The following cell connect to the api running in the same Docker environment as the Jupyter Notebook docker container.
http://app:8000/dashboard/api/job/?process=2But it can be used by any other QLF accessible api such as:
http://qltest.linea.gov.br/dashboard/api/job/?process=8
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import urllib.request
import json
job = urllib.request.urlopen("http://ql.linea.gov.br/dashboard/api/job/?process=1").read()
api = json.loads(job)
mergedqa = api['results'][0]['output']
print('mergedQA loaded!')
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import sys
sys.path.append('/app/qlf/backend/framework/qlf')
from bokeh.io import show, output_notebook
output_notebook()
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from bokeh.models import ColumnDataSource, Range1d
import numpy as np
from dashboard.bokeh.helper import get_palette, sort_obj
from bokeh.models import LinearColorMapper
check_ccds = mergedqa['TASKS']['CHECK_CCDs']
gen_info = mergedqa['GENERAL_INFO']
flavor= mergedqa['FLAVOR']
ra = gen_info['RA']
dec = gen_info['DEC']
xw_fib = check_ccds['METRICS']['XWSIGMA_FIB']
xsigma = xw_fib[0]
xfiber = np.arange(len(xsigma))
obj_type = sort_obj(gen_info)
source = ColumnDataSource(data={
'x1': ra,
'y1': dec,
'xsigma': xsigma,
'xfiber': xfiber,
'OBJ_TYPE': obj_type,
'left': np.arange(0, 500)-0.4,
'right': np.arange(0, 500)+0.4,
'bottom': [0]*500
})
# 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)
my_palette = get_palette("viridis")
xmapper = LinearColorMapper(palette=my_palette,
low=0.98*np.min(xsigma),
high=1.02*np.max(xsigma))
print('Data Ready!')
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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;">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>
"""
print('Tooltip Created!')
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from dashboard.bokeh.plots.plot2d.main import Plot2d
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='xsigma',
mapper=xmapper,
colorbar_title='xsigma'
).plot
show(wedge_plot_x)
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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="Histogram",
width=600,
height=300,
yscale="auto",
hover_mode="vline",
).quad(
source,
top='xsigma',
)
show(xhist)
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wrg = check_ccds['PARAMS']['XWSIGMA_WARN_RANGE']
delta_rg = wrg[1] - wrg[0]
hist_rg = (wrg[0] - 0.1*delta_rg, wrg[1]+0.1*delta_rg)
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']
hist, edges = np.histogram(xsigma, 'sqrt')
source_hist = ColumnDataSource(data={
'hist': hist,
'bottom': [0] * len(hist),
'left': edges[:-1],
'right': edges[1:]
})
print('Done!')
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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>
"""
print('Tooltip Created!')
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p_hist_x = Plot2d(
x_label="XSIGMA",
y_label="Frequency",
tooltip=hist_tooltip_x,
title="Histogram",
width=600,
height=300,
yscale="auto",
y_range=(0.0*max(hist), 1.1*max(hist)),
x_range=(hist_rg[0]+xw_ref[0],
hist_rg[1]+xw_ref[0]),
hover_mode="vline",
).quad(
source_hist,
top='hist',
bottom='bottom',
line_width=0.4,
)
show(p_hist_x)
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nrg = check_ccds['PARAMS']['XWSIGMA_NORMAL_RANGE']
from bokeh.models import Span, Label
for ialert in nrg:
normal_divisors = Span(location=ialert+xw_ref[0],
dimension='height',
line_color='green',
line_dash='dashed', line_width=2)
p_hist_x.add_layout(normal_divisors)
normal_labels = 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(normal_labels)
for ialert in wrg:
warning_divisors = Span(location=ialert+xw_ref[0], dimension='height', line_color='tomato',
line_dash='dotdash', line_width=2)
p_hist_x.add_layout(warning_divisors)
warning_labels = 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(warning_labels)
p_hist_x.title.text = "Histogram with labels"
show(p_hist_x)
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from dashboard.bokeh.plots.patch.main import Patch
xw_amp = check_ccds['METRICS']['XWSIGMA_AMP']
xamp = Patch().plot_amp(
dz=xw_amp[0],
refexp=[xw_ref[0]]*4,
name="XSIGMA AMP",
description="X std deviation per Amp (number of pixels)",
wrg=wrg
)
show(xamp)
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