def plotnorm(data, circleinds=[], crossinds=[], edgeinds=[], url_path=None, fileroot=None,
tools="hover,tap,pan,box_select,wheel_zoom,reset", plot_width=450, plot_height=400):
""" Make a light-weight norm figure """
fields = ['zs', 'sizes', 'colors', 'abssnr', 'key', 'snrs']
if not circleinds: circleinds = range(len(data['snrs']))
source = ColumnDataSource(data = dict({(key, tuple([value[i] for i in circleinds if i not in edgeinds]))
for (key, value) in data.iteritems() if key in fields}))
norm = Figure(plot_width=plot_width, plot_height=plot_height, toolbar_location="left", x_axis_label='SNR observed',
y_axis_label='SNR expected', tools=tools, webgl=True)
norm.circle('abssnr', 'zs', size='sizes', line_color=None, fill_color='colors', fill_alpha=0.2, source=source)
if crossinds:
sourceneg = ColumnDataSource(data = dict({(key, tuple([value[i] for i in crossinds]))
for (key, value) in data.iteritems() if key in fields}))
norm.cross('abssnr', 'zs', size='sizes', line_color='colors', line_alpha=0.3, source=sourceneg)
if edgeinds:
sourceedge = ColumnDataSource(data = dict({(key, tuple([value[i] for i in edgeinds]))
for (key, value) in data.iteritems() if key in fields}))
norm.circle('abssnr', 'zs', size='sizes', line_color='colors', fill_color='colors', source=sourceedge, line_alpha=0.5, fill_alpha=0.2)
hover = norm.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('SNR', '@snrs'), ('key', '@key')])
if url_path and fileroot:
url = '{}/cands_{}[email protected]'.format(url_path, fileroot)
taptool = norm.select(type=TapTool)
taptool.callback = OpenURL(url=url)
return norm
def plotnoisedist(noisepkl, plot_width=450, plot_height=400):
""" """
# plot noise and flag distributions
scans, segments, noiseperbl, flagfrac, imstd = read_noise(noisepkl)
pl = Figure(plot_width=plot_width,
plot_height=plot_height,
toolbar_location="above",
x_axis_label='Flag fraction',
y_axis_label='Image noise (sys)',
tools='pan, wheel_zoom, reset')
pl.cross(flagfrac, imstd, line_alpha=0.2, color='blue')
# find medians
flagfrac_med = np.median(flagfrac)
imstd_med = np.median(imstd)
logger.info('Median image noise (sys) = {0}'.format(imstd_med))
logger.info('Median flag fraction = {0}'.format(flagfrac_med))
pl.cross(flagfrac_med, imstd_med, line_alpha=1, size=40, color='red')
# estimate number of zero noise images
zeronoisefrac = float(len(np.where(imstd == 0.)[0])) / len(imstd)
logger.info('{0:.0%} of noise images are zeros'.format(zeronoisefrac))
return pl, imstd_med, flagfrac_med
def plotstat(data, circleinds=None, crossinds=None, edgeinds=None, url_path=None, fileroot=None,
tools="hover,tap,pan,box_select,wheel_zoom,reset", plot_width=450, plot_height=400):
""" Make a light-weight stat figure """
fields = ['imkur', 'specstd', 'sizes', 'colors', 'snrs', 'key']
if not circleinds: circleinds = range(len(data['snrs']))
# set ranges
datalen = len(data['dm'])
inds = circleinds + crossinds + edgeinds
specstd = [data['specstd'][i] for i in inds]
specstd_min = min(specstd)
specstd_max = max(specstd)
imkur = [data['imkur'][i] for i in inds]
imkur_min = min(imkur)
imkur_max = max(imkur)
source = ColumnDataSource(data = dict({(key, tuple([value[i] for i in circleinds if i not in edgeinds]))
for (key, value) in data.iteritems() if key in fields}))
stat = Figure(plot_width=plot_width, plot_height=plot_height, toolbar_location="left", x_axis_label='Spectral std',
y_axis_label='Image kurtosis', x_range=(specstd_min, specstd_max),
y_range=(imkur_min, imkur_max), tools=tools, webgl=True)
stat.circle('specstd', 'imkur', size='sizes', line_color=None, fill_color='colors', fill_alpha=0.2, source=source)
if crossinds:
sourceneg = ColumnDataSource(data = dict({(key, tuple([value[i] for i in crossinds]))
for (key, value) in data.iteritems() if key in fields}))
stat.cross('specstd', 'imkur', size='sizes', line_color='colors', line_alpha=0.3, source=sourceneg)
if edgeinds:
sourceedge = ColumnDataSource(data = dict({(key, tuple([value[i] for i in edgeinds]))
for (key, value) in data.iteritems() if key in fields}))
stat.circle('specstd', 'imkur', size='sizes', line_color='colors', fill_color='colors', source=sourceedge, line_alpha=0.5, fill_alpha=0.2)
hover = stat.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('SNR', '@snrs'), ('key', '@key')])
if url_path and fileroot:
url = '{}/cands_{}[email protected]'.format(url_path, fileroot)
taptool = stat.select(type=TapTool)
taptool.callback = OpenURL(url=url)
return stat
def plotdmt(data, circleinds=[], crossinds=[], edgeinds=[], url_path=None, fileroot=None,
tools="hover,tap,pan,box_select,wheel_zoom,reset", plot_width=950, plot_height=500):
""" Make a light-weight dm-time figure """
fields = ['dm', 'time', 'sizes', 'colors', 'snrs', 'key']
if not circleinds: circleinds = range(len(data['snrs']))
# set ranges
datalen = len(data['dm'])
inds = circleinds + crossinds + edgeinds
dm = [data['dm'][i] for i in inds]
dm_min = min(min(dm), max(dm)/1.2)
dm_max = max(max(dm), min(dm)*1.2)
time = [data['time'][i] for i in inds]
time_min = min(time)
time_max = max(time)
source = ColumnDataSource(data = dict({(key, tuple([value[i] for i in circleinds if i not in edgeinds]))
for (key, value) in data.iteritems() if key in fields}))
dmt = Figure(plot_width=plot_width, plot_height=plot_height, toolbar_location="left", x_axis_label='Time (s; relative)',
y_axis_label='DM (pc/cm3)', x_range=(time_min, time_max), y_range=(dm_min, dm_max),
webgl=True, tools=tools)
dmt.circle('time', 'dm', size='sizes', fill_color='colors', line_color=None, fill_alpha=0.2, source=source)
if crossinds:
sourceneg = ColumnDataSource(data = dict({(key, tuple([value[i] for i in crossinds]))
for (key, value) in data.iteritems() if key in fields}))
dmt.cross('time', 'dm', size='sizes', fill_color='colors', line_alpha=0.3, source=sourceneg)
if edgeinds:
sourceedge = ColumnDataSource(data = dict({(key, tuple([value[i] for i in edgeinds]))
for (key, value) in data.iteritems() if key in fields}))
dmt.circle('time', 'dm', size='sizes', line_color='colors', fill_color='colors', line_alpha=0.5, fill_alpha=0.2, source=sourceedge)
hover = dmt.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('SNR', '@snrs'), ('key', '@key')])
if url_path and fileroot:
# url = '{}/cands_{}_sc@scan-seg@seg-i@candint-dm@[email protected]'.format(url_path, fileroot)
url = '{}/cands_{}[email protected]'.format(url_path, fileroot)
taptool = dmt.select(type=TapTool)
taptool.callback = OpenURL(url=url)
return dmt
def plotloc(data, circleinds=[], crossinds=[], edgeinds=[], url_path=None, fileroot=None,
tools="hover,tap,pan,box_select,wheel_zoom,reset", plot_width=450, plot_height=400):
""" Make a light-weight loc figure """
fields = ['l1', 'm1', 'sizes', 'colors', 'snrs', 'key']
if not circleinds: circleinds = range(len(data['snrs']))
# set ranges
datalen = len(data['dm'])
inds = circleinds + crossinds + edgeinds
l1 = [data['l1'][i] for i in inds]
l1_min = min(l1)
l1_max = max(l1)
m1 = [data['m1'][i] for i in inds]
m1_min = min(m1)
m1_max = max(m1)
source = ColumnDataSource(data = dict({(key, tuple([value[i] for i in circleinds if i not in edgeinds]))
for (key, value) in data.iteritems() if key in fields}))
loc = Figure(plot_width=plot_width, plot_height=plot_height, toolbar_location="left", x_axis_label='l1 (rad)', y_axis_label='m1 (rad)',
x_range=(l1_min, l1_max), y_range=(m1_min,m1_max), tools=tools, webgl=True)
loc.circle('l1', 'm1', size='sizes', line_color=None, fill_color='colors', fill_alpha=0.2, source=source)
if crossinds:
sourceneg = ColumnDataSource(data = dict({(key, tuple([value[i] for i in crossinds]))
for (key, value) in data.iteritems() if key in fields}))
loc.cross('l1', 'm1', size='sizes', line_color='colors', line_alpha=0.3, source=sourceneg)
if edgeinds:
sourceedge = ColumnDataSource(data = dict({(key, tuple([value[i] for i in edgeinds]))
for (key, value) in data.iteritems() if key in fields}))
loc.circle('l1', 'm1', size='sizes', line_color='colors', fill_color='colors', source=sourceedge, line_alpha=0.5, fill_alpha=0.2)
hover = loc.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('SNR', '@snrs'), ('key', '@key')])
if url_path and fileroot:
url = '{}/cands_{}[email protected]'.format(url_path, fileroot)
taptool = loc.select(type=TapTool)
taptool.callback = OpenURL(url=url)
return loc
def plotnoisedist(noisepkl, plot_width=450, plot_height=400):
""" """
# plot noise and flag distributions
scans, segments, noiseperbl, flagfrac, imstd = read_noise(noisepkl)
pl = Figure(plot_width=plot_width, plot_height=plot_height, toolbar_location="above",
x_axis_label='Flag fraction', y_axis_label='Image noise (sys)', tools='pan, wheel_zoom, reset')
pl.cross(flagfrac, imstd, line_alpha=0.2, color='blue')
# find medians
flagfrac_med = np.median(flagfrac)
imstd_med = np.median(imstd)
logger.info('Median image noise (sys) = {0}'.format(imstd_med))
logger.info('Median flag fraction = {0}'.format(flagfrac_med))
pl.cross(flagfrac_med, imstd_med, line_alpha=1, size=40, color='red')
# estimate number of zero noise images
zeronoisefrac = float(len(np.where(imstd == 0.)[0]))/len(imstd)
logger.info('{0:.0%} of noise images are zeros'.format(zeronoisefrac))
return pl, imstd_med, flagfrac_med
class pf_graph:
def end_date(self):
now = date.today() + timedelta(hours=4)
return now.strftime("%Y%m%d")
def get_quotes(self):
self.data_slice = slice(-self.data_length, None)
self.pf.asset_ticker = self.ticker
quote = fn.get_quotes_finam(self.ticker,
start_date=self.from_date,
end_date=self.end_date(),
period=self.quote_period,
verbose=False,
timeout=30)
self.quote = quote[self.data_slice]
#print(self.quote.tail())
#self.quote.to_csv('/home/jupyter/bokeh/pf/quotes_{}'.format(self.quote_period))
def update_quotes(self):
quote = self.quote
last_day = quote.ix[-1].name
last_day_begin = datetime(last_day.year, last_day.month, last_day.day)
quote_base = quote.ix[:last_day_begin + timedelta(seconds=-1)]
last_day_str = last_day_begin.strftime('%Y%m%d')
#while True:
#print('get quotes')
try:
quote = fn.get_quotes_finam(self.ticker,
start_date=last_day_str,
end_date=self.end_date(),
period=self.quote_period,
verbose=False,
timeout=5)
except timeout as e:
print('get quotes: timeout, period={}!'.format(self.quote_period))
self.text.value = 'timeout!'
return
except:
self.text.value = 'get quote error!'
return
#print('obtain quotes')
self.quote = quote_base.append(quote)[self.data_slice]
time_str = quote.ix[-1].name.strftime('%Y-%m-%d %H:%M')
if self.text_marker == len(markers) - 1:
self.text_marker = 0
else:
self.text_marker += 1
self.text.value = '{} [{}] {}'.format(
markers[self.text_marker], time_str,
quote.ix[-1][self.ticker + '_Close'])
#time.sleep(1)
def __init__(self, quote_period, box_size, graph_name, candles=30):
# Set up data
self.graph_counter = 0
self.data_length = candles
self.data_slice = slice(-self.data_length, None)
self.quote_period = quote_period
self.ticker = 'SPFB.RTS'
self.box_size_divider = 1.
self.pf = point_and_figure.Point_and_Figure()
#print(quote_period)
if (quote_period == 'daily' or quote_period == 'hour'):
self.from_date = (date.today() - timedelta(
days=days_look_back_min_candles * 5)).strftime('%Y%m%d')
else:
self.from_date = (
date.today() -
timedelta(days=days_look_back_min_candles)).strftime('%Y%m%d')
self.get_quotes()
self.last_quote_date = None
self.pf.box_size = box_size
self.pf.process_df(self.quote)
self.pf.prepare_datasource()
self.plot = Figure(
plot_height=400,
plot_width=600,
title=graph_name,
tools="crosshair,pan,reset,resize,wheel_zoom,save",
)
self.yticker = bokeh.models.FixedTicker()
#print pf.yticks
self.yticker.ticks = list(self.pf.yticks)
self.xticker = bokeh.models.FixedTicker()
self.xticker.ticks = list(self.pf.xticks)
# ticker.interval = 1
#p.xaxis.ticker = ticker
self.plot.ygrid.ticker = self.yticker
self.plot.xgrid.ticker = self.xticker
self.plot.yaxis.formatter = bokeh.models.NumeralTickFormatter(
format=self.pf.bokeh_ticks_format)
self.plot.yaxis.ticker = self.yticker
#self.plot.ygrid.band_fill_alpha = 0.05
#self.plot.ygrid.band_fill_color = "navy"
#self.glyphs_size = 100.
#self.glyphs_size = self.pf.box_size / 1. / len(self.pf.yticks) * self.pf.scale_factor
self.scale = Slider(title="scale",
value=300,
start=300,
end=3000,
step=1)
self.scale.on_change('value', self.scale_change)
self.recalc_glyphs_size = True
self.calc_glyphs()
self.source_x = ColumnDataSource(
data=dict(x=self.pf.x_x, y=self.pf.x_y, size=self.glyphs_size_x))
self.source_o = ColumnDataSource(
data=dict(x=self.pf.o_x, y=self.pf.o_y, size=self.glyphs_size_o))
self.source_digits = ColumnDataSource(
data=dict(x=self.pf.digits_x,
y=self.pf.digits_y,
text=self.pf.digits_text,
size=self.font_size))
self.graph_o = self.plot.circle('x',
'y',
source=self.source_o,
alpha=0.5,
size='size',
color="navy")
self.graph_x = self.plot.cross('x',
'y',
source=self.source_x,
alpha=0.5,
size='size',
color="navy",
angle=np.pi / 4)
self.graph_digits = self.plot.text(
'x',
'y',
source=self.source_digits,
alpha=0.5,
text='text',
color="navy",
text_font_size='size') #text_font_size
self.box = Slider(title="box",
value=self.pf.box_size,
start=10,
end=500,
step=1)
self.box.on_change('value', self.box_change)
self.text = TextInput()
self.text_marker = 0
def calc_glyphs(self):
#self.glyphs_size = self.pf.box_size / 4. / len(self.pf.yticks) * self.pf.scale_factor
#print self.glyphs_size
if self.recalc_glyphs_size:
self.glyphs_size = min(
self.plot.plot_height / 1.2 / (len(self.pf.yticks) + 3),
self.plot.plot_width / 1.2 / (len(self.pf.xticks) + 3))
self.scale.value = int(self.glyphs_size * 100)
self.recalc_glyphs_size = False
if self.glyphs_size < 3:
self.glyphs_size = 3
self.glyphs_size_x = [self.glyphs_size for _ in self.pf.x_x]
self.glyphs_size_o = [self.glyphs_size for _ in self.pf.o_x]
self.font_size = [
'{}px'.format(self.glyphs_size) for _ in self.pf.digits_x
]
#print(self.glyphs_size_x)
def scale_change(self, attrname, old, new):
self.glyphs_size = self.scale.value / 100.
#print(self.glyphs_size)
def box_change(self, attrname, old, new):
self.pf.box_size = self.box.value / self.box_size_divider
self.recalc_glyphs_size = True
def update_data(self, attrname, old, new):
#print('box')
# Get the current slider values
# Generate the new curve
self.pf.process_df(self.quote)
self.pf.prepare_datasource()
self.yticker.ticks = list(self.pf.yticks)
self.xticker.ticks = list(self.pf.xticks)
self.calc_glyphs()
#self.graph_o.glyph.size = self.glyphs_size
#self.graph_x.glyph.size = self.glyphs_size
#self.shuffle(x=self.pf.x_x, y=self.pf.x_y)
data = dict(x=self.pf.x_x, y=self.pf.x_y, size=self.glyphs_size_x)
self.source_x.data = data
#self.shuffle(x=self.pf.o_x, y=self.pf.o_y)
data = dict(x=self.pf.o_x, y=self.pf.o_y, size=self.glyphs_size_o)
self.source_o.data = data
data = dict(x=self.pf.digits_x,
y=self.pf.digits_y,
text=self.pf.digits_text,
size=self.font_size)
self.source_digits.data = data
title="Optimization with side condition",
x_range=[lagrange_settings.x_min, lagrange_settings.x_max],
y_range=[lagrange_settings.y_min, lagrange_settings.y_max])
# Plot contour of f(x,y)
contour_f = my_bokeh_utils.Contour(plot, line_color='grey', line_width=1)
# Plot active isolevel f(x,y)=v
contour_f0 = my_bokeh_utils.Contour(plot, add_label=True, line_color='black', line_width=2, legend='f(x,y) = v')
# Plot constraint function contour g(x,y)=0
contour_g = my_bokeh_utils.Contour(plot, line_color='red', line_width=2, legend='g(x,y) = 0')
# Plot corresponding tangent vector
quiver_isolevel = my_bokeh_utils.Quiver(plot, fix_at_middle=False, line_width=2, color='black')
# Plot corresponding tangent vector
quiver_constraint = my_bokeh_utils.Quiver(plot, fix_at_middle=False, line_width=2, color='red')
# Plot mark at position on constraint function
plot.cross(x='x', y='y', color='red', size=10, line_width=2, source=source_mark)
# object that detects, if a position in the plot is clicked on
interactor = my_bokeh_utils.Interactor(plot)
# adds callback function to interactor, if position in plot is clicked, call on_selection_change
interactor.on_click(on_selection_change)
# text input window for objective function f(x,y) to be optimized
f_input = TextInput(value=lagrange_settings.f_init, title="f(x,y):")
f_input.on_change('value', f_changed)
# dropdown menu for selecting one of the sample functions
sample_fun_input_f = Dropdown(label="choose a sample function f(x,y) or enter one below",
menu=lagrange_settings.sample_f_names)
sample_fun_input_f.on_click(sample_fun_input_f_changed)
def plotall(data, circleinds=[], crossinds=[], edgeinds=[], htmlname=None, noiseplot=None, url_path='plots', fileroot=None):
""" Create interactive plot (preserving links between panels) from data dictionary
data has keys of snr, time, dm, sizes, key and more.
Optional index arguments are used to filter full data set.
This can be used to remove bad segments or apply different symbols to subsets.
url_path is path difference to png files for taptool. ('../plots' for jupyter notebook, 'plots' for public page)
fileroot is the sdm file name used as root for all png files.
"""
# set up data dictionary
if not circleinds: circleinds = calcinds(data, np.abs(data['snrs']).min())
if not crossinds: crossinds = calcinds(data, -1*np.abs(data['snrs']).min())
TOOLS = "hover,tap,pan,box_select,wheel_zoom,reset"
# set ranges
datalen = len(data['dm'])
inds = circleinds + crossinds + edgeinds
dm = [data['dm'][i] for i in inds]
dm_min = min(min(dm), max(dm)/1.2)
dm_max = max(max(dm), min(dm)*1.2)
time = [data['time'][i] for i in inds]
time_min = min(time)
time_max = max(time)
l1 = [data['l1'][i] for i in inds]
l1_min = min(l1)
l1_max = max(l1)
m1 = [data['m1'][i] for i in inds]
m1_min = min(m1)
m1_max = max(m1)
specstd = [data['specstd'][i] for i in inds]
specstd_min = min(specstd)
specstd_max = max(specstd)
imkur = [data['imkur'][i] for i in inds]
imkur_min = min(imkur)
imkur_max = max(imkur)
# create figures
dmt = Figure(plot_width=950, plot_height=500, toolbar_location="left", x_axis_label='Time (s; relative)',
y_axis_label='DM (pc/cm3)', x_range=(time_min, time_max), y_range=(dm_min, dm_max),
webgl=True, tools=TOOLS)
loc = Figure(plot_width=450, plot_height=400, toolbar_location="left", x_axis_label='l1 (rad)', y_axis_label='m1 (rad)',
x_range=(l1_min, l1_max), y_range=(m1_min,m1_max), tools=TOOLS, webgl=True)
stat = Figure(plot_width=450, plot_height=400, toolbar_location="left", x_axis_label='Spectral std',
y_axis_label='Image kurtosis', x_range=(specstd_min, specstd_max),
y_range=(imkur_min, imkur_max), tools=TOOLS, webgl=True)
norm = Figure(plot_width=450, plot_height=400, toolbar_location="left", x_axis_label='SNR observed',
y_axis_label='SNR expected', tools=TOOLS, webgl=True)
# create positive symbol source and add glyphs
source = ColumnDataSource(data = dict({(key, tuple([value[i] for i in circleinds if i not in edgeinds]))
for (key, value) in data.iteritems()}))
dmt.circle('time', 'dm', size='sizes', fill_color='colors', line_color=None, fill_alpha=0.2, source=source)
loc.circle('l1', 'm1', size='sizes', line_color=None, fill_color='colors', fill_alpha=0.2, source=source)
stat.circle('specstd', 'imkur', size='sizes', line_color=None, fill_color='colors', fill_alpha=0.2, source=source)
norm.circle('abssnr', 'zs', size='sizes', line_color=None, fill_color='colors', fill_alpha=0.2, source=source)
# create negative symbol source and add glyphs
if crossinds:
sourceneg = ColumnDataSource(data = dict({(key, tuple([value[i] for i in crossinds]))
for (key, value) in data.iteritems()}))
dmt.cross('time', 'dm', size='sizes', fill_color='colors', line_alpha=0.3, source=sourceneg)
loc.cross('l1', 'm1', size='sizes', line_color='colors', line_alpha=0.3, source=sourceneg)
stat.cross('specstd', 'imkur', size='sizes', line_color='colors', line_alpha=0.3, source=sourceneg)
norm.cross('abssnr', 'zs', size='sizes', line_color='colors', line_alpha=0.3, source=sourceneg)
# create linked symbol source and add glyphs
if edgeinds:
sourceedge = ColumnDataSource(data = dict({(key, tuple([value[i] for i in edgeinds]))
for (key, value) in data.iteritems()}))
dmt.circle('time', 'dm', size='sizes', line_color='colors', fill_color='colors', line_alpha=0.5, fill_alpha=0.2, source=sourceedge)
loc.circle('l1', 'm1', size='sizes', line_color='colors', fill_color='colors', source=sourceedge, line_alpha=0.5, fill_alpha=0.2)
stat.circle('specstd', 'imkur', size='sizes', line_color='colors', fill_color='colors', source=sourceedge, line_alpha=0.5, fill_alpha=0.2)
norm.circle('abssnr', 'zs', size='sizes', line_color='colors', fill_color='colors', source=sourceedge, line_alpha=0.5, fill_alpha=0.2)
hover = dmt.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('SNR', '@snrs'), ('key', '@key')])
hover = loc.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('SNR', '@snrs'), ('key', '@key')])
hover = stat.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('SNR', '@snrs'), ('key', '@key')])
hover = norm.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('SNR', '@snrs'), ('key', '@key')])
if url_path and fileroot:
url = '{}/cands_{}[email protected]'.format(url_path, fileroot)
taptool = dmt.select(type=TapTool)
taptool.callback = OpenURL(url=url)
taptool = loc.select(type=TapTool)
taptool.callback = OpenURL(url=url)
taptool = stat.select(type=TapTool)
taptool.callback = OpenURL(url=url)
taptool = norm.select(type=TapTool)
taptool.callback = OpenURL(url=url)
# this approach does not preserve links between panels
# dmt = plotdmt(data, circleinds=circleinds, crossinds=crossinds, edgeinds=edgeinds, url_path=url_path, fileroot=fileroot, tools=TOOLS) # maybe add size?
# loc = plotloc(data, circleinds=circleinds, crossinds=crossinds, edgeinds=edgeinds, url_path=url_path, fileroot=fileroot, tools=TOOLS)
# stat = plotstat(data, circleinds=circleinds, crossinds=crossinds, edgeinds=edgeinds, url_path=url_path, fileroot=fileroot, tools=TOOLS)
# norm = plotnorm(data, circleinds=circleinds, crossinds=crossinds, edgeinds=edgeinds, url_path=url_path, fileroot=fileroot, tools=TOOLS)
# arrange figures
top = HBox(dmt, width=950)
middle = HBox(loc, stat, width=950)
if noiseplot:
bottom = HBox(norm, noiseplot, width=950)
else:
bottom = HBox(norm, width=950)
combined = VBox(top, middle, bottom, width=950)
if htmlname:
output_file(htmlname)
save(combined)
else:
return combined
def plotall(data,
circleinds=[],
crossinds=[],
edgeinds=[],
htmlname=None,
noiseplot=None,
url_path='plots',
fileroot=None):
""" Create interactive plot (preserving links between panels) from data dictionary
data has keys of snr, time, dm, sizes, key and more.
Optional index arguments are used to filter full data set.
This can be used to remove bad segments or apply different symbols to subsets.
url_path is path difference to png files for taptool. ('../plots' for jupyter notebook, 'plots' for public page)
fileroot is the sdm file name used as root for all png files.
"""
# set up data dictionary
if not circleinds: circleinds = calcinds(data, np.abs(data['snrs']).min())
if not crossinds:
crossinds = calcinds(data, -1 * np.abs(data['snrs']).min())
TOOLS = "hover,tap,pan,box_select,wheel_zoom,reset"
# set ranges
datalen = len(data['dm'])
inds = circleinds + crossinds + edgeinds
dm = [data['dm'][i] for i in inds]
dm_min = min(min(dm), max(dm) / 1.2)
dm_max = max(max(dm), min(dm) * 1.2)
time = [data['time'][i] for i in inds]
time_min = min(time)
time_max = max(time)
l1 = [data['l1'][i] for i in inds]
l1_min = min(l1)
l1_max = max(l1)
m1 = [data['m1'][i] for i in inds]
m1_min = min(m1)
m1_max = max(m1)
specstd = [data['specstd'][i] for i in inds]
specstd_min = min(specstd)
specstd_max = max(specstd)
imkur = [data['imkur'][i] for i in inds]
imkur_min = min(imkur)
imkur_max = max(imkur)
# create figures
dmt = Figure(plot_width=950,
plot_height=500,
toolbar_location="left",
x_axis_label='Time (s; relative)',
y_axis_label='DM (pc/cm3)',
x_range=(time_min, time_max),
y_range=(dm_min, dm_max),
output_backend='webgl',
tools=TOOLS)
loc = Figure(plot_width=450,
plot_height=400,
toolbar_location="left",
x_axis_label='l1 (rad)',
y_axis_label='m1 (rad)',
x_range=(l1_min, l1_max),
y_range=(m1_min, m1_max),
tools=TOOLS,
output_backend='webgl')
stat = Figure(plot_width=450,
plot_height=400,
toolbar_location="left",
x_axis_label='Spectral std',
y_axis_label='Image kurtosis',
x_range=(specstd_min, specstd_max),
y_range=(imkur_min, imkur_max),
tools=TOOLS,
output_backend='webgl')
norm = Figure(plot_width=450,
plot_height=400,
toolbar_location="left",
x_axis_label='SNR observed',
y_axis_label='SNR expected',
tools=TOOLS,
output_backend='webgl')
# create positive symbol source and add glyphs
source = ColumnDataSource(
data=dict({(key,
tuple([value[i] for i in circleinds if i not in edgeinds]))
for (key, value) in data.iteritems()}))
dmt.circle('time',
'dm',
size='sizes',
fill_color='colors',
line_color=None,
fill_alpha=0.2,
source=source)
loc.circle('l1',
'm1',
size='sizes',
line_color=None,
fill_color='colors',
fill_alpha=0.2,
source=source)
stat.circle('specstd',
'imkur',
size='sizes',
line_color=None,
fill_color='colors',
fill_alpha=0.2,
source=source)
norm.circle('abssnr',
'zs',
size='sizes',
line_color=None,
fill_color='colors',
fill_alpha=0.2,
source=source)
# create negative symbol source and add glyphs
if crossinds:
sourceneg = ColumnDataSource(
data=dict({(key, tuple([value[i] for i in crossinds]))
for (key, value) in data.iteritems()}))
dmt.cross('time',
'dm',
size='sizes',
fill_color='colors',
line_alpha=0.3,
source=sourceneg)
loc.cross('l1',
'm1',
size='sizes',
line_color='colors',
line_alpha=0.3,
source=sourceneg)
stat.cross('specstd',
'imkur',
size='sizes',
line_color='colors',
line_alpha=0.3,
source=sourceneg)
norm.cross('abssnr',
'zs',
size='sizes',
line_color='colors',
line_alpha=0.3,
source=sourceneg)
# create linked symbol source and add glyphs
if edgeinds:
sourceedge = ColumnDataSource(
data=dict({(key, tuple([value[i] for i in edgeinds]))
for (key, value) in data.iteritems()}))
dmt.circle('time',
'dm',
size='sizes',
line_color='colors',
fill_color='colors',
line_alpha=0.5,
fill_alpha=0.2,
source=sourceedge)
loc.circle('l1',
'm1',
size='sizes',
line_color='colors',
fill_color='colors',
source=sourceedge,
line_alpha=0.5,
fill_alpha=0.2)
stat.circle('specstd',
'imkur',
size='sizes',
line_color='colors',
fill_color='colors',
source=sourceedge,
line_alpha=0.5,
fill_alpha=0.2)
norm.circle('abssnr',
'zs',
size='sizes',
line_color='colors',
fill_color='colors',
source=sourceedge,
line_alpha=0.5,
fill_alpha=0.2)
hover = dmt.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('SNR', '@snrs'), ('key', '@key')])
hover = loc.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('SNR', '@snrs'), ('key', '@key')])
hover = stat.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('SNR', '@snrs'), ('key', '@key')])
hover = norm.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('SNR', '@snrs'), ('key', '@key')])
if url_path and fileroot:
url = '{}/cands_{}[email protected]'.format(url_path, fileroot)
taptool = dmt.select(type=TapTool)
taptool.callback = OpenURL(url=url)
taptool = loc.select(type=TapTool)
taptool.callback = OpenURL(url=url)
taptool = stat.select(type=TapTool)
taptool.callback = OpenURL(url=url)
taptool = norm.select(type=TapTool)
taptool.callback = OpenURL(url=url)
# this approach does not preserve links between panels
# dmt = plotdmt(data, circleinds=circleinds, crossinds=crossinds, edgeinds=edgeinds, url_path=url_path, fileroot=fileroot, tools=TOOLS) # maybe add size?
# loc = plotloc(data, circleinds=circleinds, crossinds=crossinds, edgeinds=edgeinds, url_path=url_path, fileroot=fileroot, tools=TOOLS)
# stat = plotstat(data, circleinds=circleinds, crossinds=crossinds, edgeinds=edgeinds, url_path=url_path, fileroot=fileroot, tools=TOOLS)
# norm = plotnorm(data, circleinds=circleinds, crossinds=crossinds, edgeinds=edgeinds, url_path=url_path, fileroot=fileroot, tools=TOOLS)
# arrange figures
top = Row(dmt, width=950)
middle = Row(loc, stat, width=950)
if noiseplot:
bottom = Row(norm, noiseplot, width=950)
else:
bottom = Row(norm, width=950)
combined = Column(top, middle, bottom, width=950)
if htmlname:
output_file(htmlname)
save(combined)
else:
return combined
def plotnorm(data,
circleinds=[],
crossinds=[],
edgeinds=[],
url_path=None,
fileroot=None,
tools="hover,tap,pan,box_select,wheel_zoom,reset",
plot_width=450,
plot_height=400):
""" Make a light-weight norm figure """
fields = ['zs', 'sizes', 'colors', 'abssnr', 'key', 'snrs']
if not circleinds: circleinds = range(len(data['snrs']))
source = ColumnDataSource(
data=dict({(key,
tuple([value[i] for i in circleinds if i not in edgeinds]))
for (key, value) in data.iteritems() if key in fields}))
norm = Figure(plot_width=plot_width,
plot_height=plot_height,
toolbar_location="left",
x_axis_label='SNR observed',
y_axis_label='SNR expected',
tools=tools,
output_backend='webgl')
norm.circle('abssnr',
'zs',
size='sizes',
line_color=None,
fill_color='colors',
fill_alpha=0.2,
source=source)
if crossinds:
sourceneg = ColumnDataSource(
data=dict({(key, tuple([value[i] for i in crossinds]))
for (key, value) in data.iteritems() if key in fields}))
norm.cross('abssnr',
'zs',
size='sizes',
line_color='colors',
line_alpha=0.3,
source=sourceneg)
if edgeinds:
sourceedge = ColumnDataSource(
data=dict({(key, tuple([value[i] for i in edgeinds]))
for (key, value) in data.iteritems() if key in fields}))
norm.circle('abssnr',
'zs',
size='sizes',
line_color='colors',
fill_color='colors',
source=sourceedge,
line_alpha=0.5,
fill_alpha=0.2)
hover = norm.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('SNR', '@snrs'), ('key', '@key')])
if url_path and fileroot:
url = '{}/cands_{}[email protected]'.format(url_path, fileroot)
taptool = norm.select(type=TapTool)
taptool.callback = OpenURL(url=url)
return norm
def plotstat(data,
circleinds=None,
crossinds=None,
edgeinds=None,
url_path=None,
fileroot=None,
tools="hover,tap,pan,box_select,wheel_zoom,reset",
plot_width=450,
plot_height=400):
""" Make a light-weight stat figure """
fields = ['imkur', 'specstd', 'sizes', 'colors', 'snrs', 'key']
if not circleinds: circleinds = range(len(data['snrs']))
# set ranges
datalen = len(data['dm'])
inds = circleinds + crossinds + edgeinds
specstd = [data['specstd'][i] for i in inds]
specstd_min = min(specstd)
specstd_max = max(specstd)
imkur = [data['imkur'][i] for i in inds]
imkur_min = min(imkur)
imkur_max = max(imkur)
source = ColumnDataSource(
data=dict({(key,
tuple([value[i] for i in circleinds if i not in edgeinds]))
for (key, value) in data.iteritems() if key in fields}))
stat = Figure(plot_width=plot_width,
plot_height=plot_height,
toolbar_location="left",
x_axis_label='Spectral std',
y_axis_label='Image kurtosis',
x_range=(specstd_min, specstd_max),
y_range=(imkur_min, imkur_max),
tools=tools,
output_backend='webgl')
stat.circle('specstd',
'imkur',
size='sizes',
line_color=None,
fill_color='colors',
fill_alpha=0.2,
source=source)
if crossinds:
sourceneg = ColumnDataSource(
data=dict({(key, tuple([value[i] for i in crossinds]))
for (key, value) in data.iteritems() if key in fields}))
stat.cross('specstd',
'imkur',
size='sizes',
line_color='colors',
line_alpha=0.3,
source=sourceneg)
if edgeinds:
sourceedge = ColumnDataSource(
data=dict({(key, tuple([value[i] for i in edgeinds]))
for (key, value) in data.iteritems() if key in fields}))
stat.circle('specstd',
'imkur',
size='sizes',
line_color='colors',
fill_color='colors',
source=sourceedge,
line_alpha=0.5,
fill_alpha=0.2)
hover = stat.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('SNR', '@snrs'), ('key', '@key')])
if url_path and fileroot:
url = '{}/cands_{}[email protected]'.format(url_path, fileroot)
taptool = stat.select(type=TapTool)
taptool.callback = OpenURL(url=url)
return stat
def plotloc(data,
circleinds=[],
crossinds=[],
edgeinds=[],
url_path=None,
fileroot=None,
tools="hover,tap,pan,box_select,wheel_zoom,reset",
plot_width=450,
plot_height=400):
""" Make a light-weight loc figure """
fields = ['l1', 'm1', 'sizes', 'colors', 'snrs', 'key']
if not circleinds: circleinds = range(len(data['snrs']))
# set ranges
datalen = len(data['dm'])
inds = circleinds + crossinds + edgeinds
l1 = [data['l1'][i] for i in inds]
l1_min = min(l1)
l1_max = max(l1)
m1 = [data['m1'][i] for i in inds]
m1_min = min(m1)
m1_max = max(m1)
source = ColumnDataSource(
data=dict({(key,
tuple([value[i] for i in circleinds if i not in edgeinds]))
for (key, value) in data.iteritems() if key in fields}))
loc = Figure(plot_width=plot_width,
plot_height=plot_height,
toolbar_location="left",
x_axis_label='l1 (rad)',
y_axis_label='m1 (rad)',
x_range=(l1_min, l1_max),
y_range=(m1_min, m1_max),
tools=tools,
output_backend='webgl')
loc.circle('l1',
'm1',
size='sizes',
line_color=None,
fill_color='colors',
fill_alpha=0.2,
source=source)
if crossinds:
sourceneg = ColumnDataSource(
data=dict({(key, tuple([value[i] for i in crossinds]))
for (key, value) in data.iteritems() if key in fields}))
loc.cross('l1',
'm1',
size='sizes',
line_color='colors',
line_alpha=0.3,
source=sourceneg)
if edgeinds:
sourceedge = ColumnDataSource(
data=dict({(key, tuple([value[i] for i in edgeinds]))
for (key, value) in data.iteritems() if key in fields}))
loc.circle('l1',
'm1',
size='sizes',
line_color='colors',
fill_color='colors',
source=sourceedge,
line_alpha=0.5,
fill_alpha=0.2)
hover = loc.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('SNR', '@snrs'), ('key', '@key')])
if url_path and fileroot:
url = '{}/cands_{}[email protected]'.format(url_path, fileroot)
taptool = loc.select(type=TapTool)
taptool.callback = OpenURL(url=url)
return loc
def plotdmt(data,
circleinds=[],
crossinds=[],
edgeinds=[],
url_path=None,
fileroot=None,
tools="hover,tap,pan,box_select,wheel_zoom,reset",
plot_width=950,
plot_height=500):
""" Make a light-weight dm-time figure """
fields = ['dm', 'time', 'sizes', 'colors', 'snrs', 'key']
if not circleinds: circleinds = range(len(data['snrs']))
# set ranges
datalen = len(data['dm'])
inds = circleinds + crossinds + edgeinds
dm = [data['dm'][i] for i in inds]
dm_min = min(min(dm), max(dm) / 1.2)
dm_max = max(max(dm), min(dm) * 1.2)
time = [data['time'][i] for i in inds]
time_min = min(time)
time_max = max(time)
source = ColumnDataSource(
data=dict({(key,
tuple([value[i] for i in circleinds if i not in edgeinds]))
for (key, value) in data.iteritems() if key in fields}))
dmt = Figure(plot_width=plot_width,
plot_height=plot_height,
toolbar_location="left",
x_axis_label='Time (s; relative)',
y_axis_label='DM (pc/cm3)',
x_range=(time_min, time_max),
y_range=(dm_min, dm_max),
output_backend='webgl',
tools=tools)
dmt.circle('time',
'dm',
size='sizes',
fill_color='colors',
line_color=None,
fill_alpha=0.2,
source=source)
if crossinds:
sourceneg = ColumnDataSource(
data=dict({(key, tuple([value[i] for i in crossinds]))
for (key, value) in data.iteritems() if key in fields}))
dmt.cross('time',
'dm',
size='sizes',
fill_color='colors',
line_alpha=0.3,
source=sourceneg)
if edgeinds:
sourceedge = ColumnDataSource(
data=dict({(key, tuple([value[i] for i in edgeinds]))
for (key, value) in data.iteritems() if key in fields}))
dmt.circle('time',
'dm',
size='sizes',
line_color='colors',
fill_color='colors',
line_alpha=0.5,
fill_alpha=0.2,
source=sourceedge)
hover = dmt.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('SNR', '@snrs'), ('key', '@key')])
if url_path and fileroot:
# url = '{}/cands_{}_sc@scan-seg@seg-i@candint-dm@[email protected]'.format(url_path, fileroot)
url = '{}/cands_{}[email protected]'.format(url_path, fileroot)
taptool = dmt.select(type=TapTool)
taptool.callback = OpenURL(url=url)
return dmt
