def photometry_plot(obj_id, user, width=600, device="browser"):
"""Create object photometry scatter plot.
Parameters
----------
obj_id : str
ID of Obj to be plotted.
Returns
-------
dict
Returns Bokeh JSON embedding for the desired plot.
"""
data = pd.read_sql(
DBSession()
.query(
Photometry,
Telescope.nickname.label("telescope"),
Instrument.name.label("instrument"),
)
.join(Instrument, Instrument.id == Photometry.instrument_id)
.join(Telescope, Telescope.id == Instrument.telescope_id)
.filter(Photometry.obj_id == obj_id)
.filter(
Photometry.groups.any(Group.id.in_([g.id for g in user.accessible_groups]))
)
.statement,
DBSession().bind,
)
if data.empty:
return None, None, None
# get spectra to annotate on phot plots
spectra = (
Spectrum.query_records_accessible_by(user)
.filter(Spectrum.obj_id == obj_id)
.all()
)
data['color'] = [get_color(f) for f in data['filter']]
# get marker for each unique instrument
instruments = list(data.instrument.unique())
markers = []
for i, inst in enumerate(instruments):
markers.append(phot_markers[i % len(phot_markers)])
filters = list(set(data['filter']))
colors = [get_color(f) for f in filters]
color_mapper = CategoricalColorMapper(factors=filters, palette=colors)
color_dict = {'field': 'filter', 'transform': color_mapper}
labels = []
for i, datarow in data.iterrows():
label = f'{datarow["instrument"]}/{datarow["filter"]}'
if datarow['origin'] is not None:
label += f'/{datarow["origin"]}'
labels.append(label)
data['label'] = labels
data['zp'] = PHOT_ZP
data['magsys'] = 'ab'
data['alpha'] = 1.0
data['lim_mag'] = (
-2.5 * np.log10(data['fluxerr'] * PHOT_DETECTION_THRESHOLD) + data['zp']
)
# Passing a dictionary to a bokeh datasource causes the frontend to die,
# deleting the dictionary column fixes that
del data['original_user_data']
# keep track of things that are only upper limits
data['hasflux'] = ~data['flux'].isna()
# calculate the magnitudes - a photometry point is considered "significant"
# or "detected" (and thus can be represented by a magnitude) if its snr
# is above PHOT_DETECTION_THRESHOLD
obsind = data['hasflux'] & (
data['flux'].fillna(0.0) / data['fluxerr'] >= PHOT_DETECTION_THRESHOLD
)
data.loc[~obsind, 'mag'] = None
data.loc[obsind, 'mag'] = -2.5 * np.log10(data[obsind]['flux']) + PHOT_ZP
# calculate the magnitude errors using standard error propagation formulae
# https://en.wikipedia.org/wiki/Propagation_of_uncertainty#Example_formulae
data.loc[~obsind, 'magerr'] = None
coeff = 2.5 / np.log(10)
magerrs = np.abs(coeff * data[obsind]['fluxerr'] / data[obsind]['flux'])
data.loc[obsind, 'magerr'] = magerrs
data['obs'] = obsind
data['stacked'] = False
split = data.groupby('label', sort=False)
finite = np.isfinite(data['flux'])
fdata = data[finite]
lower = np.min(fdata['flux']) * 0.95
upper = np.max(fdata['flux']) * 1.05
xmin = data['mjd'].min() - 2
xmax = data['mjd'].max() + 2
# Layout parameters based on device type
active_drag = None if "mobile" in device or "tablet" in device else "box_zoom"
tools = (
'box_zoom,pan,reset'
if "mobile" in device or "tablet" in device
else "box_zoom,wheel_zoom,pan,reset,save"
)
legend_loc = "below" if "mobile" in device or "tablet" in device else "right"
legend_orientation = (
"vertical" if device in ["browser", "mobile_portrait"] else "horizontal"
)
# Compute a plot component height based on rough number of legend rows added below the plot
# Values are based on default sizing of bokeh components and an estimate of how many
# legend items would fit on the average device screen. Note that the legend items per
# row is computed more exactly later once labels are extracted from the data (with the
# add_plot_legend() function).
#
# The height is manually computed like this instead of using built in aspect_ratio/sizing options
# because with the new Interactive Legend approach (instead of the legacy CheckboxLegendGroup), the
# Legend component is considered part of the plot and plays into the sizing computations. Since the
# number of items in the legend can alter the needed heights of the plot, using built-in Bokeh options
# for sizing does not allow for keeping the actual graph part of the plot at a consistent aspect ratio.
#
# For the frame width, by default we take the desired plot width minus 64 for the y-axis/label taking
# up horizontal space
frame_width = width - 64
if device == "mobile_portrait":
legend_items_per_row = 1
legend_row_height = 24
aspect_ratio = 1
elif device == "mobile_landscape":
legend_items_per_row = 4
legend_row_height = 50
aspect_ratio = 1.8
elif device == "tablet_portrait":
legend_items_per_row = 5
legend_row_height = 50
aspect_ratio = 1.5
elif device == "tablet_landscape":
legend_items_per_row = 7
legend_row_height = 50
aspect_ratio = 1.8
elif device == "browser":
# Width minus some base width for the legend, which is only a column to the right
# for browser mode
frame_width = width - 200
height = (
500
if device == "browser"
else math.floor(width / aspect_ratio)
+ legend_row_height * int(len(split) / legend_items_per_row)
+ 30 # 30 is the height of the toolbar
)
plot = figure(
frame_width=frame_width,
height=height,
active_drag=active_drag,
tools=tools,
toolbar_location='above',
toolbar_sticky=True,
y_range=(lower, upper),
min_border_right=16,
x_axis_location='above',
sizing_mode="stretch_width",
)
plot.xaxis.axis_label = 'MJD'
now = Time.now().mjd
plot.extra_x_ranges = {"Days Ago": Range1d(start=now - xmin, end=now - xmax)}
plot.add_layout(LinearAxis(x_range_name="Days Ago", axis_label="Days Ago"), 'below')
imhover = HoverTool(tooltips=tooltip_format)
imhover.renderers = []
plot.add_tools(imhover)
model_dict = {}
legend_items = []
for i, (label, sdf) in enumerate(split):
renderers = []
# for the flux plot, we only show things that have a flux value
df = sdf[sdf['hasflux']]
key = f'obs{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='flux',
color='color',
marker=factor_mark('instrument', markers, instruments),
fill_color=color_dict,
alpha='alpha',
source=ColumnDataSource(df),
)
renderers.append(model_dict[key])
imhover.renderers.append(model_dict[key])
key = f'bin{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='flux',
color='color',
marker=factor_mark('instrument', markers, instruments),
fill_color=color_dict,
source=ColumnDataSource(
data=dict(
mjd=[],
flux=[],
fluxerr=[],
filter=[],
color=[],
lim_mag=[],
mag=[],
magerr=[],
stacked=[],
instrument=[],
)
),
)
renderers.append(model_dict[key])
imhover.renderers.append(model_dict[key])
key = 'obserr' + str(i)
y_err_x = []
y_err_y = []
for d, ro in df.iterrows():
px = ro['mjd']
py = ro['flux']
err = ro['fluxerr']
y_err_x.append((px, px))
y_err_y.append((py - err, py + err))
model_dict[key] = plot.multi_line(
xs='xs',
ys='ys',
color='color',
alpha='alpha',
source=ColumnDataSource(
data=dict(
xs=y_err_x, ys=y_err_y, color=df['color'], alpha=[1.0] * len(df)
)
),
)
renderers.append(model_dict[key])
key = f'binerr{i}'
model_dict[key] = plot.multi_line(
xs='xs',
ys='ys',
color='color',
# legend_label=label,
source=ColumnDataSource(data=dict(xs=[], ys=[], color=[])),
)
renderers.append(model_dict[key])
legend_items.append(LegendItem(label=label, renderers=renderers))
if device == "mobile_portrait":
plot.xaxis.ticker.desired_num_ticks = 5
plot.yaxis.axis_label = 'Flux (μJy)'
plot.toolbar.logo = None
add_plot_legend(plot, legend_items, width, legend_orientation, legend_loc)
slider = Slider(
start=0.0,
end=15.0,
value=0.0,
step=1.0,
title='Binsize (days)',
max_width=350,
margin=(4, 10, 0, 10),
)
callback = CustomJS(
args={'slider': slider, 'n_labels': len(split), **model_dict},
code=open(
os.path.join(os.path.dirname(__file__), '../static/js/plotjs', 'stackf.js')
)
.read()
.replace('default_zp', str(PHOT_ZP))
.replace('detect_thresh', str(PHOT_DETECTION_THRESHOLD)),
)
slider.js_on_change('value', callback)
# Mark the first and last detections
detection_dates = data[data['hasflux']]['mjd']
if len(detection_dates) > 0:
first = round(detection_dates.min(), 6)
last = round(detection_dates.max(), 6)
first_color = "#34b4eb"
last_color = "#8992f5"
midpoint = (upper + lower) / 2
line_top = 5 * upper - 4 * midpoint
line_bottom = 5 * lower - 4 * midpoint
y = np.linspace(line_bottom, line_top, num=5000)
first_r = plot.line(
x=np.full(5000, first),
y=y,
line_alpha=0.5,
line_color=first_color,
line_width=2,
)
plot.add_tools(
HoverTool(
tooltips=[("First detection", f'{first}')],
renderers=[first_r],
)
)
last_r = plot.line(
x=np.full(5000, last),
y=y,
line_alpha=0.5,
line_color=last_color,
line_width=2,
)
plot.add_tools(
HoverTool(
tooltips=[("Last detection", f'{last}')],
renderers=[last_r],
)
)
# Mark when spectra were taken
annotate_spec(plot, spectra, lower, upper)
layout = column(slider, plot, width=width, height=height)
p1 = Panel(child=layout, title='Flux')
# now make the mag light curve
ymax = (
np.nanmax(
(
np.nanmax(data.loc[obsind, 'mag']) if any(obsind) else np.nan,
np.nanmax(data.loc[~obsind, 'lim_mag']) if any(~obsind) else np.nan,
)
)
+ 0.1
)
ymin = (
np.nanmin(
(
np.nanmin(data.loc[obsind, 'mag']) if any(obsind) else np.nan,
np.nanmin(data.loc[~obsind, 'lim_mag']) if any(~obsind) else np.nan,
)
)
- 0.1
)
plot = figure(
frame_width=frame_width,
height=height,
active_drag=active_drag,
tools=tools,
y_range=(ymax, ymin),
x_range=(xmin, xmax),
toolbar_location='above',
toolbar_sticky=True,
x_axis_location='above',
sizing_mode="stretch_width",
)
plot.xaxis.axis_label = 'MJD'
now = Time.now().mjd
plot.extra_x_ranges = {"Days Ago": Range1d(start=now - xmin, end=now - xmax)}
plot.add_layout(LinearAxis(x_range_name="Days Ago", axis_label="Days Ago"), 'below')
obj = DBSession().query(Obj).get(obj_id)
if obj.dm is not None:
plot.extra_y_ranges = {
"Absolute Mag": Range1d(start=ymax - obj.dm, end=ymin - obj.dm)
}
plot.add_layout(
LinearAxis(y_range_name="Absolute Mag", axis_label="m - DM"), 'right'
)
# Mark the first and last detections again
detection_dates = data[obsind]['mjd']
if len(detection_dates) > 0:
first = round(detection_dates.min(), 6)
last = round(detection_dates.max(), 6)
midpoint = (ymax + ymin) / 2
line_top = 5 * ymax - 4 * midpoint
line_bottom = 5 * ymin - 4 * midpoint
y = np.linspace(line_bottom, line_top, num=5000)
first_r = plot.line(
x=np.full(5000, first),
y=y,
line_alpha=0.5,
line_color=first_color,
line_width=2,
)
plot.add_tools(
HoverTool(
tooltips=[("First detection", f'{first}')],
renderers=[first_r],
)
)
last_r = plot.line(
x=np.full(5000, last),
y=y,
line_alpha=0.5,
line_color=last_color,
line_width=2,
)
plot.add_tools(
HoverTool(
tooltips=[("Last detection", f'{last}')],
renderers=[last_r],
point_policy='follow_mouse',
)
)
# Mark when spectra were taken
annotate_spec(plot, spectra, ymax, ymin)
imhover = HoverTool(tooltips=tooltip_format)
imhover.renderers = []
plot.add_tools(imhover)
model_dict = {}
# Legend items are individually stored instead of being applied
# directly when plotting so that they can be separated into multiple
# Legend() components if needed (to simulate horizontal row wrapping).
# This is necessary because Bokeh does not support row wrapping with
# horizontally-oriented legends out-of-the-box.
legend_items = []
for i, (label, df) in enumerate(split):
renderers = []
key = f'obs{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='mag',
color='color',
marker=factor_mark('instrument', markers, instruments),
fill_color=color_dict,
alpha='alpha',
source=ColumnDataSource(df[df['obs']]),
)
renderers.append(model_dict[key])
imhover.renderers.append(model_dict[key])
unobs_source = df[~df['obs']].copy()
unobs_source.loc[:, 'alpha'] = 0.8
key = f'unobs{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='lim_mag',
color=color_dict,
marker='inverted_triangle',
fill_color='white',
line_color='color',
alpha='alpha',
source=ColumnDataSource(unobs_source),
)
renderers.append(model_dict[key])
imhover.renderers.append(model_dict[key])
key = f'bin{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='mag',
color=color_dict,
marker=factor_mark('instrument', markers, instruments),
fill_color='color',
source=ColumnDataSource(
data=dict(
mjd=[],
flux=[],
fluxerr=[],
filter=[],
color=[],
lim_mag=[],
mag=[],
magerr=[],
instrument=[],
stacked=[],
)
),
)
renderers.append(model_dict[key])
imhover.renderers.append(model_dict[key])
key = 'obserr' + str(i)
y_err_x = []
y_err_y = []
for d, ro in df[df['obs']].iterrows():
px = ro['mjd']
py = ro['mag']
err = ro['magerr']
y_err_x.append((px, px))
y_err_y.append((py - err, py + err))
model_dict[key] = plot.multi_line(
xs='xs',
ys='ys',
color='color',
alpha='alpha',
source=ColumnDataSource(
data=dict(
xs=y_err_x,
ys=y_err_y,
color=df[df['obs']]['color'],
alpha=[1.0] * len(df[df['obs']]),
)
),
)
renderers.append(model_dict[key])
key = f'binerr{i}'
model_dict[key] = plot.multi_line(
xs='xs',
ys='ys',
color='color',
source=ColumnDataSource(data=dict(xs=[], ys=[], color=[])),
)
renderers.append(model_dict[key])
key = f'unobsbin{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='lim_mag',
color='color',
marker='inverted_triangle',
fill_color='white',
line_color=color_dict,
alpha=0.8,
source=ColumnDataSource(
data=dict(
mjd=[],
flux=[],
fluxerr=[],
filter=[],
color=[],
lim_mag=[],
mag=[],
magerr=[],
instrument=[],
stacked=[],
)
),
)
imhover.renderers.append(model_dict[key])
renderers.append(model_dict[key])
key = f'all{i}'
model_dict[key] = ColumnDataSource(df)
key = f'bold{i}'
model_dict[key] = ColumnDataSource(
df[
[
'mjd',
'flux',
'fluxerr',
'mag',
'magerr',
'filter',
'zp',
'magsys',
'lim_mag',
'stacked',
]
]
)
legend_items.append(LegendItem(label=label, renderers=renderers))
add_plot_legend(plot, legend_items, width, legend_orientation, legend_loc)
plot.yaxis.axis_label = 'AB mag'
plot.toolbar.logo = None
slider = Slider(
start=0.0,
end=15.0,
value=0.0,
step=1.0,
title='Binsize (days)',
max_width=350,
margin=(4, 10, 0, 10),
)
button = Button(label="Export Bold Light Curve to CSV")
button.js_on_click(
CustomJS(
args={'slider': slider, 'n_labels': len(split), **model_dict},
code=open(
os.path.join(
os.path.dirname(__file__), '../static/js/plotjs', "download.js"
)
)
.read()
.replace('objname', obj_id)
.replace('default_zp', str(PHOT_ZP)),
)
)
# Don't need to expose CSV download on mobile
top_layout = (
slider if "mobile" in device or "tablet" in device else row(slider, button)
)
callback = CustomJS(
args={'slider': slider, 'n_labels': len(split), **model_dict},
code=open(
os.path.join(os.path.dirname(__file__), '../static/js/plotjs', 'stackm.js')
)
.read()
.replace('default_zp', str(PHOT_ZP))
.replace('detect_thresh', str(PHOT_DETECTION_THRESHOLD)),
)
slider.js_on_change('value', callback)
layout = column(top_layout, plot, width=width, height=height)
p2 = Panel(child=layout, title='Mag')
# now make period plot
# get periods from annotations
annotation_list = obj.get_annotations_readable_by(user)
period_labels = []
period_list = []
for an in annotation_list:
if 'period' in an.data:
period_list.append(an.data['period'])
period_labels.append(an.origin + ": %.9f" % an.data['period'])
if len(period_list) > 0:
period = period_list[0]
else:
period = None
# don't generate if no period annotated
if period is not None:
# bokeh figure for period plotting
period_plot = figure(
frame_width=frame_width,
height=height,
active_drag=active_drag,
tools=tools,
y_range=(ymax, ymin),
x_range=(-0.01, 2.01), # initially one phase
toolbar_location='above',
toolbar_sticky=False,
x_axis_location='below',
sizing_mode="stretch_width",
)
# axis labels
period_plot.xaxis.axis_label = 'phase'
period_plot.yaxis.axis_label = 'mag'
period_plot.toolbar.logo = None
# do we have a distance modulus (dm)?
obj = DBSession().query(Obj).get(obj_id)
if obj.dm is not None:
period_plot.extra_y_ranges = {
"Absolute Mag": Range1d(start=ymax - obj.dm, end=ymin - obj.dm)
}
period_plot.add_layout(
LinearAxis(y_range_name="Absolute Mag", axis_label="m - DM"), 'right'
)
# initiate hover tool
period_imhover = HoverTool(tooltips=tooltip_format)
period_imhover.renderers = []
period_plot.add_tools(period_imhover)
# initiate period radio buttons
period_selection = RadioGroup(labels=period_labels, active=0)
phase_selection = RadioGroup(labels=["One phase", "Two phases"], active=1)
# store all the plot data
period_model_dict = {}
# iterate over each filter
legend_items = []
for i, (label, df) in enumerate(split):
renderers = []
# fold x-axis on period in days
df['mjd_folda'] = (df['mjd'] % period) / period
df['mjd_foldb'] = df['mjd_folda'] + 1.0
# phase plotting
for ph in ['a', 'b']:
key = 'fold' + ph + f'{i}'
period_model_dict[key] = period_plot.scatter(
x='mjd_fold' + ph,
y='mag',
color='color',
marker=factor_mark('instrument', markers, instruments),
fill_color=color_dict,
alpha='alpha',
# visible=('a' in ph),
source=ColumnDataSource(df[df['obs']]), # only visible data
)
# add to hover tool
period_imhover.renderers.append(period_model_dict[key])
renderers.append(period_model_dict[key])
# errorbars for phases
key = 'fold' + ph + f'err{i}'
y_err_x = []
y_err_y = []
# get each visible error value
for d, ro in df[df['obs']].iterrows():
px = ro['mjd_fold' + ph]
py = ro['mag']
err = ro['magerr']
# set up error tuples
y_err_x.append((px, px))
y_err_y.append((py - err, py + err))
# plot phase errors
period_model_dict[key] = period_plot.multi_line(
xs='xs',
ys='ys',
color='color',
alpha='alpha',
# visible=('a' in ph),
source=ColumnDataSource(
data=dict(
xs=y_err_x,
ys=y_err_y,
color=df[df['obs']]['color'],
alpha=[1.0] * len(df[df['obs']]),
)
),
)
renderers.append(period_model_dict[key])
legend_items.append(LegendItem(label=label, renderers=renderers))
add_plot_legend(
period_plot, legend_items, width, legend_orientation, legend_loc
)
# set up period adjustment text box
period_title = Div(text="Period (days): ")
period_textinput = TextInput(value=str(period if period is not None else 0.0))
period_textinput.js_on_change(
'value',
CustomJS(
args={
'textinput': period_textinput,
'numphases': phase_selection,
'n_labels': len(split),
'p': period_plot,
**period_model_dict,
},
code=open(
os.path.join(
os.path.dirname(__file__), '../static/js/plotjs', 'foldphase.js'
)
).read(),
),
)
# a way to modify the period
period_double_button = Button(label="*2", width=30)
period_double_button.js_on_click(
CustomJS(
args={'textinput': period_textinput},
code="""
const period = parseFloat(textinput.value);
textinput.value = parseFloat(2.*period).toFixed(9);
""",
)
)
period_halve_button = Button(label="/2", width=30)
period_halve_button.js_on_click(
CustomJS(
args={'textinput': period_textinput},
code="""
const period = parseFloat(textinput.value);
textinput.value = parseFloat(period/2.).toFixed(9);
""",
)
)
# a way to select the period
period_selection.js_on_click(
CustomJS(
args={'textinput': period_textinput, 'periods': period_list},
code="""
textinput.value = parseFloat(periods[this.active]).toFixed(9);
""",
)
)
phase_selection.js_on_click(
CustomJS(
args={
'textinput': period_textinput,
'numphases': phase_selection,
'n_labels': len(split),
'p': period_plot,
**period_model_dict,
},
code=open(
os.path.join(
os.path.dirname(__file__), '../static/js/plotjs', 'foldphase.js'
)
).read(),
)
)
# layout
if device == "mobile_portrait":
period_controls = column(
row(
period_title,
period_textinput,
period_double_button,
period_halve_button,
width=width,
sizing_mode="scale_width",
),
phase_selection,
period_selection,
width=width,
)
# Add extra height to plot based on period control components added
# 18 is the height of each period selection radio option (per default font size)
# and the 130 encompasses the other components which are consistent no matter
# the data size.
height += 130 + 18 * len(period_labels)
else:
period_controls = column(
row(
period_title,
period_textinput,
period_double_button,
period_halve_button,
phase_selection,
width=width,
sizing_mode="scale_width",
),
period_selection,
margin=10,
)
# Add extra height to plot based on period control components added
# Numbers are derived in similar manner to the "mobile_portrait" case above
height += 90 + 18 * len(period_labels)
period_layout = column(period_plot, period_controls, width=width, height=height)
# Period panel
p3 = Panel(child=period_layout, title='Period')
# tabs for mag, flux, period
tabs = Tabs(tabs=[p2, p1, p3], width=width, height=height, sizing_mode='fixed')
else:
# tabs for mag, flux
tabs = Tabs(tabs=[p2, p1], width=width, height=height + 90, sizing_mode='fixed')
return bokeh_embed.json_item(tabs)
def photometry_plot(obj_id, user, width=600, height=300, device="browser"):
"""Create object photometry scatter plot.
Parameters
----------
obj_id : str
ID of Obj to be plotted.
Returns
-------
dict
Returns Bokeh JSON embedding for the desired plot.
"""
data = pd.read_sql(
DBSession().query(
Photometry,
Telescope.nickname.label("telescope"),
Instrument.name.label("instrument"),
).join(Instrument, Instrument.id == Photometry.instrument_id).join(
Telescope, Telescope.id == Instrument.telescope_id).filter(
Photometry.obj_id == obj_id).filter(
Photometry.groups.any(
Group.id.in_([g.id for g in user.accessible_groups
]))).statement,
DBSession().bind,
)
if data.empty:
return None, None, None
# get spectra to annotate on phot plots
spectra = (Spectrum.query_records_accessible_by(user).filter(
Spectrum.obj_id == obj_id).all())
data['color'] = [get_color(f) for f in data['filter']]
labels = []
for i, datarow in data.iterrows():
label = f'{datarow["instrument"]}/{datarow["filter"]}'
if datarow['origin'] is not None:
label += f'/{datarow["origin"]}'
labels.append(label)
data['label'] = labels
data['zp'] = PHOT_ZP
data['magsys'] = 'ab'
data['alpha'] = 1.0
data['lim_mag'] = (
-2.5 * np.log10(data['fluxerr'] * PHOT_DETECTION_THRESHOLD) +
data['zp'])
# Passing a dictionary to a bokeh datasource causes the frontend to die,
# deleting the dictionary column fixes that
del data['original_user_data']
# keep track of things that are only upper limits
data['hasflux'] = ~data['flux'].isna()
# calculate the magnitudes - a photometry point is considered "significant"
# or "detected" (and thus can be represented by a magnitude) if its snr
# is above PHOT_DETECTION_THRESHOLD
obsind = data['hasflux'] & (data['flux'].fillna(0.0) / data['fluxerr'] >=
PHOT_DETECTION_THRESHOLD)
data.loc[~obsind, 'mag'] = None
data.loc[obsind, 'mag'] = -2.5 * np.log10(data[obsind]['flux']) + PHOT_ZP
# calculate the magnitude errors using standard error propagation formulae
# https://en.wikipedia.org/wiki/Propagation_of_uncertainty#Example_formulae
data.loc[~obsind, 'magerr'] = None
coeff = 2.5 / np.log(10)
magerrs = np.abs(coeff * data[obsind]['fluxerr'] / data[obsind]['flux'])
data.loc[obsind, 'magerr'] = magerrs
data['obs'] = obsind
data['stacked'] = False
split = data.groupby('label', sort=False)
finite = np.isfinite(data['flux'])
fdata = data[finite]
lower = np.min(fdata['flux']) * 0.95
upper = np.max(fdata['flux']) * 1.05
active_drag = None if "mobile" in device or "tablet" in device else "box_zoom"
tools = ('box_zoom,pan,reset' if "mobile" in device or "tablet" in device
else "box_zoom,wheel_zoom,pan,reset,save")
plot = figure(
aspect_ratio=2.0 if device == "mobile_landscape" else 1.5,
sizing_mode='scale_both',
active_drag=active_drag,
tools=tools,
toolbar_location='above',
toolbar_sticky=True,
y_range=(lower, upper),
min_border_right=16,
)
imhover = HoverTool(tooltips=tooltip_format)
imhover.renderers = []
plot.add_tools(imhover)
model_dict = {}
for i, (label, sdf) in enumerate(split):
# for the flux plot, we only show things that have a flux value
df = sdf[sdf['hasflux']]
key = f'obs{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='flux',
color='color',
marker='circle',
fill_color='color',
alpha='alpha',
source=ColumnDataSource(df),
)
imhover.renderers.append(model_dict[key])
key = f'bin{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='flux',
color='color',
marker='circle',
fill_color='color',
source=ColumnDataSource(data=dict(
mjd=[],
flux=[],
fluxerr=[],
filter=[],
color=[],
lim_mag=[],
mag=[],
magerr=[],
stacked=[],
instrument=[],
)),
)
imhover.renderers.append(model_dict[key])
key = 'obserr' + str(i)
y_err_x = []
y_err_y = []
for d, ro in df.iterrows():
px = ro['mjd']
py = ro['flux']
err = ro['fluxerr']
y_err_x.append((px, px))
y_err_y.append((py - err, py + err))
model_dict[key] = plot.multi_line(
xs='xs',
ys='ys',
color='color',
alpha='alpha',
source=ColumnDataSource(data=dict(xs=y_err_x,
ys=y_err_y,
color=df['color'],
alpha=[1.0] * len(df))),
)
key = f'binerr{i}'
model_dict[key] = plot.multi_line(
xs='xs',
ys='ys',
color='color',
source=ColumnDataSource(data=dict(xs=[], ys=[], color=[])),
)
plot.xaxis.axis_label = 'MJD'
if device == "mobile_portrait":
plot.xaxis.ticker.desired_num_ticks = 5
plot.yaxis.axis_label = 'Flux (μJy)'
plot.toolbar.logo = None
colors_labels = data[['color', 'label']].drop_duplicates()
toggle = CheckboxWithLegendGroup(
labels=colors_labels.label.tolist(),
active=list(range(len(colors_labels))),
colors=colors_labels.color.tolist(),
width=width // 5,
inline=True if "tablet" in device else False,
)
# TODO replace `eval` with Namespaces
# https://github.com/bokeh/bokeh/pull/6340
toggle.js_on_click(
CustomJS(
args={
'toggle': toggle,
**model_dict
},
code=open(
os.path.join(os.path.dirname(__file__), '../static/js/plotjs',
'togglef.js')).read(),
))
slider = Slider(
start=0.0,
end=15.0,
value=0.0,
step=1.0,
title='Binsize (days)',
max_width=350,
margin=(4, 10, 0, 10),
)
callback = CustomJS(
args={
'slider': slider,
'toggle': toggle,
**model_dict
},
code=open(
os.path.join(os.path.dirname(__file__),
'../static/js/plotjs', 'stackf.js')).read().replace(
'default_zp', str(PHOT_ZP)).replace(
'detect_thresh',
str(PHOT_DETECTION_THRESHOLD)),
)
slider.js_on_change('value', callback)
# Mark the first and last detections
detection_dates = data[data['hasflux']]['mjd']
if len(detection_dates) > 0:
first = round(detection_dates.min(), 6)
last = round(detection_dates.max(), 6)
first_color = "#34b4eb"
last_color = "#8992f5"
midpoint = (upper + lower) / 2
line_top = 5 * upper - 4 * midpoint
line_bottom = 5 * lower - 4 * midpoint
y = np.linspace(line_bottom, line_top, num=5000)
first_r = plot.line(
x=np.full(5000, first),
y=y,
line_alpha=0.5,
line_color=first_color,
line_width=2,
)
plot.add_tools(
HoverTool(
tooltips=[("First detection", f'{first}')],
renderers=[first_r],
))
last_r = plot.line(
x=np.full(5000, last),
y=y,
line_alpha=0.5,
line_color=last_color,
line_width=2,
)
plot.add_tools(
HoverTool(
tooltips=[("Last detection", f'{last}')],
renderers=[last_r],
))
# Mark when spectra were taken
annotate_spec(plot, spectra, lower, upper)
plot_layout = (column(plot, toggle) if "mobile" in device
or "tablet" in device else row(plot, toggle))
layout = column(slider,
plot_layout,
sizing_mode='scale_width',
width=width)
p1 = Panel(child=layout, title='Flux')
# now make the mag light curve
ymax = (np.nanmax((
np.nanmax(data.loc[obsind, 'mag']) if any(obsind) else np.nan,
np.nanmax(data.loc[~obsind, 'lim_mag']) if any(~obsind) else np.nan,
)) + 0.1)
ymin = (np.nanmin((
np.nanmin(data.loc[obsind, 'mag']) if any(obsind) else np.nan,
np.nanmin(data.loc[~obsind, 'lim_mag']) if any(~obsind) else np.nan,
)) - 0.1)
xmin = data['mjd'].min() - 2
xmax = data['mjd'].max() + 2
plot = figure(
aspect_ratio=2.0 if device == "mobile_landscape" else 1.5,
sizing_mode='scale_both',
width=width,
active_drag=active_drag,
tools=tools,
y_range=(ymax, ymin),
x_range=(xmin, xmax),
toolbar_location='above',
toolbar_sticky=True,
x_axis_location='above',
)
# Mark the first and last detections again
detection_dates = data[obsind]['mjd']
if len(detection_dates) > 0:
first = round(detection_dates.min(), 6)
last = round(detection_dates.max(), 6)
midpoint = (ymax + ymin) / 2
line_top = 5 * ymax - 4 * midpoint
line_bottom = 5 * ymin - 4 * midpoint
y = np.linspace(line_bottom, line_top, num=5000)
first_r = plot.line(
x=np.full(5000, first),
y=y,
line_alpha=0.5,
line_color=first_color,
line_width=2,
)
plot.add_tools(
HoverTool(
tooltips=[("First detection", f'{first}')],
renderers=[first_r],
))
last_r = plot.line(
x=np.full(5000, last),
y=y,
line_alpha=0.5,
line_color=last_color,
line_width=2,
)
plot.add_tools(
HoverTool(
tooltips=[("Last detection", f'{last}')],
renderers=[last_r],
point_policy='follow_mouse',
))
# Mark when spectra were taken
annotate_spec(plot, spectra, ymax, ymin)
imhover = HoverTool(tooltips=tooltip_format)
imhover.renderers = []
plot.add_tools(imhover)
model_dict = {}
for i, (label, df) in enumerate(split):
key = f'obs{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='mag',
color='color',
marker='circle',
fill_color='color',
alpha='alpha',
source=ColumnDataSource(df[df['obs']]),
)
imhover.renderers.append(model_dict[key])
unobs_source = df[~df['obs']].copy()
unobs_source.loc[:, 'alpha'] = 0.8
key = f'unobs{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='lim_mag',
color='color',
marker='inverted_triangle',
fill_color='white',
line_color='color',
alpha='alpha',
source=ColumnDataSource(unobs_source),
)
imhover.renderers.append(model_dict[key])
key = f'bin{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='mag',
color='color',
marker='circle',
fill_color='color',
source=ColumnDataSource(data=dict(
mjd=[],
flux=[],
fluxerr=[],
filter=[],
color=[],
lim_mag=[],
mag=[],
magerr=[],
instrument=[],
stacked=[],
)),
)
imhover.renderers.append(model_dict[key])
key = 'obserr' + str(i)
y_err_x = []
y_err_y = []
for d, ro in df[df['obs']].iterrows():
px = ro['mjd']
py = ro['mag']
err = ro['magerr']
y_err_x.append((px, px))
y_err_y.append((py - err, py + err))
model_dict[key] = plot.multi_line(
xs='xs',
ys='ys',
color='color',
alpha='alpha',
source=ColumnDataSource(data=dict(
xs=y_err_x,
ys=y_err_y,
color=df[df['obs']]['color'],
alpha=[1.0] * len(df[df['obs']]),
)),
)
key = f'binerr{i}'
model_dict[key] = plot.multi_line(
xs='xs',
ys='ys',
color='color',
source=ColumnDataSource(data=dict(xs=[], ys=[], color=[])),
)
key = f'unobsbin{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='lim_mag',
color='color',
marker='inverted_triangle',
fill_color='white',
line_color='color',
alpha=0.8,
source=ColumnDataSource(data=dict(
mjd=[],
flux=[],
fluxerr=[],
filter=[],
color=[],
lim_mag=[],
mag=[],
magerr=[],
instrument=[],
stacked=[],
)),
)
imhover.renderers.append(model_dict[key])
key = f'all{i}'
model_dict[key] = ColumnDataSource(df)
key = f'bold{i}'
model_dict[key] = ColumnDataSource(df[[
'mjd',
'flux',
'fluxerr',
'mag',
'magerr',
'filter',
'zp',
'magsys',
'lim_mag',
'stacked',
]])
plot.xaxis.axis_label = 'MJD'
plot.yaxis.axis_label = 'AB mag'
plot.toolbar.logo = None
obj = DBSession().query(Obj).get(obj_id)
if obj.dm is not None:
plot.extra_y_ranges = {
"Absolute Mag": Range1d(start=ymax - obj.dm, end=ymin - obj.dm)
}
plot.add_layout(
LinearAxis(y_range_name="Absolute Mag", axis_label="m - DM"),
'right')
now = Time.now().mjd
plot.extra_x_ranges = {
"Days Ago": Range1d(start=now - xmin, end=now - xmax)
}
plot.add_layout(LinearAxis(x_range_name="Days Ago", axis_label="Days Ago"),
'below')
colors_labels = data[['color', 'label']].drop_duplicates()
toggle = CheckboxWithLegendGroup(
labels=colors_labels.label.tolist(),
active=list(range(len(colors_labels))),
colors=colors_labels.color.tolist(),
width=width // 5,
inline=True if "tablet" in device else False,
)
# TODO replace `eval` with Namespaces
# https://github.com/bokeh/bokeh/pull/6340
toggle.js_on_click(
CustomJS(
args={
'toggle': toggle,
**model_dict
},
code=open(
os.path.join(os.path.dirname(__file__), '../static/js/plotjs',
'togglem.js')).read(),
))
slider = Slider(
start=0.0,
end=15.0,
value=0.0,
step=1.0,
title='Binsize (days)',
max_width=350,
margin=(4, 10, 0, 10),
)
button = Button(label="Export Bold Light Curve to CSV")
button.js_on_click(
CustomJS(
args={
'slider': slider,
'toggle': toggle,
**model_dict
},
code=open(
os.path.join(os.path.dirname(__file__), '../static/js/plotjs',
"download.js")).read().replace('objname',
obj_id).replace(
'default_zp',
str(PHOT_ZP)),
))
# Don't need to expose CSV download on mobile
top_layout = (slider if "mobile" in device or "tablet" in device else row(
slider, button))
callback = CustomJS(
args={
'slider': slider,
'toggle': toggle,
**model_dict
},
code=open(
os.path.join(os.path.dirname(__file__),
'../static/js/plotjs', 'stackm.js')).read().replace(
'default_zp', str(PHOT_ZP)).replace(
'detect_thresh',
str(PHOT_DETECTION_THRESHOLD)),
)
slider.js_on_change('value', callback)
plot_layout = (column(plot, toggle) if "mobile" in device
or "tablet" in device else row(plot, toggle))
layout = column(top_layout,
plot_layout,
sizing_mode='scale_width',
width=width)
p2 = Panel(child=layout, title='Mag')
# now make period plot
# get periods from annotations
annotation_list = obj.get_annotations_readable_by(user)
period_labels = []
period_list = []
for an in annotation_list:
if 'period' in an.data:
period_list.append(an.data['period'])
period_labels.append(an.origin + ": %.9f" % an.data['period'])
if len(period_list) > 0:
period = period_list[0]
else:
period = None
# don't generate if no period annotated
if period is not None:
# bokeh figure for period plotting
period_plot = figure(
aspect_ratio=1.5,
sizing_mode='scale_both',
active_drag='box_zoom',
tools='box_zoom,wheel_zoom,pan,reset,save',
y_range=(ymax, ymin),
x_range=(-0.1, 1.1), # initially one phase
toolbar_location='above',
toolbar_sticky=False,
x_axis_location='below',
)
# axis labels
period_plot.xaxis.axis_label = 'phase'
period_plot.yaxis.axis_label = 'mag'
period_plot.toolbar.logo = None
# do we have a distance modulus (dm)?
obj = DBSession().query(Obj).get(obj_id)
if obj.dm is not None:
period_plot.extra_y_ranges = {
"Absolute Mag": Range1d(start=ymax - obj.dm, end=ymin - obj.dm)
}
period_plot.add_layout(
LinearAxis(y_range_name="Absolute Mag", axis_label="m - DM"),
'right')
# initiate hover tool
period_imhover = HoverTool(tooltips=tooltip_format)
period_imhover.renderers = []
period_plot.add_tools(period_imhover)
# initiate period radio buttons
period_selection = RadioGroup(labels=period_labels, active=0)
phase_selection = RadioGroup(labels=["One phase", "Two phases"],
active=0)
# store all the plot data
period_model_dict = {}
# iterate over each filter
for i, (label, df) in enumerate(split):
# fold x-axis on period in days
df['mjd_folda'] = (df['mjd'] % period) / period
df['mjd_foldb'] = df['mjd_folda'] + 1.0
# phase plotting
for ph in ['a', 'b']:
key = 'fold' + ph + f'{i}'
period_model_dict[key] = period_plot.scatter(
x='mjd_fold' + ph,
y='mag',
color='color',
marker='circle',
fill_color='color',
alpha='alpha',
visible=('a' in ph),
source=ColumnDataSource(
df[df['obs']]), # only visible data
)
# add to hover tool
period_imhover.renderers.append(period_model_dict[key])
# errorbars for phases
key = 'fold' + ph + f'err{i}'
y_err_x = []
y_err_y = []
# get each visible error value
for d, ro in df[df['obs']].iterrows():
px = ro['mjd_fold' + ph]
py = ro['mag']
err = ro['magerr']
# set up error tuples
y_err_x.append((px, px))
y_err_y.append((py - err, py + err))
# plot phase errors
period_model_dict[key] = period_plot.multi_line(
xs='xs',
ys='ys',
color='color',
alpha='alpha',
visible=('a' in ph),
source=ColumnDataSource(data=dict(
xs=y_err_x,
ys=y_err_y,
color=df[df['obs']]['color'],
alpha=[1.0] * len(df[df['obs']]),
)),
)
# toggle for folded photometry
period_toggle = CheckboxWithLegendGroup(
labels=colors_labels.label.tolist(),
active=list(range(len(colors_labels))),
colors=colors_labels.color.tolist(),
width=width // 5,
)
# use javascript to perform toggling on click
# TODO replace `eval` with Namespaces
# https://github.com/bokeh/bokeh/pull/6340
period_toggle.js_on_click(
CustomJS(
args={
'toggle': period_toggle,
'numphases': phase_selection,
'p': period_plot,
**period_model_dict,
},
code=open(
os.path.join(os.path.dirname(__file__),
'../static/js/plotjs', 'togglep.js')).read(),
))
# set up period adjustment text box
period_title = Div(text="Period (days): ")
period_textinput = TextInput(
value=str(period if period is not None else 0.0))
period_textinput.js_on_change(
'value',
CustomJS(
args={
'textinput': period_textinput,
'toggle': period_toggle,
'numphases': phase_selection,
'p': period_plot,
**period_model_dict,
},
code=open(
os.path.join(os.path.dirname(__file__),
'../static/js/plotjs',
'foldphase.js')).read(),
),
)
# a way to modify the period
period_double_button = Button(label="*2")
period_double_button.js_on_click(
CustomJS(
args={'textinput': period_textinput},
code="""
const period = parseFloat(textinput.value);
textinput.value = parseFloat(2.*period).toFixed(9);
""",
))
period_halve_button = Button(label="/2")
period_halve_button.js_on_click(
CustomJS(
args={'textinput': period_textinput},
code="""
const period = parseFloat(textinput.value);
textinput.value = parseFloat(period/2.).toFixed(9);
""",
))
# a way to select the period
period_selection.js_on_click(
CustomJS(
args={
'textinput': period_textinput,
'periods': period_list
},
code="""
textinput.value = parseFloat(periods[this.active]).toFixed(9);
""",
))
phase_selection.js_on_click(
CustomJS(
args={
'textinput': period_textinput,
'toggle': period_toggle,
'numphases': phase_selection,
'p': period_plot,
**period_model_dict,
},
code=open(
os.path.join(os.path.dirname(__file__),
'../static/js/plotjs',
'foldphase.js')).read(),
))
# layout
period_column = column(
period_toggle,
period_title,
period_textinput,
period_selection,
row(period_double_button, period_halve_button, width=180),
phase_selection,
width=180,
)
period_layout = column(
row(period_plot, period_column),
sizing_mode='scale_width',
width=width,
)
# Period panel
p3 = Panel(child=period_layout, title='Period')
# tabs for mag, flux, period
tabs = Tabs(tabs=[p2, p1, p3],
width=width,
height=height,
sizing_mode='fixed')
else:
# tabs for mag, flux
tabs = Tabs(tabs=[p2, p1],
width=width,
height=height,
sizing_mode='fixed')
return bokeh_embed.json_item(tabs)