def render(self, context):
qs = context[self.var_name]
P = [c.p_bary for c in qs.all() if c.best_dm > 0]
DM = [c.best_dm for c in qs.all() if c.best_dm > 0]
REDCHISQ = [c.reduced_chi_sq for c in qs.all() if c.best_dm > 0]
LINKS = [reverse('bestprof_detail', args=[c.pk])
for c in qs.all() if c.best_dm > 0]
RA = [c.ra_deg for c in qs.all() if c.best_dm > 0]
DEC = [c.dec_deg for c in qs.all() if c.best_dm > 0]
cv = SVGCanvas(940, 550, background_color='white')
if P:
lo_dm = min(DM)
max_dm = max(DM)
pc = PlotContainer(0, -20, 880, 550, color='black', x_log=True,
y_log=True, data_background_color='gray')
gr = RGBGradient((lo_dm, max_dm), (0, 0, 1), (1, 0, 0))
scp = ScatterPlotter(P, REDCHISQ, RA, DEC, DM, gradient=gr,
gradient_i=4, links=LINKS,
symbol=RADECSymbol)
pc.add_plotter(scp)
# pc.top.hide_tickmarklabels()
pc.top.hide_label()
# pc.right.hide_tickmarklabels()
pc.right.hide_label()
pc.left.set_label('Reduced Chi Square')
pc.bottom.set_label('Period (ms)')
cv.add_plot_container(pc)
# Gradient:
pc = PlotContainer(820, -20, 120, 550, color='black', data_padding=0)
pc.top.hide_label()
pc.top.hide_tickmarks()
pc.left.hide_tickmarks()
pc.bottom.hide_label()
pc.bottom.hide_tickmarks()
pc.left.hide_label()
pc.right.set_label('Dispersion Measure cm^-3 pc')
pc.add_plotter(GradientPlotter(gr))
cv.add_plot_container(pc)
# write number of candidates shown:
tf = TextFragment(50, 520,
'(Showing %d candidates.)' % len(P),
color='black', font_size=15)
cv.add_plot_container(tf)
else:
tf = TextFragment(200, 200, 'No Candidates found.', color='red',
font_size=50)
cv.add_plot_container(tf)
tmp = StringIO.StringIO()
cv.draw(tmp)
return tmp.getvalue()
def render(self, context):
qs = context[self.var_name]
tmp = StringIO.StringIO()
P = [c.p_bary for c in qs.all() if c.p_bary > 0]
cv = SVGCanvas(940, 550, background_color='white')
if P:
binned = bin_data_log(P, 200)
pc = PlotContainer(0, -20, 950, 550, color='black', x_log=True)
pc.bottom.set_label('Period (ms)')
pc.top.hide_label()
pc.left.set_label('Count')
pc.right.hide_label()
hp = HistogramPlotter(binned, color='black')
pc.add_plotter(hp)
cv.add_plot_container(pc)
# write number of candidates shown:
tf = TextFragment(50, 520, '(Showing %d candidates.)' %
len(P), color='black', font_size=15)
cv.add_plot_container(tf)
else:
tf = TextFragment(200, 200, 'No Candidates found.', color='red',
font_size=50)
cv.add_plot_container(tf)
tmp = StringIO.StringIO()
cv.draw(tmp)
return tmp.getvalue()
def render(self, context):
qs = context[self.var_name]
tmp = StringIO.StringIO()
P = [c.p_bary for c in qs.all() if c.p_bary > 0]
cv = SVGCanvas(940, 550, background_color='white')
if P:
binned = bin_data_log(P, 200)
pc = PlotContainer(0, -20, 950, 550, color='black', x_log=True)
pc.bottom.set_label('Period (ms)')
pc.top.hide_label()
pc.left.set_label('Count')
pc.right.hide_label()
hp = HistogramPlotter(binned, color='black')
pc.add_plotter(hp)
cv.add_plot_container(pc)
# write number of candidates shown:
tf = TextFragment(50,
520,
'(Showing %d candidates.)' % len(P),
color='black',
font_size=15)
cv.add_plot_container(tf)
else:
tf = TextFragment(200,
200,
'No Candidates found.',
color='red',
font_size=50)
cv.add_plot_container(tf)
tmp = StringIO.StringIO()
cv.draw(tmp)
return tmp.getvalue()
output_files = check_directories(options, args, p)
for plot_i, searchoutdir, fn in output_files:
print plot_i, searchoutdir, fn
# Do some setting up and calculations:
cv = SVGCanvas(1250, 760)
try:
print 'Scanning for .singlepulse and .inf files ...'
spr = SinglePulseReaderCondensed(searchoutdir, options.s, options.e,
delays_file, options.lo, options.hi)
print '... done'
except:
raise
datapath = os.path.abspath(searchoutdir)
msg = 'Problem with data in %s, nothing present?' % datapath
cv.add_plot_container(TextFragment(100, 100, msg, font_size=15))
else:
print 'Number of DM-trials: %d' % len(spr.dms)
tf = TextFragment(870, 600, spr.sp_map[spr.dms[0]][:-12])
cv.add_plot_container(tf)
min_dmi, max_dmi = get_dmi_range(spr, options.dmspercell)
# for main, count detections (or do SNR calculation per cell):
ar = count_detections(spr, options.dmspercell, max_dmi, options.s,
options.e, options.secondspercell,
options.type)
# set up the gradient:
if options.type == 'N':
m, M = 1, 30
elif options.type == 'M':
m, M = 5, 30
c.add_plotter(XLimitPlotter(300))
c.add_plotter(YLimitPlotter(300))
c.add_plotter(CrossHairPlotter(1e+6, 1e+6))
c.add_plotter(
ScatterPlotter([1000], [1000], symbol=CrossHairSymbol, color='lime'))
c.add_plotter(LinkBox((100, 100, 10000, 10000), 'http://www.slashdot.org'))
c.left.set_interval(stretch_interval((10, 1000), 1.2, True), log=False)
c.right.set_interval(stretch_interval((1000, 10), 1.2, True), log=False)
c.bottom.set_interval((-100, 100), False)
c.left.label_link('http://slashdot.org')
c.right.label_link('http://slashdot.org')
c.top.label_link('http://slashdot.org')
c.bottom.label_link('http://slashdot.org')
c.top.set_label('OH JOY!')
cv.add_plot_container(c)
# Histogram testing:
c = PlotContainer(100,
500,
600,
400,
background_color="white",
x_log=False,
y_log=False,
data_background_color='yellow')
cp = LegendPlotter(position=UNDER_PLOT)
colors = ['green', 'blue', 'red', 'gray', 'yellow', 'orange', 'lime']
lps = ['6 2', '2 2', '4 4', '2 2 8 2', '']
for ii in range(2):
c.add_plotter(CrossHairPlotter(200, 700, color="red"))
c.add_plotter(XLimitPlotter(300))
c.add_plotter(YLimitPlotter(300))
c.add_plotter(CrossHairPlotter(1e6, 1e6))
c.add_plotter(ScatterPlotter([1000], [1000], symbol=CrossHairSymbol, color="lime"))
c.add_plotter(LinkBox((100, 100, 10000, 10000), "http://www.slashdot.org"))
c.left.set_interval(stretch_interval((10, 1000), 1.2, True), log=False)
c.right.set_interval(stretch_interval((1000, 10), 1.2, True), log=False)
c.bottom.set_interval((-100, 100), False)
c.left.label_link("http://slashdot.org")
c.right.label_link("http://slashdot.org")
c.top.label_link("http://slashdot.org")
c.bottom.label_link("http://slashdot.org")
c.top.set_label("OH JOY!")
cv.add_plot_container(c)
# Histogram testing:
c = PlotContainer(
100, 500, 600, 400, background_color="white", x_log=False, y_log=False, data_background_color="yellow"
)
cp = LegendPlotter(position=UNDER_PLOT)
colors = ["green", "blue", "red", "gray", "yellow", "orange", "lime"]
lps = ["6 2", "2 2", "4 4", "2 2 8 2", ""]
for ii in range(2):
data = []
for i in range(10000):
data.append(random.randrange(100))
bd = bin_data(data, 100)
c.add_plotter(HistogramPlotter(bd, color="black", linepattern=lps[ii % len(lps)]))
def render(self, context):
qs = context[self.var_name]
tmp = StringIO.StringIO()
cv = SVGCanvas(940, 550, background_color='white')
P = [c.p_bary for c in qs.all() if c.best_dm > 0]
DM = [c.best_dm for c in qs.all() if c.best_dm > 0]
REDCHISQ = [c.reduced_chi_sq for c in qs.all() if c.best_dm > 0]
LINKS = [
reverse('bestprof_detail', args=[c.pk]) for c in qs.all()
if c.best_dm > 0
]
RA = [c.ra_deg for c in qs.all() if c.best_dm > 0]
DEC = [c.dec_deg for c in qs.all() if c.best_dm > 0]
if P:
max_redchisq = max(REDCHISQ)
min_redchisq = min(REDCHISQ)
# Main panel showing candidate period-DM scatter plot:
pc = PlotContainer(0,
-20,
880,
550,
color='black',
x_log=True,
y_log=True,
data_background_color='gray')
pc.bottom.set_label('Period (ms)')
pc.top.hide_label()
pc.left.set_label('Dispersion Measure (cm^-3 pc)')
pc.right.hide_label()
gr = RGBGradient((min_redchisq, max_redchisq), (0, 0, 1),
(1, 0, 0))
scp = ScatterPlotter(P,
DM,
RA,
DEC,
REDCHISQ,
gradient=gr,
gradient_i=4,
links=LINKS,
symbol=RADECSymbol)
pc.add_plotter(scp)
cv.add_plot_container(pc)
# Gradient:
pc = PlotContainer(820,
-20,
120,
550,
color='black',
data_padding=0)
pc.top.hide_label()
pc.top.hide_tickmarks()
pc.left.hide_tickmarks()
pc.bottom.hide_label()
pc.bottom.hide_tickmarks()
pc.left.hide_label()
pc.right.set_label('Candidate Reduced Chi-Square')
pc.add_plotter(GradientPlotter(copy.deepcopy(scp.gradient)))
cv.add_plot_container(pc)
# write number of candidates shown:
tf = TextFragment(50,
520,
'(Showing %d candidates.)' % len(P),
color='black',
font_size=15)
cv.add_plot_container(tf)
else:
tf = TextFragment(200,
200,
'No Candidates found.',
color='red',
font_size=50)
cv.add_plot_container(tf)
cv.draw(tmp)
return tmp.getvalue()
