff = chan_list[i_chan+1] - chan_list[i_chan]
else:
n_diff = 256 - chan_list[len(chan_list) - 1]
for i_ind in range(n_diff):
energy_ind.append(i_chan)
energy_ind = np.array(energy_ind)
# print 'n_energy = ', len(energy_ind)
# print 'energy_ind = ', energy_ind
# add a final number on the end here for ease of testing ranges
#print 'n_chan = ', len(chan_list)
#print 'n_chan = ', len(chan_list)
#print 'CHAN_LIST = ', chan_list
# Now read in table of energies, which depend on date the datra was taken
chan_table = np.loadtxt(chan_table_file, skiprows=13, usecols=(2,3,4,5,6,7))
# Channel table is now indexed as chan_table[pha_range][date/pcuid]
# Need to differentiate between PCUOD = 0 OR > 0 for start/stop_date[4]
n_event = len(ph_mjd)
## ph_chan_new = np.zeros_like(ph_chan)
# Map ph_chan input (0-255) to a row number from table (0-128) using chan_list extracted above
## for i_event in range(n_event):
# for i_chan in range(len(chan_list)):
## i_chan = 0
## while((i_chan < len(chan_list)-1) and (not (chan_list[i_chan] <= ph_chan[i_event] < chan_list[i_chan+1]))):
#print 'i_chan = ', i_chan
## i_chan += 1
## if(i_chan >= len(chan_list)):
## print 'Warning: PHA value at MJD {0:10.4f} is {1:d}'.format(ph_mjd, ph_chan)
## print ' Setting to -1.'
## ph_chan_new[i_event] = -1
## else:
## ph_chan_new[i_event] = i_chan
# Make array of datetime-format dates from MJDs
ph_datetime = mjd.mjdtodate(ph_mjd)
ph_chan_col = np.zeros_like(ph_datetime) - 1 # initialize to -1
for i_date in range(n_date):
if(i_date < 4):
match_ind = np.where((ph_datetime > start_date[i_date]) & (ph_datetime < stop_date[i_date]))
ph_chan_col[match_ind] = i_date
else: # to accoutn for final date split between PCUID 0 vs 1/2/3/4
match_ind = np.where((ph_datetime > start_date[i_date]) & (ph_datetime < stop_date[i_date]) & (ph_pcuid == 0))
ph_chan_col[match_ind] = 4
match_ind = np.where((ph_datetime > start_date[i_date]) & (ph_datetime < stop_date[i_date]) & (ph_pcuid > 0))
ph_chan_col[match_ind] = 5
# Read off energies from table based on PHA channel keyword and date index.
# Energies in table are EMAX, so EMIN for a given channel is given as the energy corresponding to channel-1
# If channel = 0, then EMIN = 0.
ph_emin = np.array([chan_table[energy_ind[ph_chan[i_event]]-1][ph_chan_col[i_event]] if(energy_ind[ph_chan[i_event]]>0) else 0. for i_event in range(n_event)])
ph_emin[ph_chan_col < 0] = -1.
ph_emax = np.array([chan_table[energy_ind[ph_chan[i_event]]][ph_chan_col[i_event]] for i_event in range(n_event)])
ph_emax[ph_chan_col < 0] = -1.
ph_erange = np.array([(ph_emin[i_event], ph_emax[i_event]) for i_event in range(n_event)])
if(return_chans==True):
return ph_erange, ph_chan, energy_ind[ph_chan]
else:
return ph_erange