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()
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 plot(filename, spr, options, pulse_trains, pt_idx, rejected_pulses,
nlink, plink, marker_dms):
'''
Create a pulsetrain diagnostic plot.
Note: Call this function to create the plot, not others in this module.
'''
# Find DM range for this plot:
lodm, hidm = find_dm_range(spr.dms, pulse_trains[pt_idx])
# Flatten pulses into list of candidates (foreground, background):
foreground = flatten_pulses(pulse_trains[pt_idx], lodm, hidm)
background = flatten_pulses(rejected_pulses, lodm, hidm)
for i in range(len(pulse_trains)):
if i != pt_idx:
background.extend(flatten_pulses(pulse_trains[i], lodm, hidm))
# Determine whether it is wise to switch to rasterized plotting (called
# fallback).
n_candidates = len(foreground) + len(background)
if n_candidates > MAX_N_CANDIDATES:
fallback = True
else:
fallback = False
# Set up the overall canvas to wich all panels are plot:
cv = SVGCanvas(1250, 750)
# Add the three panels to the plot:
plot_main_panel(cv, foreground, background, lodm, hidm, marker_dms,
fallback)
plot_snr_scatter(cv, foreground, background, lodm, hidm, marker_dms,
fallback)
plot_count_histogram(cv, spr.dms, foreground, background, lodm, hidm,
marker_dms)
# Add the text identifying the data set etc. to the diagnostic plot:
add_text(cv, spr.md_map[spr.dms[0]], nlink, plink, n_candidates, fallback)
add_settings_text(cv, options)
add_trigger_text(cv, pulse_trains, pt_idx)
# Finally write everything out to an SVG (xml) file.
with open(filename, 'w') as f:
cv.draw(f)
pc_gr.top.hide_label()
pc_gr.bottom.set_label(TYPE2TITLE[options.type])
pc_gr.add_plotter(grp)
cv.add_plot_container(pc_gr)
# Text (data set, next previous, etc...)
if len(output_files) > 1:
if plot_i < len(output_files) - 1:
tf = TextFragment(870, 580, 'Next', color='blue',
link=output_files[plot_i + 1][2])
cv.add_plot_container(tf)
if plot_i > 0:
tf = TextFragment(920, 580, 'Previous', color='blue',
link=output_files[plot_i - 1][2])
cv.add_plot_container(tf)
elif options.uselinkplaceholder:
print 'Using link placeholders'
cv.add_plot_container(TextFragment(870, 580, 'Home',
color='blue',
link='HOME_PLACEHOLDER'))
cv.add_plot_container(TextFragment(1020, 580, 'Next',
color='blue',
link='NEXT_PLACEHOLDER'))
cv.add_plot_container(TextFragment(1070, 580, 'Previous',
color='blue',
link='PREVIOUS_PLACEHOLDER'))
with open(fn, 'w') as f:
cv.draw(f)
print spr.n_error, spr.n_success
c.top.hide_all()
c.bottom.hide_all()
c.left.hide_label()
c.left.hide_tickmarklabels()
c.right.set_label('Significance (sigma)')
c.add_plotter(GradientPlotter(gr2))
cv.add_plot_container(c)
# ------------------------------------------------------
# -- For debugging raster fallback ---------------------
# Note: this also tests the 'new' shortcuts on SVGCanvas and PlotContainer
# The raster fallback might still have some off-by-one problems.
gr = RGBGradient((0, 25), (0, 0, 1), (1, 0, 0))
gr2 = RGBGradient((0, 25), (1, 0, 0), (0, 0, 1))
c = PlotContainer(100, 1900, 600, 400)
xdata = list(range(25))
c.add(ScatterPlotter(xdata, symbols=[RasterDebugSymbol]), True)
c.add(ScatterPlotter(xdata, symbols=[RasterDebugSymbol]))
xdata.reverse()
colors = ['yellow' for x in xdata]
c.add(ScatterPlotter(xdata, symbols=[BaseSymbol], colors=colors))
c.add(ScatterPlotter(xdata, gradient=gr, gradient_i=0), True)
cv.add(c)
#
# ------------------------------------------------------
cv.draw(sys.stdout)
c.top.hide_all()
c.bottom.hide_all()
c.left.hide_label()
c.left.hide_tickmarklabels()
c.right.set_label("Significance (sigma)")
c.add_plotter(GradientPlotter(gr2))
cv.add_plot_container(c)
# ------------------------------------------------------
# -- For debugging raster fallback ---------------------
# Note: this also tests the 'new' shortcuts on SVGCanvas and PlotContainer
# The raster fallback might still have some off-by-one problems.
gr = RGBGradient((0, 25), (0, 0, 1), (1, 0, 0))
gr2 = RGBGradient((0, 25), (1, 0, 0), (0, 0, 1))
c = PlotContainer(100, 1900, 600, 400)
xdata = list(range(25))
c.add(ScatterPlotter(xdata, symbols=[RasterDebugSymbol]), True)
c.add(ScatterPlotter(xdata, symbols=[RasterDebugSymbol]))
xdata.reverse()
colors = ["yellow" for x in xdata]
c.add(ScatterPlotter(xdata, symbols=[BaseSymbol], colors=colors))
c.add(ScatterPlotter(xdata, gradient=gr, gradient_i=0), True)
cv.add(c)
#
# ------------------------------------------------------
cv.draw(sys.stdout)
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()
'%.2f ms' % bpf.header.p_bary[0], alignment='start',
font_size=TEXTSIZE))
cv.add_plot_container(TextFragment(x + width,
y + height + 2 * TEXTSIZE, '%.3f pc cm^-3' % bpf.header.best_dm,
alignment='end', font_size=TEXTSIZE))
files = os.listdir(sys.argv[1])
tmp = []
for f in files:
if f.endswith('.bestprof'):
try:
bpf = BestprofFile(os.path.join(sys.argv[1], f))
except:
print 'There was a problem reading %s' % os.path.join(sys.argv[1], f)
else:
tmp.append(bpf)
tmp.sort(key=lambda x: x.header.reduced_chi_sq)
tmp.reverse()
cv = SVGCanvas(1140, 1240)
N_HORIZONTAL = 5
N_VERTICAL = 10
for ix in range(N_HORIZONTAL):
for iy in range(N_VERTICAL):
try:
plot_profile(cv, 220 * ix + 20, 140 * iy + 20, 200, 100, tmp[ix + N_HORIZONTAL * iy], psr_name='B0000+0000')
except IndexError, e:
pass
cv.draw('test234.xml')
files = os.listdir(sys.argv[1])
tmp = []
for f in files:
if f.endswith('.bestprof'):
try:
bpf = BestprofFile(os.path.join(sys.argv[1], f))
except:
print 'There was a problem reading %s' % os.path.join(
sys.argv[1], f)
else:
tmp.append(bpf)
tmp.sort(key=lambda x: x.header.reduced_chi_sq)
tmp.reverse()
cv = SVGCanvas(1140, 1240)
N_HORIZONTAL = 5
N_VERTICAL = 10
for ix in range(N_HORIZONTAL):
for iy in range(N_VERTICAL):
try:
plot_profile(cv,
220 * ix + 20,
140 * iy + 20,
200,
100,
tmp[ix + N_HORIZONTAL * iy],
psr_name='B0000+0000')
except IndexError, e:
pass
cv.draw('test234.xml')
