def load_qa(self):
cam = self.selected_arm+str(self.selected_spectrograph)
mergedqa = QLFModels().get_output(self.selected_process_id, cam)
check_fibers = mergedqa['TASKS']['CHECK_FIBERS']
gen_info = mergedqa['GENERAL_INFO']
flavor = mergedqa['FLAVOR']
nrg = check_fibers['PARAMS']['XYSHIFTS_NORMAL_RANGE']
wrg = check_fibers['PARAMS']['XYSHIFTS_WARN_RANGE']
info_col = Title().write_description('xyshifts')
# Prepare tables
current_exposures = check_fibers['METRICS']['XYSHIFTS']
program = gen_info['PROGRAM'].upper()
if flavor == 'science':
reference_exposures = check_fibers['PARAMS']['XYSHIFTS_' +
program + '_REF']
else:
reference_exposures = []
keynames = ["X", "Y"]
metric = Table().reference_table(keynames, current_exposures, reference_exposures)
alert = Table().alert_table(nrg, wrg)
layout = column(info_col, Div(),
metric, alert,
css_classes=["display-grid"])
return file_html(layout, CDN, "XYSHIFTS")
def load_qa(self):
cam = self.selected_arm+str(self.selected_spectrograph)
mergedqa = QLFModels().get_output(self.selected_process_id, cam)
check_ccds = mergedqa['TASKS']['CHECK_CCDs']
getbias = check_ccds['METRICS']
nrg = check_ccds['PARAMS']['BIAS_AMP_NORMAL_RANGE']
wrg = check_ccds['PARAMS']['BIAS_AMP_WARN_RANGE']
if mergedqa['FLAVOR'].upper() == 'SCIENCE':
program = mergedqa['GENERAL_INFO']['PROGRAM'].upper()
program_prefix = '_'+program
else:
program_prefix = ''
refexp = mergedqa['TASKS']['CHECK_CCDs']['PARAMS']['BIAS_AMP' +
program_prefix+'_REF']
# PATCH
p = Patch().plot_amp(
dz=getbias["BIAS_AMP"],
refexp=refexp,
name="BIAS_AMP",
description="Average bias value (photon counts)",
wrg=wrg
)
p_status = Patch().plot_amp(
dz=getbias["BIAS_AMP"],
refexp=refexp,
name="BIAS_AMP (STATUS)",
description="Average bias value (photon counts)",
wrg=wrg,
nrg=nrg,
status_plot=True,
)
# INFO TABLES:
info_col = Title().write_description('getbias')
# Prepare tables
current_exposures = check_ccds['METRICS']['BIAS_AMP']
gen_info = mergedqa['GENERAL_INFO']
flavor = mergedqa["FLAVOR"]
if flavor == 'science':
program = gen_info['PROGRAM'].upper()
reference_exposures = check_ccds['PARAMS']['BIAS_AMP_' +
program + '_REF']
else:
reference_exposures = check_ccds['PARAMS']['BIAS_AMP_REF']
keynames = ["BIAS_AMP" for i in range(len(current_exposures))]
table = Table().single_table(keynames, current_exposures, reference_exposures, nrg, wrg)
layout = column(info_col, Div(),
table, Div(),
column(p, sizing_mode='scale_both'), #css_classes=["main-one"]),
column(p_status, sizing_mode='scale_both'),
css_classes=["display-grid"])
return file_html(layout, CDN, "GETBIAS")
def load_qa(self):
cam = self.selected_arm+str(self.selected_spectrograph)
mergedqa = QLFModels().get_output(self.selected_process_id, cam)
gen_info = mergedqa['GENERAL_INFO']
check_spectra = mergedqa['TASKS']['CHECK_SPECTRA']
nrg = check_spectra['PARAMS']['SKYRBAND_NORMAL_RANGE']
wrg = check_spectra['PARAMS']['SKYRBAND_WARN_RANGE']
info_col = Title().write_description('skyR')
# Prepare Tables
current_exposures = [check_spectra['METRICS']['SKYRBAND']]
program = gen_info['PROGRAM'].upper()
reference_exposures = check_spectra['PARAMS']['SKYRBAND_' +
program + '_REF']
keynames = ["SKYRBAND" for i in range(len(current_exposures))]
table = Table().single_table(keynames, current_exposures, reference_exposures, nrg, wrg)
layout = column(info_col, Div(),
table, Div(),
css_classes=["display-grid"])
return file_html(layout, CDN, "SKYR")
def load_qa(self):
cam = self.selected_arm + str(self.selected_spectrograph)
mergedqa = QLFModels().get_output(self.selected_process_id, cam)
check_flat = mergedqa['TASKS']['CHECK_FIBERFLAT']
flat = check_flat['METRICS']
nrg = check_flat['PARAMS']['CHECKFLAT_NORMAL_RANGE']
wrg = check_flat['PARAMS']['CHECKFLAT_WARN_RANGE']
info_col = Title().write_description('fiberflat')
# Prepare tables
current_exposures = check_flat['METRICS']['CHECKFLAT']
reference_exposures = check_flat['PARAMS']['CHECKFLAT_REF']
keynames = ["CHECKFLAT"]
table = Table().single_table(keynames, [current_exposures],
reference_exposures, nrg, wrg)
layout = column(info_col,
Div(),
table,
Div(),
css_classes=["display-grid"])
return file_html(layout, CDN, "FIBERFLAT")
def load_qa(self):
cam = self.selected_arm + str(self.selected_spectrograph)
mergedqa = QLFModels().get_output(self.selected_process_id, cam)
check_arc = mergedqa['TASKS']['CHECK_ARC']
nrg = check_arc['PARAMS']['CHECKARC_NORMAL_RANGE']
wrg = check_arc['PARAMS']['CHECKARC_WARN_RANGE']
info_col = Title().write_description('arc')
# Prepare tables
current_exposures = check_arc['METRICS']['CHECKARC']
reference_exposures = check_arc['PARAMS']['CHECKARC_REF']
keynames = ["CHECKARC"]
table = Table().single_table(keynames, [current_exposures],
reference_exposures, list(map(int, nrg)),
list(map(int, wrg)))
layout = column(info_col,
Div(),
table,
Div(),
css_classes=["display-grid"])
return file_html(layout, CDN, "ARC")
def load_qa(self):
cam = self.selected_arm + str(self.selected_spectrograph)
mergedqa = QLFModels().get_output(self.selected_process_id, cam)
check_ccds = mergedqa['TASKS']['CHECK_CCDs']
getrms = check_ccds['METRICS']
nrg = check_ccds['PARAMS']['NOISE_AMP_NORMAL_RANGE']
wrg = check_ccds['PARAMS']['NOISE_AMP_WARN_RANGE']
if mergedqa['FLAVOR'].upper() == 'SCIENCE':
program = mergedqa['GENERAL_INFO']['PROGRAM'].upper()
program_prefix = '_' + program
else:
program_prefix = ''
refexp = mergedqa['TASKS']['CHECK_CCDs']['PARAMS']['NOISE_AMP' +
program_prefix +
'_REF']
# amp 1
p = Patch().plot_amp(dz=getrms["NOISE_AMP"],
refexp=refexp,
name="NOISE_AMP",
description="NOISE per Amp (photon counts)",
wrg=wrg)
# amp 2
p2 = Patch().plot_amp(
dz=getrms["NOISE_OVERSCAN_AMP"],
refexp=refexp,
name="NOISE_OVERSCAN_AMP",
description="NOISE Overscan per Amp (photon counts)",
wrg=wrg)
info_col = Title().write_description('getrms')
# Prepare tables
current_exposures = check_ccds['METRICS']['NOISE_AMP']
gen_info = mergedqa['GENERAL_INFO']
flavor = mergedqa["FLAVOR"]
if flavor == 'science':
program = gen_info['PROGRAM'].upper()
reference_exposures = check_ccds['PARAMS']['LITFRAC_AMP_' +
program + '_REF']
else:
reference_exposures = check_ccds['PARAMS']['LITFRAC_AMP_REF']
keynames = ["NOISE_AMP" for i in range(len(current_exposures))]
metric = Table().reference_table(keynames, current_exposures,
reference_exposures)
alert = Table().alert_table(nrg, wrg)
layout = column(info_col,
Div(),
metric,
alert,
column(p, sizing_mode='scale_both'),
column(p2, sizing_mode='scale_both'),
css_classes=["display-grid"])
return file_html(layout, CDN, "GETRMS")
def load_qa(self):
process_id = self.selected_process_id
process = Process.objects.get(pk=process_id)
exposure = process.exposure
fmap = Fibermap.objects.filter(exposure=exposure)[0]
src = self.data_source(fmap)
p = self.wedge_plot(self.selected_arm, fmap, common_source=src)
info_col = Title().write_description('globalfiber')
layout = column([info_col, p], sizing_mode='scale_width')
return file_html(layout, CDN, "Global Fiber")
def load_qa(self):
cam = self.selected_arm + str(self.selected_spectrograph)
mergedqa = QLFModels().get_output(self.selected_process_id, cam)
gen_info = mergedqa['GENERAL_INFO']
flavor = mergedqa['FLAVOR']
if flavor == "science":
ra = gen_info['RA']
dec = gen_info['DEC']
check_ccds = mergedqa['TASKS']['CHECK_CCDs']
xwsigma = check_ccds['METRICS']['XWSIGMA']
xw_amp = check_ccds['METRICS']['XWSIGMA_AMP']
xw_fib = check_ccds['METRICS']['XWSIGMA_FIB']
nrg = check_ccds['PARAMS']['XWSIGMA_NORMAL_RANGE']
wrg = check_ccds['PARAMS']['XWSIGMA_WARN_RANGE']
obj_type = sort_obj(gen_info) # [""]*500
if mergedqa['FLAVOR'].upper() == 'SCIENCE':
program = mergedqa['GENERAL_INFO']['PROGRAM'].upper()
program_prefix = '_' + program
else:
program_prefix = ''
xw_ref = check_ccds['PARAMS']['XWSIGMA' + program_prefix + '_REF']
xsigma = xw_fib[0]
wsigma = xw_fib[1]
delta_rg = wrg[1] - wrg[0]
hist_rg = (wrg[0] - 0.1 * delta_rg, wrg[1] + 0.1 * delta_rg)
my_palette = get_palette("RdYlBu_r")
xfiber = np.arange(len(xsigma))
wfiber = np.arange(len(wsigma))
if mergedqa['FLAVOR'].upper() != 'SCIENCE':
ra = np.full(500, np.nan)
dec = np.full(500, np.nan)
source = ColumnDataSource(
data={
'x1': ra,
'y1': dec,
'xsigma': xsigma,
'wsigma': wsigma,
'delta_xsigma': np.array(xsigma) - xw_ref[0],
'delta_wsigma': np.array(wsigma) - xw_ref[1],
'xref': [xw_ref[0]] * len(xsigma),
'wref': [xw_ref[1]] * len(xsigma),
'xfiber': xfiber,
'wfiber': wfiber,
'OBJ_TYPE': obj_type,
'left': np.arange(0, 500) - 0.4,
'right': np.arange(0, 500) + 0.4,
'bottom': [0] * 500
})
xsigma_tooltip = """
<div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">XSigma: </span>
<span style="font-size: 1vw; color: #515151">@xsigma</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Reference: </span>
<span style="font-size: 1vw; color: #515151">@xref</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Obj Type: </span>
<span style="font-size: 1vw; color: #515151;">@OBJ_TYPE</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">RA: </span>
<span style="font-size: 1vw; color: #515151;">@x1</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">DEC: </span>
<span style="font-size: 1vw; color: #515151;">@y1</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">FIBER ID: </span>
<span style="font-size: 1vw; color: #515151;">@xfiber</span>
</div>
</div>
"""
wsigma_tooltip = """
<div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">WSigma: </span>
<span style="font-size: 1vw; color: #515151">@wsigma</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Reference: </span>
<span style="font-size: 1vw; color: #515151">@wref</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Obj Type: </span>
<span style="font-size: 1vw; color: #515151;">@OBJ_TYPE</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">RA: </span>
<span style="font-size: 1vw; color: #515151;">@x1</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">DEC: </span>
<span style="font-size: 1vw; color: #515151;">@y1</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">FIBER ID: </span>
<span style="font-size: 1vw; color: #515151;">@wfiber</span>
</div>
</div>
"""
url = "http://legacysurvey.org/viewer?ra=@ra&dec=@dec&zoom=16&layer=decals-dr5"
# determining the position of selected cam fibers:
obj_type = sort_obj(gen_info)
# ---------------------------------
if flavor == "science":
# axes limit
xmin, xmax = [min(ra[:]), max(ra[:])]
ymin, ymax = [min(dec[:]), max(dec[:])]
xfac, yfac = [(xmax - xmin) * 0.06, (ymax - ymin) * 0.06]
left, right = xmin - xfac, xmax + xfac
bottom, top = ymin - yfac, ymax + yfac
low, high = wrg
xmapper = LinearColorMapper(palette=my_palette,
low=low,
high=high,
nan_color="darkgrey")
wmapper = LinearColorMapper(palette=my_palette,
low=low,
high=high,
nan_color="darkgrey")
# ============
# XSIGMA WEDGE
radius = 0.0165
radius_hover = 0.02
# centralize wedges in plots:
ra_center = 0.5 * (max(ra) + min(ra))
dec_center = 0.5 * (max(dec) + min(dec))
xrange_wedge = Range1d(start=ra_center + .95, end=ra_center - .95)
yrange_wedge = Range1d(start=dec_center + .82,
end=dec_center - .82)
wedge_plot_x = Plot2d(x_range=xrange_wedge,
y_range=yrange_wedge,
x_label="RA",
y_label="DEC",
tooltip=xsigma_tooltip,
title="XSIGMA",
width=500,
height=380,
yscale="auto").wedge(
source,
x='x1',
y='y1',
field='delta_xsigma',
mapper=xmapper,
colorbar_title='xsigma').plot
taptool = wedge_plot_x.select(type=TapTool)
taptool.callback = OpenURL(url=url)
# ============
# WSIGMA WEDGE
wedge_plot_w = Plot2d(x_range=xrange_wedge,
y_range=yrange_wedge,
x_label="RA",
y_label="DEC",
tooltip=wsigma_tooltip,
title="WSIGMA",
width=500,
height=380,
yscale="auto").wedge(
source,
x='x1',
y='y1',
field='delta_wsigma',
mapper=wmapper,
colorbar_title='wsigma').plot
taptool = wedge_plot_w.select(type=TapTool)
taptool.callback = OpenURL(url=url)
# ================================
# Stat histogram
# x_fiber_hist
d_yplt = (max(xsigma) - min(xsigma)) * 0.1
yrange = [0, max(xsigma) + d_yplt]
xhist = Plot2d(
yrange,
x_label="Fiber number",
y_label="X std dev (number of pixels)",
tooltip=xsigma_tooltip,
title="",
width=600,
height=300,
yscale="auto",
hover_mode="vline",
).quad(
source,
top='xsigma',
)
# w_fiber_hist
d_yplt = (max(wsigma) - min(wsigma)) * 0.1
yrange = [0, max(wsigma) + d_yplt]
whist = Plot2d(
yrange,
x_label="Fiber number",
y_label="W std dev (number of pixels)",
tooltip=xsigma_tooltip,
title="",
width=600,
height=300,
yscale="auto",
hover_mode="vline",
).quad(
source,
top='wsigma',
)
# ================================
# Stat histogram
def histpar(yscale, hist):
if yscale == 'log':
ylabel = "Frequency + 1"
yrange = (1, 11**(int(np.log10(max(hist))) + 1))
bottomval = 'bottomplusone'
histval = 'histplusone'
else:
ylabel = "Frequency"
yrange = (0.0 * max(hist), 1.1 * max(hist))
bottomval = 'bottom'
histval = 'hist'
return [ylabel, yrange, bottomval, histval]
xhistlabel = "XSIGMA"
yscale = "auto"
hist_tooltip_x = """
<div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Frequency: </span>
<span style="font-size: 1vw; color: #515151">@hist</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">XSIGMA: </span>
<span style="font-size: 1vw; color: #515151;">[@left, @right]</span>
</div>
</div>
"""
hist, edges = np.histogram(xsigma, 'sqrt')
source_hist = ColumnDataSource(
data={
'hist': hist,
'histplusone': hist + 1,
'bottom': [0] * len(hist),
'bottomplusone': [1] * len(hist),
'left': edges[:-1],
'right': edges[1:]
})
ylabel, yrange, bottomval, histval = histpar(yscale, hist)
p_hist_x = Plot2d(
x_label=xhistlabel,
y_label=ylabel,
tooltip=hist_tooltip_x,
title="",
width=600,
height=300,
yscale="auto",
y_range=yrange,
x_range=(hist_rg[0] + xw_ref[0], hist_rg[1] + xw_ref[0]),
hover_mode="vline",
).quad(
source_hist,
top=histval,
bottom=bottomval,
line_width=0.4,
)
# Histogram 2
xhistlabel = "WSIGMA"
hist_tooltip_w = """
<div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Frequency: </span>
<span style="font-size: 1vw; color: #515151">@hist</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">WSIGMA: </span>
<span style="font-size: 1vw; color: #515151;">[@left, @right]</span>
</div>
</div>
"""
hist, edges = np.histogram(wsigma, 'sqrt')
source_hist = ColumnDataSource(
data={
'hist': hist,
'histplusone': hist + 1,
'bottom': [0] * len(hist),
'bottomplusone': [1] * len(hist),
'left': edges[:-1],
'right': edges[1:]
})
ylabel, yrange, bottomval, histval = histpar(yscale, hist)
yrangew = yrange
p_hist_w = Plot2d(
x_label=xhistlabel,
y_label=ylabel,
tooltip=hist_tooltip_w,
title="",
width=600,
height=300,
yscale="auto",
y_range=yrange,
x_range=(hist_rg[0] + xw_ref[1], hist_rg[1] + xw_ref[1]),
hover_mode="vline",
).quad(
source_hist,
top=histval,
bottom=bottomval,
line_width=0.8,
)
# --------------------------------------------------------------
# vlines ranges:
bname = 'XWSIGMA'
for ialert in nrg: # par[bname+'_NORMAL_RANGE']:
spans = Span(location=ialert + xw_ref[0],
dimension='height',
line_color='green',
line_dash='dashed',
line_width=2)
p_hist_x.add_layout(spans)
my_label = Label(x=ialert + xw_ref[0],
y=yrange[-1] / 2.2,
y_units='data',
text='Normal Range',
text_color='green',
angle=np.pi / 2.)
p_hist_x.add_layout(my_label)
for ialert in wrg: # par[bname+'_WARN_RANGE']:
spans = Span(location=ialert + xw_ref[0],
dimension='height',
line_color='tomato',
line_dash='dotdash',
line_width=2)
p_hist_x.add_layout(spans)
my_label = Label(x=ialert + xw_ref[0],
y=yrange[-1] / 2.2,
y_units='data',
text='Warning Range',
text_color='tomato',
angle=np.pi / 2.)
p_hist_x.add_layout(my_label)
for ialert in nrg: # par[bname+'_NORMAL_RANGE']:
spans = Span(location=ialert + xw_ref[1],
dimension='height',
line_color='green',
line_dash='dashed',
line_width=2)
p_hist_w.add_layout(spans)
my_label = Label(x=ialert + xw_ref[1],
y=yrangew[-1] / 2.2,
y_units='data',
text='Normal Range',
text_color='green',
angle=np.pi / 2.)
p_hist_w.add_layout(my_label)
for ialert in wrg: # par[bname+'_WARN_RANGE']:
spans = Span(location=ialert + xw_ref[1],
dimension='height',
line_color='tomato',
line_dash='dotdash',
line_width=2)
p_hist_w.add_layout(spans)
my_label = Label(x=ialert + xw_ref[1],
y=yrangew[-1] / 2.2,
y_units='data',
text='Warning Range',
text_color='tomato',
angle=np.pi / 2.)
p_hist_w.add_layout(my_label)
# amp 1
xamp = Patch().plot_amp(
dz=xw_amp[0],
refexp=[xw_ref[0]] * 4,
name="XSIGMA AMP",
description="X standard deviation per Amp (number of pixels)",
wrg=wrg)
# amp 2
wamp = Patch().plot_amp(
dz=xw_amp[1],
refexp=[xw_ref[1]] * 4,
name="WSIGMA AMP",
description="W standard deviation per Amp (number of pixels)",
wrg=wrg)
# -------------------------------------------------------------------------
info_col = Title().write_description('xwsigma')
current_exposures = check_ccds['METRICS']['XWSIGMA']
alert_x = Table().alert_table(nrg, wrg)
alert_w = Table().alert_table(nrg, wrg)
if flavor == 'science':
program = gen_info['PROGRAM'].upper()
reference_exposures = check_ccds['PARAMS']['XWSIGMA_' + program +
'_REF']
keynames = ["XSIGMA"]
x_metric = Table().reference_table(keynames,
[current_exposures[0]],
[reference_exposures[0]])
keynames = ["WSIGMA"]
w_metric = Table().reference_table(keynames,
[current_exposures[1]],
[reference_exposures[1]])
layout = column(
info_col,
widgetbox(Div(), css_classes=["tableranges"]),
widgetbox(
Div(text=
'<h2 align=center style="text-align:center;"> XSIGMA </h2>'
)),
widgetbox(
Div(text=
'<h2 align=center style="text-align:center;"> WSIGMA </h2>'
)),
x_metric,
w_metric,
alert_x,
alert_w,
column(wedge_plot_x, sizing_mode='scale_both'),
column(wedge_plot_w, sizing_mode='scale_both'),
column(xhist, sizing_mode='scale_both'),
column(whist, sizing_mode='scale_both'),
column(p_hist_x, sizing_mode='scale_both'),
column(p_hist_w, sizing_mode='scale_both'),
column(xamp, sizing_mode='scale_both'),
column(wamp, sizing_mode='scale_both'),
css_classes=["display-grid"],
sizing_mode='scale_width')
else:
reference_exposures = check_ccds['PARAMS']['XWSIGMA_REF']
keynames = ["XSIGMA"]
x_metric = Table().reference_table(keynames,
[current_exposures[0]],
[reference_exposures[0]])
keynames = ["WSIGMA"]
w_metric = Table().reference_table(keynames,
[current_exposures[1]],
[reference_exposures[1]])
layout = column(
info_col,
widgetbox(Div(), css_classes=["tableranges"]),
widgetbox(
Div(text=
'<h2 align=center style="text-align:center;"> XSIGMA </h2>'
)),
widgetbox(
Div(text=
'<h2 align=center style="text-align:center;"> WSIGMA </h2>'
)),
x_metric,
w_metric,
alert_x,
alert_w,
column(xhist, sizing_mode='scale_both'),
column(whist, sizing_mode='scale_both'),
column(p_hist_x, sizing_mode='scale_both'),
column(p_hist_w, sizing_mode='scale_both'),
column(xamp, sizing_mode='scale_both'),
column(wamp, sizing_mode='scale_both'),
css_classes=["display-grid"],
sizing_mode='scale_width')
return file_html(layout, CDN, "XWSIGMA")
def load_qa(self):
cam = self.selected_arm + str(self.selected_spectrograph)
mergedqa = QLFModels().get_output(self.selected_process_id, cam)
gen_info = mergedqa['GENERAL_INFO']
ra = gen_info['RA']
dec = gen_info['DEC']
check_spectra = mergedqa['TASKS']['CHECK_SPECTRA']
skycont = check_spectra['METRICS']
sky = skycont['SKYCONT_FIBER']
skyfibers = gen_info['SKY_FIBERID']
nrg = check_spectra['PARAMS']['SKYCONT_NORMAL_RANGE']
wrg = check_spectra['PARAMS']['SKYCONT_WARN_RANGE']
current_exposures = [check_spectra['METRICS']['SKYCONT']]
program = gen_info['PROGRAM'].upper()
reference_exposures = check_spectra['PARAMS']['SKYCONT_' + program +
'_REF']
ra_sky = [ra[i % 500] for i in skyfibers]
dec_sky = [dec[i % 500] for i in skyfibers]
my_palette = get_palette("RdYlBu_r")
skc_tooltips = """
<div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">SKY CONT: </span>
<span style="font-size: 1vw; color: #515151;">@skycont</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">RA: </span>
<span style="font-size: 1vw; color: #515151;">@ra</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">DEC: </span>
<span style="font-size: 1vw; color: #515151;">@dec</span>
</div>
</div>
"""
url = "http://legacysurvey.org/viewer?ra=@ra&dec=@dec&zoom=16&layer=decals-dr5"
qlf_fiberid = np.arange(0, 500)
# sky continuum per sky fiber averaged over two continuum regions,
# 'n' is number of sky fibers
ra_not, dec_not = [], []
fiber_mod = []
for fiber in skyfibers:
fiber_mod.append(fiber % 500)
for i in range(500):
if i not in fiber_mod:
ra_not.append(ra[i])
dec_not.append(dec[i])
source = ColumnDataSource(
data={
'skycont': sky,
'delta_skycont': np.array(sky) - reference_exposures,
'fiberid': skyfibers,
'ra': ra_sky,
'dec': dec_sky
})
source_not = ColumnDataSource(data={
'ra': ra_not,
'dec': dec_not,
'skycont': [''] * len(dec_not)
})
low, high = wrg
mapper = LinearColorMapper(
palette=my_palette,
low=low, #np.min(sky),
high=high, #np.max(sky),
nan_color='darkgrey')
radius = 0.0165 # 0.013
radius_hover = 0.02 # 0.015
# centralize wedges in plots:
ra_center = 0.5 * (max(ra) + min(ra))
dec_center = 0.5 * (max(dec) + min(dec))
xrange_wedge = Range1d(start=ra_center + .95, end=ra_center - .95)
yrange_wedge = Range1d(start=dec_center + .82, end=dec_center - .82)
wedge_plot = Plot2d(x_range=xrange_wedge,
y_range=yrange_wedge,
x_label="RA",
y_label="DEC",
tooltip=skc_tooltips,
title="SKY_CONT",
width=500,
height=380,
yscale="auto").wedge(
source,
x='ra',
y='dec',
field='delta_skycont',
mapper=mapper,
).wedge(
source_not,
x='ra',
y='dec',
).plot
taptool = wedge_plot.select(type=TapTool)
taptool.callback = OpenURL(url=url)
# infos
info_col = Title().write_description('skycont')
# Prepare tables
keynames = ["SKYCONT" for i in range(len(current_exposures))]
table = Table().single_table(keynames, current_exposures,
reference_exposures, nrg, wrg)
layout = column(info_col,
Div(),
table,
Div(),
column(wedge_plot,
sizing_mode='scale_both',
css_classes=["main-one"]),
css_classes=["display-grid"])
# End of Bokeh Block
return file_html(layout, CDN, "SKYCONT")
def load_qa(self):
cam = self.selected_arm + str(self.selected_spectrograph)
mergedqa = QLFModels().get_output(self.selected_process_id, cam)
gen_info = mergedqa['GENERAL_INFO']
check_spectra = mergedqa['TASKS']['CHECK_SPECTRA']
std_fiberid = mergedqa['GENERAL_INFO']['STAR_FIBERID']
nrg = check_spectra['PARAMS']['DELTAMAG_TGT_NORMAL_RANGE']
wrg = check_spectra['PARAMS']['DELTAMAG_TGT_WARN_RANGE']
fiber_mag = np.array(mergedqa['GENERAL_INFO']['FIBER_MAGS'])
if 'b' in cam:
arm_id = 0
elif 'r' in cam:
arm_id = 1
else:
arm_id = 2
fiber_mag = fiber_mag[arm_id] #.flatten()
tooltip = """
<div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">INTEG: </span>
<span style="font-size: 1vw; color: #515151;">@integ</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">FIBER ID: </span>
<span style="font-size: 1vw; color: #515151;">@x</span>
</div>
</div>
"""
source = ColumnDataSource(
data={
'integ': [i if i > -999 else np.nan for i in fiber_mag],
'x': np.arange(len(fiber_mag)),
})
print(len(fiber_mag))
yrange = [0, 1.1 * max(fiber_mag)]
fiber_hist = Plot2d(
yrange,
x_label="Fibers",
y_label="Integral (counts)",
tooltip=tooltip,
title="",
width=600,
height=400,
yscale="auto",
hover_mode="vline",
).vbar(
source,
y="integ",
)
info_col = Title().write_description('integ')
# Reading obj_type
objlist = mergedqa["TASKS"]["CHECK_SPECTRA"]["METRICS"]["OBJLIST"]
if 'SKY' in objlist:
objlist.remove('SKY')
# Prepare tables
current_exposures = check_spectra['METRICS']['DELTAMAG_TGT']
program = gen_info['PROGRAM'].upper()
reference_exposures = check_spectra['PARAMS']['DELTAMAG_TGT_' +
program + '_REF']
keynames = ["DELTAMAG_TGT" + " ({})".format(i) for i in objlist]
table = Table().single_table(keynames, current_exposures,
reference_exposures, nrg, wrg)
layout = column(info_col,
Div(),
table,
Div(),
column(fiber_hist,
sizing_mode='scale_both',
css_classes=["main-one"]),
css_classes=["display-grid"])
return file_html(layout, CDN, "INTEG")
def load_qa(self):
cam = self.selected_arm+str(self.selected_spectrograph)
mergedqa = QLFModels().get_output(self.selected_process_id, cam)
check_spectra = mergedqa['TASKS']['CHECK_SPECTRA']
gen_info = mergedqa['GENERAL_INFO']
ra = gen_info['RA']
dec = gen_info['DEC']
nrg = check_spectra['PARAMS']['PEAKCOUNT_NORMAL_RANGE']
wrg = check_spectra['PARAMS']['PEAKCOUNT_WARN_RANGE']
current_exposures = [check_spectra['METRICS']['PEAKCOUNT']]
program = gen_info['PROGRAM'].upper()
reference_exposures = check_spectra['PARAMS']['PEAKCOUNT_' +
program + '_REF']
obj_type = sort_obj(gen_info)
my_palette = get_palette("RdYlBu_r")
peak_tooltip = """
<div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">PEAKCOUNT: </span>
<span style="font-size: 1vw; color: #515151">@peakcount_fib</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">RA: </span>
<span style="font-size: 1vw; color: #515151;">@x1</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">DEC: </span>
<span style="font-size: 1vw; color: #515151;">@y1</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Obj Type: </span>
<span style="font-size: 1vw; color: #515151;">@OBJ_TYPE</span>
</div>
</div>
"""
url = "http://legacysurvey.org/viewer?ra=@ra&dec=@dec&zoom=16&layer=decals-dr5"
qlf_fiberid = np.arange(0, 500)
peak_hover = HoverTool(tooltips=peak_tooltip)
peakcount_fib = check_spectra['METRICS']['PEAKCOUNT_FIB']
source = ColumnDataSource(data={
'x1': ra,
'y1': dec,
'peakcount_fib': peakcount_fib,
'delta_peakcount_fib': np.array(peakcount_fib)-reference_exposures,
'QLF_FIBERID': qlf_fiberid,
'OBJ_TYPE': obj_type,
})
low, high = wrg
mapper = LinearColorMapper(palette=my_palette,
low=low, #0.98*np.min(peakcount_fib),
high=high, #1.02*np.max(peakcount_fib))
nan_color='darkgrey')
radius = 0.0165
radius_hover = 0.02
# centralize wedges in plots:
ra_center=0.5*(max(ra)+min(ra))
dec_center=0.5*(max(dec)+min(dec))
xrange_wedge = Range1d(start=ra_center + .95, end=ra_center-.95)
yrange_wedge = Range1d(start=dec_center+.82, end=dec_center-.82)
# axes limit
xmin, xmax = [min(gen_info['RA'][:]), max(gen_info['RA'][:])]
ymin, ymax = [min(gen_info['DEC'][:]), max(gen_info['DEC'][:])]
xfac, yfac = [(xmax-xmin)*0.06, (ymax-ymin)*0.06]
left, right = xmin - xfac, xmax+xfac
bottom, top = ymin-yfac, ymax+yfac
wedge_plot = Plot2d(
x_range=xrange_wedge,
y_range=yrange_wedge,
x_label="RA",
y_label="DEC",
tooltip=peak_tooltip,
title="PEAKCOUNT",
width=500,
height=380,
).wedge(
source,
x='x1',
y='y1',
field='delta_peakcount_fib',
mapper=mapper,
).plot
info_col = Title().write_description('skypeak')
# ================================
# histogram
hist_tooltip = """
<div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Frequency: </span>
<span style="font-size: 1vw; color: #515151">@hist</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Peakcount: </span>
<span style="font-size: 1vw; color: #515151;">[@left, @right]</span>
</div>
</div>
"""
hist, edges = np.histogram(peakcount_fib, bins="sqrt")
source_hist = ColumnDataSource(data={
'hist': hist,
'histplusone': hist+1,
'bottom': [0] * len(hist),
'bottomplusone': [1]*len(hist),
'left': edges[:-1],
'right': edges[1:]
})
p_hist = Plot2d(
y_range=(1, 11**(int(np.log10(max(hist)))+1)),
x_label='PEAKCOUNT',
y_label='Frequency + 1',
tooltip=hist_tooltip,
title="",
width=550,
height=300,
yscale="log",
hover_mode="vline",
).quad(
source_hist,
top='histplusone',
bottom='bottomplusone',
line_width=1,
)
# Prepare tables
keynames = ["PEAKCOUNT" for i in range(len(current_exposures))]
metric = Table().reference_table(keynames, current_exposures, reference_exposures)
alert = Table().alert_table(nrg, wrg)
layout = column(info_col, Div(),
metric, alert,
column(wedge_plot, sizing_mode='scale_both'),
column(p_hist, sizing_mode='scale_both'),
css_classes=["display-grid"])
return file_html(layout, CDN, "SKYPEAK")
def load_qa(self):
cam = self.selected_arm + str(self.selected_spectrograph)
mergedqa = QLFModels().get_output(self.selected_process_id, cam)
# list of available object in petal
objlist = mergedqa["TASKS"]["CHECK_SPECTRA"]["METRICS"]["OBJLIST"]
if 'SKY' in objlist:
objlist.remove('SKY')
gen_info = mergedqa['GENERAL_INFO']
ra = gen_info['RA']
dec = gen_info['DEC']
check_spectra = mergedqa['TASKS']['CHECK_SPECTRA']
snr = check_spectra['METRICS']
nrg = check_spectra['PARAMS']['FIDSNR_TGT_NORMAL_RANGE']
wrg = check_spectra['PARAMS']['FIDSNR_TGT_WARN_RANGE']
# Object identification in fibers:
obj_fiber = sort_obj(gen_info)
qlf_obj = ['ELG', 'LRG', 'QSO', 'STAR']
avobj = ['STAR' if x == 'STD' else x for x in objlist]
obj_idx = {}
for o in qlf_obj:
try:
obj_idx.update({o: avobj.index(o)})
except:
obj_idx.update({o: None})
try:
exptime = gen_info['EXPTIME']
name_warn = ''
except:
exptime = 1000
name_warn = ' (exptime fixed)'
# Sort objects for QLF:
obj_idx = {}
for o in qlf_obj:
try:
obj_idx.update({o: avobj.index(o)})
except:
obj_idx.update({o: None})
def good_idx(mag, snr):
# Filtering measurements with good SNR & good MAG
# Treating None (inf and Nan already treated in db)
mag_2 = np.array([-9998 if x is None else x for x in mag])
snr_2 = np.array([-9998 if x is None else x for x in snr])
idx = np.arange(len(snr_2))
# Filtering values with good mag AND snr
return list(idx[(mag_2 > -999) & (snr_2 > 0)])
# Treating bad snr and mag
mag_snr = {}
for o in avobj:
snr_ql, mag_ql = snr['SNR_MAG_TGT'][obj_idx[o]]
idx = good_idx(mag_ql, snr_ql)
x = [mag_ql[i] for i in idx]
y = [snr_ql[i] for i in idx]
mag_snr.update({o: [y, x]})
# Preparing xy_plot data:
if obj_idx['ELG'] is not None:
elg_snr = mag_snr['ELG']
if obj_idx['LRG'] is not None:
lrg_snr = mag_snr['LRG']
if obj_idx['QSO'] is not None:
qso_snr = mag_snr['QSO']
if obj_idx['STAR'] is not None:
star_snr = mag_snr['STAR']
#lrg_snr = mag_snr['LRG']
def fit_func(xdata, coeff):
""" astro fit
"""
r1 = 0.0 # read noise
a, b = coeff
x = np.linspace(min(xdata), max(xdata), 1000)
Flux = 10**(-0.4 * (x - 22.5))
y = a * Flux * exptime / np.sqrt(a * Flux * exptime + b * exptime +
r1**2)
return x, y
data_model = {
'x': [],
'y': [],
'y2': [],
'fiber_id': [],
'ra': [],
'dec': [],
}
elg = ColumnDataSource(data=data_model.copy())
lrg = ColumnDataSource(data=data_model.copy())
qso = ColumnDataSource(data=data_model.copy())
star = ColumnDataSource(data=data_model.copy())
data_fit = {
'x': [],
'y': [],
'y2': [],
'fiber_id': [],
'ra': [],
'dec': []
}
elg_fit = ColumnDataSource(data=data_fit.copy())
lrg_fit = ColumnDataSource(data=data_fit.copy())
qso_fit = ColumnDataSource(data=data_fit.copy())
star_fit = ColumnDataSource(data=data_fit.copy())
if obj_idx['ELG'] is not None:
elg.data['x'] = elg_snr[1]
elg.data['y'] = np.array(elg_snr[0])
elg.data['y2'] = np.array(elg_snr[0])**2
elg.data['fiber_id'] = gen_info['ELG_FIBERID']
elg.data['ra'] = [ra[i % 500] for i in gen_info['ELG_FIBERID']]
elg.data['dec'] = [dec[i % 500] for i in gen_info['ELG_FIBERID']]
xfit, yfit = fit_func(elg_snr[1],
snr['FITCOEFF_TGT'][obj_idx['ELG']])
elg_fit.data['x'] = xfit
elg_fit.data['y'] = np.array(yfit)
elg_fit.data['y2'] = np.array(yfit)**2
for key in ['fiber_id', 'ra', 'dec']:
elg_fit.data[key] = [''] * len(yfit)
if obj_idx['LRG'] is not None:
lrg.data['x'] = lrg_snr[1]
lrg.data['y'] = np.array(lrg_snr[0])
lrg.data['y2'] = np.array(lrg_snr[0])**2
lrg.data['fiber_id'] = gen_info['LRG_FIBERID']
lrg.data['ra'] = [ra[i % 500] for i in gen_info['LRG_FIBERID']]
lrg.data['dec'] = [dec[i % 500] for i in gen_info['LRG_FIBERID']]
xfit, yfit = fit_func(lrg_snr[1],
snr['FITCOEFF_TGT'][obj_idx['LRG']])
lrg_fit.data['x'] = xfit
lrg_fit.data['y'] = np.array(yfit)
lrg_fit.data['y2'] = np.array(yfit)**2
for key in ['fiber_id', 'ra', 'dec']:
lrg_fit.data[key] = [''] * len(yfit)
if obj_idx['QSO'] is not None:
qso.data['x'] = qso_snr[1]
qso.data['y'] = np.array(qso_snr[0])
qso.data['y2'] = np.array(qso_snr[0])**2
qso.data['fiber_id'] = gen_info['QSO_FIBERID']
qso.data['ra'] = [ra[i % 500] for i in gen_info['QSO_FIBERID']]
qso.data['dec'] = [dec[i % 500] for i in gen_info['QSO_FIBERID']]
xfit, yfit = fit_func(qso_snr[1],
snr['FITCOEFF_TGT'][obj_idx['QSO']])
qso_fit.data['x'] = xfit
qso_fit.data['y'] = np.array(yfit)
qso_fit.data['y2'] = np.array(yfit)**2
for key in ['fiber_id', 'ra', 'dec']:
qso_fit.data[key] = [''] * len(yfit)
if obj_idx['STAR'] is not None:
star.data['x'] = star_snr[1]
star.data['y'] = np.array(star_snr[0])
star.data['y2'] = np.array(star_snr[0])**2
star.data['fiber_id'] = gen_info['STAR_FIBERID']
star.data['ra'] = [ra[i % 500] for i in gen_info['STAR_FIBERID']]
star.data['dec'] = [dec[i % 500] for i in gen_info['STAR_FIBERID']]
xfit, yfit = fit_func(star_snr[1],
snr['FITCOEFF_TGT'][obj_idx['STAR']])
star_fit.data['x'] = xfit
star_fit.data['y'] = np.array(yfit)
star_fit.data['y2'] = np.array(yfit)**2
for key in ['fiber_id', 'ra', 'dec']:
star_fit.data[key] = [''] * len(yfit)
html_tooltip = """
<div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">SNR: </span>
<span style="font-size: 1vw; color: #515151;">@y</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">DECAM_{}: </span>
<span style="font-size: 1vw; color: #515151;">@x</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Fiber ID: </span>
<span style="font-size: 1vw; color: #515151;">@fiber_id</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">RA: </span>
<span style="font-size: 1vw; color: #515151;">@ra</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Dec: </span>
<span style="font-size: 1vw; color: #515151">@dec</span>
</div>
</div>
""".format(str(self.selected_arm).upper())
url = "http://legacysurvey.org/viewer?ra=@ra&dec=@dec&zoom=16&layer=decals-dr5"
elg_plot = Plot2d(
x_label="DECAM_{}".format(str(self.selected_arm).upper()),
y_label="MEDIAN SNR^2",
tooltip=html_tooltip,
title="ELG",
width=500,
height=380,
yscale="log",
).line(
source=elg_fit,
y='y2',
).circle(
source=elg,
size=8,
y='y2',
fill_color="blue",
).plot
taptool = elg_plot.select(type=TapTool)
taptool.callback = OpenURL(url=url)
lrg_plot = Plot2d(x_label="DECAM_{}".format(
str(self.selected_arm).upper()),
y_label="MEDIAN SNR^2",
tooltip=html_tooltip,
title="LRG",
width=500,
height=380,
yscale="log").line(
source=lrg_fit,
y='y2',
).circle(
source=lrg,
size=8,
y='y2',
fill_color="red",
).plot
taptool = lrg_plot.select(type=TapTool)
taptool.callback = OpenURL(url=url)
qso_plot = Plot2d(x_label="DECAM_{}".format(
str(self.selected_arm).upper()),
y_label="MEDIAN SNR^2",
tooltip=html_tooltip,
title="QSO",
width=500,
height=380,
yscale="log").line(
source=qso_fit,
y='y2',
).circle(
source=qso,
size=8,
y='y2',
fill_color="green",
).plot
taptool = qso_plot.select(type=TapTool)
taptool.callback = OpenURL(url=url)
star_plot = Plot2d(
x_label="DECAM_{}".format(str(self.selected_arm).upper()),
y_label="MEDIAN SNR^2",
tooltip=html_tooltip,
title="STAR",
width=500,
height=380,
yscale="log",
).line(source=star_fit, y='y2').circle(
source=star,
size=8,
y='y2',
fill_color="yellow",
).plot
taptool = star_plot.select(type=TapTool)
taptool.callback = OpenURL(url=url)
# infos
info_col = Title().write_description('snr')
# -----------------
# WEDGES
snr_tooltip = """
<div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Resid: </span>
<span style="font-size: 1vw; color: #515151">@resid_snr</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Obj Type: </span>
<span style="font-size: 1vw; color: #515151;">@OBJ_TYPE</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">RA: </span>
<span style="font-size: 1vw; color: #515151;">@x1</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">DEC: </span>
<span style="font-size: 1vw; color: #515151;">@y1</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">FIBER ID: </span>
<span style="font-size: 1vw; color: #515151;">@QLF_FIBERID</span>
</div>
</div>
"""
median = snr['MEDIAN_SNR']
resid = snr['SNR_RESID']
qlf_fiberid = range(0, 500)
my_palette = get_palette('bwr')
fibersnr_tgt = []
for i in avobj:
fibersnr_tgt.append(gen_info[i + '_FIBERID'])
fibersnr = []
for i in list(range(len(fibersnr_tgt))):
fibersnr = fibersnr + fibersnr_tgt[i]
source = ColumnDataSource(
data={
'x1': [ra[i % 500] for i in fibersnr],
'y1': [dec[i % 500] for i in fibersnr],
'resid_snr': resid,
'QLF_FIBERID': fibersnr,
'OBJ_TYPE': [obj_fiber[i % 500] for i in fibersnr],
'median': median
})
ra_not = []
dec_not = []
obj_not = []
fiber_not = []
fiber_mod = []
for fiber in fibersnr:
fiber_mod.append(fiber % 500)
for i in range(500):
if i not in fiber_mod:
ra_not.append(ra[i])
dec_not.append(dec[i])
fiber_not.append(i)
obj_not.append(obj_fiber[i])
source_not = ColumnDataSource(
data={
'x1': ra_not,
'y1': dec_not,
'resid_snr': [''] * len(dec_not),
'QLF_FIBERID': fiber_not,
'OBJ_TYPE': obj_not
})
rmax, rmin = np.nanmax(resid), np.nanmin(resid)
if np.isnan(rmax) or np.isnan(rmin):
fill_color = 'lightgray'
else:
dy = (rmax - rmin) * 0.1
mapper = LinearColorMapper(palette=my_palette,
nan_color='darkgray',
low=rmin - dy,
high=rmax + dy)
fill_color = {'field': 'resid_snr', 'transform': mapper}
radius = 0.0165
radius_hover = 0.02
# centralize wedges in plots:
ra_center = 0.5 * (max(ra) + min(ra))
dec_center = 0.5 * (max(dec) + min(dec))
xrange_wedge = Range1d(start=ra_center + .95, end=ra_center - .95)
yrange_wedge = Range1d(start=dec_center + .82, end=dec_center - .82)
# axes limit
xmin, xmax = [min(ra[:]), max(ra[:])]
ymin, ymax = [min(dec[:]), max(dec[:])]
xfac, yfac = [(xmax - xmin) * 0.06, (ymax - ymin) * 0.06]
left, right = xmin - xfac, xmax + xfac
bottom, top = ymin - yfac, ymax + yfac
# WEDGE RESIDUAL plots
wedge_plot = Plot2d(x_range=xrange_wedge,
y_range=yrange_wedge,
x_label="RA",
y_label="DEC",
tooltip=snr_tooltip,
title='Residual SNR' + name_warn,
width=500,
height=380,
yscale="auto").wedge(
source,
x='x1',
y='y1',
field='resid_snr',
mapper=mapper,
).wedge(
source_not,
x='x1',
y='y1',
).plot
taptool = wedge_plot.select(type=TapTool)
taptool.callback = OpenURL(url=url)
# -------------------
# Median plot
median_tooltip = """
<div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">MEDIAN: </span>
<span style="font-size: 1vw; color: #515151">@median</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Resid: </span>
<span style="font-size: 1vw; color: #515151">@resid_snr</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Obj Type: </span>
<span style="font-size: 1vw; color: #515151;">@OBJ_TYPE</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">RA: </span>
<span style="font-size: 1vw; color: #515151;">@x1</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">DEC: </span>
<span style="font-size: 1vw; color: #515151;">@y1</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">FIBER ID: </span>
<span style="font-size: 1vw; color: #515151;">@QLF_FIBERID</span>
</div>
</div>
"""
mediam_plot = Plot2d(
x_label="Fiber",
y_label='Median SNR',
tooltip=median_tooltip,
title="",
width=600,
height=400,
yscale="auto",
hover_mode="vline",
).vbar(
source,
y="median",
x="QLF_FIBERID",
line_width=0.4,
)
# Prepare tables
current_exposures = check_spectra['METRICS']['FIDSNR_TGT']
program = gen_info['PROGRAM'].upper()
reference_exposures = check_spectra['PARAMS']['FIDSNR_TGT_' + program +
'_REF']
keynames = ["FIDSNR_TGT" + " ({})".format(i) for i in objlist]
metric = Table().reference_table(keynames, current_exposures,
reference_exposures)
alert = Table().alert_table(nrg, wrg)
layout = column(info_col,
Div(),
metric,
alert,
column(elg_plot, sizing_mode='scale_both'),
column(lrg_plot, sizing_mode='scale_both'),
column(qso_plot, sizing_mode='scale_both'),
column(star_plot, sizing_mode='scale_both'),
column(mediam_plot, sizing_mode='scale_both'),
column(wedge_plot, sizing_mode='scale_both'),
css_classes=["display-grid"])
return file_html(layout, CDN, "MEDIAN SNR")
def load_qa(self):
cam = self.selected_arm + str(self.selected_spectrograph)
mergedqa = QLFModels().get_output(self.selected_process_id, cam)
check_fibers = mergedqa['TASKS']['CHECK_FIBERS']
gen_info = mergedqa['GENERAL_INFO']
flavor = mergedqa["FLAVOR"]
countbins = check_fibers['METRICS']
nrg = check_fibers['PARAMS']['NGOODFIB_NORMAL_RANGE']
wrg = check_fibers['PARAMS']['NGOODFIB_WARN_RANGE']
if flavor == "science":
ra = gen_info['RA']
dec = gen_info['DEC']
else:
ra = [''] * 500
dec = [''] * 500
qlf_fiberid = np.arange(0, 500) # the fiber id is giving by petal
obj_type = sort_obj(mergedqa['GENERAL_INFO'])
hist_tooltip = """
<div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">FIBER STATUS: </span>
<span style="font-size: 1vw; color: #515151;">@status</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">FIBER ID: </span>
<span style="font-size: 1vw; color: #515151;">@fiberid</span>
</div>
</div>
"""
y = np.array(countbins['GOOD_FIBERS'])
fibers = list(countbins['GOOD_FIBERS'])
colors = ['#319b5c' if i == 1 else '#282828' for i in fibers]
x = np.array(range(len(fibers)))
hist_source = ColumnDataSource(
data={
'goodfiber': y,
'fiberid': x,
'segx0': x - 0.4,
'segx1': x + 0.4,
'segy0': y,
'segy1': y,
'status': ['GOOD' if i == 1 else 'BAD' for i in y],
'x1': ra,
'y1': dec,
'QLF_FIBERID': qlf_fiberid,
'OBJ_TYPE': obj_type,
'color': colors
})
p = Plot2d(
y_range=Range1d(-.1, 1.1),
x_label="Fiber",
y_label="Fiber Status",
tooltip=hist_tooltip,
title="Status",
width=550,
height=350,
yscale="auto",
hover_mode="vline",
).segment(hist_source).plot
# ----------------
# Wedge
if flavor == "science":
if ra is None or dec is None:
return 'Missing RA and DEC'
count_tooltip = """
<div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">FIBER STATUS: </span>
<span style="font-size: 1vw; color: #515151">@status</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">RA: </span>
<span style="font-size: 1vw; color: #515151;">@x1</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">DEC: </span>
<span style="font-size: 1vw; color: #515151;">@y1</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Obj Type: </span>
<span style="font-size: 1vw; color: #515151;">@OBJ_TYPE</span>
</div>
</div>
"""
radius = 0.0165
radius_hover = 0.02
# centralize wedges in plots:
ra_center = 0.5 * (max(ra) + min(ra))
dec_center = 0.5 * (max(dec) + min(dec))
xrange_wedge = Range1d(start=ra_center + .95, end=ra_center - .95)
yrange_wedge = Range1d(start=dec_center + .82,
end=dec_center - .82)
p2 = Plot2d(x_range=xrange_wedge,
y_range=yrange_wedge,
x_label="RA",
y_label="DEC",
tooltip=count_tooltip,
title="Fiber Status",
width=400,
height=350,
yscale="auto").wedge(
hist_source,
x='x1',
y='y1',
field='color',
).plot
else:
p2 = Div()
ngood = countbins['NGOODFIB']
fracgood = ngood / 500. - 1.
info_col = Title().write_description('countbins')
# Prepare tables
current_exposures = [check_fibers['METRICS']['NGOODFIB']]
if flavor == 'science':
program = gen_info['PROGRAM'].upper()
reference_exposures = check_fibers['PARAMS']['NGOODFIB_' +
program + '_REF']
else:
reference_exposures = check_fibers['PARAMS']['NGOODFIB_REF']
keynames = ["NGOODFIB"]
metric = Table().reference_table(keynames, current_exposures,
reference_exposures)
alert = Table().alert_table(nrg, wrg)
layout = column(info_col,
Div(),
metric,
alert,
column(p, sizing_mode='scale_both'),
column(p2, sizing_mode='scale_both'),
css_classes=["display-grid"])
return file_html(layout, CDN, "COUNTBINS")