def read_data(file):
"""
read data and convert time in the fractional year
input: file --- file name
output: time --- time in fractional year
val --- corner pix centroid mean
mid --- id of the data point. 0: faint, 1: afaint, 2: vfaint
"""
f = open(file, 'r')
data = [line.strip() for line in f.readlines()]
f.close()
time = []
val = []
mid = []
for ent in data:
atemp = re.split('\s+', ent)
#
#--- convert time in fractional year
#
ytime = tcnv.sectoFracYear(float(atemp[0]))
time.append(ytime)
try:
val.append(float(atemp[2]))
if atemp[5] == 'AFAINT':
mid.append(1)
elif atemp[5] == 'VFAINT':
mid.append(2)
else:
mid.append(0)
except:
pass
return [time, val, mid]
def hrc_gain_trend_plot():
"""
create time trend of Gaussian profiles fit on HRC PHA data. It also create trend along radial distnace for each year
Input: none but the data is read from <house_keeping>/fitting_results
Outut: time trend plots in <plot_dir> / hrc_i_time_trend.png hrc_s_time_trend.png
radial_distance plots hrc_i_radial_dist_year<year>.png
"""
file = house_keeping + 'fitting_results'
f = open(file, 'r')
data = [line.strip() for line in f.readlines()]
f.close()
#
#--- set a few things such as labeling
#
yMinSets = [0, 0, 20]
yMaxSets = [250, 250, 100]
yname = 'PHA'
entLabels = ["PHA Median", "PHA Voigt Peak Position", "PHA FWHM"]
#
#--- time trend plots
#
xmin = 1999
ctemp = tcnv.currentTime() #--- finding this year
xmax = end_year = int(ctemp[0]) + 1
xname = 'Time (Year)'
#
#--- HRC I
#
time_i = []
dist_i = []
med_i = []
center_i = []
width_i = []
time_s = []
dist_s = []
med_s = []
center_s = []
width_s = []
for ent in data:
if ent[0] == '#': #--- avoid comments
continue
atemp = re.split('\t+', ent)
#
#--- if the width is < 1 or amp is smaller than 5, probably it did not find a correct source position
#
if float(atemp[12]) < 1:
continue
if float(atemp[11]) < 5:
continue
fyear = tcnv.sectoFracYear(float(atemp[2]))
if atemp[3] == 'HRC-I':
time_i.append(fyear)
dist_i.append(float(atemp[8]))
med_i.append(float(atemp[9]))
center_i.append(float(atemp[10]))
width_i.append(float(atemp[12]))
else:
time_s.append(fyear)
dist_s.append(float(atemp[8]))
med_s.append(float(atemp[9]))
center_s.append(float(atemp[10]))
width_s.append(float(atemp[12]))
xSets = [time_i, time_i, time_i]
ySets = [med_i, center_i, width_i]
dist = dist_indexing(dist_i)
plotPanel(xmin, xmax, yMinSets, yMaxSets, xSets, ySets, dist, xname, yname,
entLabels)
cmd = 'mv out.png ' + web_dir + 'Trend_plots/hrc_i_time_trend.png'
os.system(cmd)
#
#--- HRC S
#
xSets = [time_s, time_s, time_s]
ySets = [med_s, center_s, width_s]
dist = dist_indexing(dist_s)
plotPanel(xmin, xmax, yMinSets, yMaxSets, xSets, ySets, dist, xname, yname,
entLabels)
cmd = 'mv out.png ' + web_dir + 'Trend_plots/hrc_s_time_trend.png'
os.system(cmd)
#
#--- trend along radial distance
#
xmin = 0
xmax = 16
xname = 'Radial Distance(Arcsec)'
#
#--- HRC I
#
for pyear in range(1999, end_year):
dist = []
med = []
center = []
width = []
mark = []
pend = pyear + 1
for j in range(0, len(time_i)):
if (time_i[j] >= pyear) and (time_i[j] < pend):
dist.append(dist_i[j])
med.append(med_i[j])
center.append(center_i[j])
width.append(width_i[j])
xSets = [dist, dist, dist]
ySets = [med, center, width]
plotPanel(xmin,
xmax,
yMinSets,
yMaxSets,
xSets,
ySets,
mark,
xname,
yname,
entLabels,
extraNote=pyear)
outname = web_dir + '/Trend_plots/hrc_i_radial_dist_year' + str(
pyear) + '.png'
cmd = 'mv out.png ' + outname
os.system(cmd)
#
#--- HRC S
#
for pyear in range(1999, end_year):
dist = []
med = []
center = []
width = []
mark = []
pend = pyear + 1
for j in range(0, len(time_s)):
if (time_s[j] >= pyear) and (time_s[j] < pend):
dist.append(dist_s[j])
med.append(med_s[j])
center.append(center_s[j])
width.append(width_s[j])
xSets = [dist, dist, dist]
ySets = [med, center, width]
plotPanel(xmin,
xmax,
yMinSets,
yMaxSets,
xSets,
ySets,
mark,
xname,
yname,
entLabels,
extraNote=pyear)
outname = web_dir + '/Trend_plots/hrc_s_radial_dist_year' + str(
pyear) + '.png'
cmd = 'mv out.png ' + outname
os.system(cmd)
def create_sim_temp_plots(start, stop):
"""
control function to run sim temperature trending plots etc
input: start --- starting year
stop --- stopping year
if they are the same, it will cerate the plot for the year
else, it will create the entire range (from 1999 to current)
output: plots in <web_dir>/Plots/
sim_temp_<year>.png
sim)translation_<year>.png
"""
#
#--- check whether to create full range or indivisual year plots
#
if stop - start > 1:
fname = web_dir + 'Plots/sim_temp_fullrange.png'
oname = web_dir + 'Plots/sim_translation_fullrange.png'
pname = web_dir + 'Plots/pitchangle_fullrange.png'
title = str(start) + ' - ' + str(stop-1)
yind = 0
else:
fname = web_dir + 'Plots/sim_temp_' + str(start) + '.png'
oname = web_dir + 'Plots/sim_translation_' + str(start) + '.png'
pname = web_dir + 'Plots/pitchangle_' + str(start) + '.png'
title = 'Year: ' + str(start)
yind = 1
if tcnv.isLeapYear(start) == 1:
base = 366
else:
base = 365
#
#--- read sim temperature data file
#
file = data_dir + 'tsc_temps.txt'
f = open(file, 'r')
data = [line.strip() for line in f.readlines()]
f.close()
x = []
ts = []
te = []
delta = []
steps = []
for ent in data[1:]:
atemp = re.split('\s+', ent)
try:
time = convert_to_fyear(atemp[0])
if time < start:
continue
if time > stop:
break
if yind == 1:
time = (time - start) * base
t1 = float(atemp[2])
t2 = float(atemp[3])
t3 = t2 - t1
t4 = float(atemp[5])
if t1 > 60 or t1 < -40:
continue
if t2 > 60 or t2 < -40:
continue
x.append(time) #--- time in year or yday
ts.append(t1) #--- sim temperature at the beginning
te.append(t2) #--- sim temperature at the ending
delta.append(t3) #--- the sim temperature difference
steps.append(t4) #--- the number of steps
except:
continue
#
#--- run moving average
#
mvstep = 30
[mxs, mys] = smooth_data(x, ts, mvstep)
if len(mxs) == 0:
skip = 1
else:
skip = 0
[mxe, mye] = smooth_data(x, te, mvstep)
if len(mxe) == 0:
skip = 1
else:
skip = 0
#
#--- read pitch angle data
#
file = '/data/mta/DataSeeker/data/repository/orb_angle.rdb'
f = open(file, 'r')
data = [line.strip() for line in f.readlines()]
f.close()
stime = []
angle = []
m = 0
for ent in data[2:]:
atemp = re.split('\s+', ent)
try:
time = tcnv.sectoFracYear(float(atemp[0]))
if time < start:
continue
if time > stop:
break
if yind == 1:
time = (time - start) * base
#
#--- just take the data every 20 mins or so (data is taken every 300 sec)
#
if m % 4 == 0:
stime.append(time)
angle.append(float(atemp[1]))
m += 1
except:
continue
#
#--- if yind == 0, full range, otherwise, year plot
#
if yind == 0:
xmin = start
xmax = stop
tdff = 1.0e-5
else:
xmin = 1
xmax = base
tdff = 3.5e-3
#
#--- match delta and angle --- not used
#
## angle2 = []
## js = 0
## for k in range(0, len(x)):
## angle2.append(0)
## for j in range(js, len(stime)):
## if x[k] < stime[j]:
## angle2[k] = angle[j]
## break;
## else:
## if j >= len(stime) -1:
## angle2[k] = angle[-1]
## break;
## if x[k] >= stime[j] and x[k] <= stime[j+1]:
## angle2[k] = angle[j]
## js = j
## break
## else:
## continue
#
#--- plot time tred of sim temperature
#
plot_sim_temp_data(xmin, xmax, x, ts, te, mxs, mys, mxe, mye, delta, stime, angle, fname, yind, title, skip)
#
#--- plot translation step - delta sim temperature
#
plot_step_delta(steps, delta, oname, title)
def hrc_gain_trend_plot():
"""
create time trend of Gaussian profiles fit on HRC PHA data. It also create trend along
radial distnace for each year
input: none but the data is read from <house_keeping>/fitting_results
outut: time trend plots in <plot_dir> / hrc_i_time_trend.png hrc_s_time_trend.png
radial_distance plots hrc_i_radial_dist_year<year>.png
"""
#
#--- time trend plots
#
xmin = 1999
ctemp = tcnv.currentTime() #--- finding this year
end_year = int(ctemp[0]) + 1 #--- set end of this year
xmax = end_year
xname = 'Time (Year)'
#
#--- read voigt profile fitting to the pha distribution of each data from the table.
#
ifile = data_dir + 'fitting_results'
f = open(ifile, 'r')
data = [line.strip() for line in f.readlines()]
f.close()
obsid_i = []
time_i = []
dist_i = []
med_i = []
center_i = []
width_i = []
obsid_s = []
time_s = []
dist_s = []
med_s = []
center_s = []
width_s = []
for ent in data:
if ent[0] == '#': #--- avoid comments
continue
atemp = re.split('\t+', ent)
#
#--- if the width is < 1 or amp is smaller than 5, probably it did not find a correct source position
#
if float(atemp[12]) < 1:
continue
if float(atemp[11]) < 5:
continue
#
#--- separate the data to hrc i and hrc s
#
fyear = tcnv.sectoFracYear(float(atemp[2]))
if atemp[3] == 'HRC-I':
obsid_i.append(atemp[0])
time_i.append(fyear)
dist_i.append(float(atemp[8]))
med_i.append(float(atemp[9]))
center_i.append(float(atemp[10]))
width_i.append(float(atemp[12]))
else:
obsid_s.append(atemp[0])
time_s.append(fyear)
dist_s.append(float(atemp[8]))
med_s.append(float(atemp[9]))
center_s.append(float(atemp[10]))
width_s.append(float(atemp[12]))
#
#--- create an interactive plot for HRC I
#
xSets = [time_i, time_i, time_i]
ySets = [med_i, center_i, width_i]
dist = dist_indexing(dist_i)
intlabel = crate_label_html(obsid_i, dist_i)
[fig_hi, fit1_hi, fit2_hi] = plotPanel(xmin, xmax, yMinSets, yMaxSets, \
xSets, ySets, dist, xname, yname, entLabels, intlabel=intlabel, inst = 'hrc_i')
#
#--- create an interactive plot for HRC S
#
xSets = [time_s, time_s, time_s]
ySets = [med_s, center_s, width_s]
dist = dist_indexing(dist_s)
intlabel = crate_label_html(obsid_s, dist_s)
[fig_hs, fit1_hs, fit2_hs] = plotPanel(xmin, xmax, yMinSets, yMaxSets, \
xSets, ySets, dist, xname, yname, entLabels, intlabel=intlabel, inst='hrc_s')
#
#---- update the main html page
#
update_html_page(fig_hi, fig_hs, fit1_hi, fit2_hi, fit1_hs, fit2_hs, time_i)
#
#---- create radiao html pages
#
create_radial_html_page(time_i, obsid_i, dist_i, med_i, center_i, width_i, end_year, inst='i')
create_radial_html_page(time_s, obsid_s, dist_s, med_s, center_s, width_s, end_year, inst='s')
#
#--- create energy gain fitting result table page
#
gain_fitting_table()
def plot_dead_time_trend():
"""
update hrc_i_115_dtf_plot.png plot file
input: none, but read from hrc_i_115_stat_results
output: <plot dir>/hrc_i_115_dtf_plot.png
"""
#
#--- read data
#
dfile = data_dir + 'Stats/hrc_i_115_stat_results'
f = open(dfile, 'r')
data = [line.strip() for line in f.readlines()]
f.close()
time = []
cnt_rate = []
v_rate = []
dtf = []
#
#--- computer dead time correciton fuctor
#
for ent in data:
atemp = re.split('\s+', ent)
date = tcnv.sectoFracYear(float(atemp[1]))
a_rate = float(atemp[5]) / float(atemp[13])
time.append(date)
cnt_rate.append(float(atemp[5]))
v_rate.append(float(atemp[13]))
dtf.append(a_rate)
#
#--- setting a plotting surface
#
prep_plot()
mpl.rcParams['font.size'] = 11
props = font_manager.FontProperties(size=11)
psize = 8
tfsize = 12
#
#--- start plotting
#
xmin = 1999
xmax = int(max(time)) + 1
#
#--- Count Rate per Sec
#
a1 = plt.subplot(311)
ymin = 3.45
ymax = 3.55
title = 'Count Rate per Sec'
plt.axis([xmin, xmax, ymin, ymax])
plt.plot(time, cnt_rate, color='blue', lw=0, marker='+', markersize=psize)
plt.text(2000, 3.538, title, size=tfsize, weight='bold')
#
#--- Valid Count Rate per Sec
#
a2 = plt.subplot(312)
ymin = 0
ymax = 200
title = 'Valid Count Rate per Sec'
plt.axis([xmin, xmax, ymin, ymax])
plt.plot(time, v_rate, color='blue', lw=0, marker='+', markersize=psize)
plt.text(2000, 160, title, size=tfsize, weight='bold')
#
#--- Dead Time Correction Fator
#
a3 = plt.subplot(313)
ymin = 0
ymax = 0.1
title = 'Dead Time Correctin Factor'
plt.axis([xmin, xmax, ymin, ymax])
plt.plot(time, dtf, color='blue', lw=0, marker='+', markersize=psize)
plt.text(2000, 0.02, title, size=tfsize, weight='bold')
#
#--- add x ticks label only on the panels of the last row
#
plt.setp(a1.get_xticklabels(), visible=False)
plt.setp(a2.get_xticklabels(), visible=False)
a3.set_xlabel("Time (Year)", size=11)
#
#--- save the plot in file
#
outname = 'hrc_i_115_dtf_plot.png'
outname = plot_dir + outname
create_plot_file(outname)
def read_stat_data(infile):
"""
read a stat data file and return a list of lists of data
input: infile --- input file name (with a full path)
output: 0: time --- time in year date
1: tstart --- start time in seconds from 1998.1.1
2: tstop --- stop time in seconds from 1998.1.1
3: duration --- duration in seconds
4: total_count --- total counts
5: count_per_sec --- counts per seonds
6: pha_mean --- pha mean
7: pha_median --- pha median
8: pha_sigma --- pha sigma
9: t_mean --- total count rate mean
10: t_median --- total count rate median
11: t_sigma --- total count rate sigma
12: v_mean --- valid count rate mean
13: v_median --- valid count rate median
14: v_sigma --- valid count rate sigma
15: s_mean --- shield count rate mean
16: s_median --- shield count rate median
17: s_sigma --- shield count rate sigma
18: anti_co_mean --- anti conicidnece rate mean
19: anti_co_median --- anti conicidnece rate median
20: anti_co_sigma --- anti conicidnece rate sigma
21: s2hvst --- s2hvst value
22: s2hvlv --- s2jvlv valie
23: scint --- scint
24: scint_std --- scint sigma
"""
data = hcf.read_file_data(infile)
date = []
tstart = []
tstop = []
duration = []
total_count = []
count_per_sec = []
pha_mean = []
pha_median = []
pha_sigma = []
t_mean = []
t_median = []
t_sigma = []
v_mean = []
v_median = []
v_sigma = []
s_mean = []
s_median = []
s_sigma = []
anti_co_mean = []
anti_co_median = []
anti_co_sigma = []
s2hvst = []
s2hvlv = []
scint = []
scint_std = []
time = []
for ent in data:
aval = re.split('\s+', ent)
aval3 = float(aval[3])
#
#--- if the observation interval is shorter than 900 sec, drop the data set.
#--- these data set are not accurate enough.
#
if aval3 < 900:
continue
else:
try:
date.append(aval[0])
start = float(aval[1])
tstart.append(start)
tstop.append(float(aval[2]))
duration.append(aval3)
total_count.append(float(aval[4]))
count_per_sec.append(float(aval[5]))
pha_mean.append(float(aval[6]))
pha_median.append(float(aval[7]))
pha_sigma.append(float(aval[8]))
t_mean.append(float(aval[9]))
t_median.append(float(aval[10]))
t_sigma.append(float(aval[11]))
v_mean.append(float(aval[12]))
v_median.append(float(aval[13]))
v_sigma.append(float(aval[14]))
#
#--- changing a plotting range for an easy view
#
s_mean.append(0.001 * float(aval[15]))
s_median.append(0.001 * float(aval[16]))
s_sigma.append(0.001 * float(aval[17]))
anti_co_mean.append(float(aval[18]))
anti_co_median.append(float(aval[19]))
anti_co_sigma.append(float(aval[20]))
s2hvst.append(int(float(aval[21])))
s2hvlv.append(int(float(aval[22]) + 0.5))
scint.append(float(aval[23]))
scint_std.append(float(aval[24]))
tval = tcnv.sectoFracYear(start)
time.append(tval)
except:
continue
return [time, tstart, tstop, duration, total_count, count_per_sec, \
pha_mean, pha_median, pha_sigma, \
t_mean, t_median, t_sigma, \
v_mean, v_median, v_sigma, \
s_mean, s_median, s_sigma, \
anti_co_mean, anti_co_median, anti_co_sigma, \
s2hvst, s2hvlv, scint, scint_std]
def hrc_gain_trend_plot():
"""
create time trend of Gaussian profiles fit on HRC PHA data. It also create trend along
radial distnace for each year
input: none but the data is read from <house_keeping>/fitting_results
outut: time trend plots in <plot_dir> / hrc_i_time_trend.png hrc_s_time_trend.png
radial_distance plots hrc_i_radial_dist_year<year>.png
"""
#
#--- time trend plots
#
xmin = 1999
ctemp = tcnv.currentTime() #--- finding this year
end_year = int(ctemp[0]) + 1 #--- set end of this year
xmax = end_year
xname = 'Time (Year)'
#
#--- read voigt profile fitting to the pha distribution of each data from the table.
#
ifile = data_dir + 'fitting_results'
f = open(ifile, 'r')
data = [line.strip() for line in f.readlines()]
f.close()
obsid_i = []
time_i = []
dist_i = []
med_i = []
center_i = []
width_i = []
obsid_s = []
time_s = []
dist_s = []
med_s = []
center_s = []
width_s = []
for ent in data:
if ent[0] == '#': #--- avoid comments
continue
atemp = re.split('\t+', ent)
#
#--- if the width is < 1 or amp is smaller than 5, probably it did not find a correct source position
#
if float(atemp[12]) < 1:
continue
if float(atemp[11]) < 5:
continue
#
#--- separate the data to hrc i and hrc s
#
fyear = tcnv.sectoFracYear(float(atemp[2]))
if atemp[3] == 'HRC-I':
obsid_i.append(atemp[0])
time_i.append(fyear)
dist_i.append(float(atemp[8]))
med_i.append(float(atemp[9]))
center_i.append(float(atemp[10]))
width_i.append(float(atemp[12]))
else:
obsid_s.append(atemp[0])
time_s.append(fyear)
dist_s.append(float(atemp[8]))
med_s.append(float(atemp[9]))
center_s.append(float(atemp[10]))
width_s.append(float(atemp[12]))
#
#--- create an interactive plot for HRC I
#
xSets = [time_i, time_i, time_i]
ySets = [med_i, center_i, width_i]
dist = dist_indexing(dist_i)
intlabel = crate_label_html(obsid_i, dist_i)
[fig_hi, fit1_hi, fit2_hi] = plotPanel(xmin, xmax, yMinSets, yMaxSets, \
xSets, ySets, dist, xname, yname, entLabels, intlabel=intlabel, inst = 'hrc_i')
#
#--- create an interactive plot for HRC S
#
xSets = [time_s, time_s, time_s]
ySets = [med_s, center_s, width_s]
dist = dist_indexing(dist_s)
intlabel = crate_label_html(obsid_s, dist_s)
[fig_hs, fit1_hs, fit2_hs] = plotPanel(xmin, xmax, yMinSets, yMaxSets, \
xSets, ySets, dist, xname, yname, entLabels, intlabel=intlabel, inst='hrc_s')
#
#---- update the main html page
#
update_html_page(fig_hi, fig_hs, fit1_hi, fit2_hi, fit1_hs, fit2_hs,
time_i)
#
#---- create radiao html pages
#
create_radial_html_page(time_i,
obsid_i,
dist_i,
med_i,
center_i,
width_i,
end_year,
inst='i')
create_radial_html_page(time_s,
obsid_s,
dist_s,
med_s,
center_s,
width_s,
end_year,
inst='s')
#
#--- create energy gain fitting result table page
#
gain_fitting_table()