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)
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):
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")