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dashboard_scalar_metrics.py
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dashboard_scalar_metrics.py
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from __future__ import division
# Scalar_metrics.py DESISPEC 0.17.1
# To do: Finish the documentation
# python3 and matplotlib2 (cm.jet)
#dbg import logging
#dbg from dashboard.bokeh.helper import get_data
import requests
import os
import sys
import ast
#dbg QLF_API_URL = os.environ.get('QLF_API_URL',
#dbg 'http://localhost:8000/dashboard/api')
#dbg logger = logging.getLogger(__name__)
class LoadMetrics:
""" Read values from the yaml's files and return an alert (NORMAL, WARN or ALARM)
associated to a given metric. It also attributes a color for a wedge in the interface
CORRECTING WXSIGMA
Functions:
----------
Load_qa(qa)
Load_metrics_n_tests()
keys_from_scalars( params_keys)
test_ranges(qa, kind_of_test)
qa_status(qa)
step_color( step_name)
"""
silent = 'False' # Defining a silent mode
prfx = 'ql-'
qa_name = ['countpix', 'getbias', 'getrms'
, 'xwsigma', 'countbins', 'integ'
, 'skycont', 'skypeak', 'skyresid', 'snr']
params_keys = [['NPIX_ALARM_RANGE', 'CUTHI', 'NPIX_WARN_RANGE', 'CUTLO']
, ['DIFF_ALARM_RANGE', 'PERCENTILES', 'DIFF_WARN_RANGE']
, ['RMS_ALARM_RANGE', 'RMS_WARN_RANGE']
, ['B_PEAKS', 'R_PEAKS', 'XSHIFT_ALARM_RANGE', 'WSHIFT_ALARM_RANGE'
, 'Z_PEAKS', 'WSHIFT_WARN_RANGE', 'XSHIFT_WARN_RANGE']
, ['CUTHI', 'CUTLO', 'CUTMED', 'NGOOD_ALARM_RANGE', 'NGOOD_WARN_RANGE']
, ['MAGDIFF_ALARM_RANGE', 'MAGDIFF_WARN_RANGE']
, ['SKYCONT_ALARM_RANGE', 'SKYCONT_WARN_RANGE', 'B_CONT', 'Z_CONT', 'R_CONT']
, ['B_PEAKS', 'R_PEAKS', 'SUMCOUNT_WARN_RANGE', 'SUMCOUNT_ALARM_RANGE', 'Z_PEAKS']
, ['PCHI_RESID', 'PER_RESID', 'SKY_ALARM_RANGE', 'SKY_WARN_RANGE', 'BIN_SZ']
, ['FIDSNR_WARN_RANGE', 'FIDSNR_ALARM_RANGE', 'FIDMAG']]
params_dict = {'countbins': ['CUTHI', 'CUTLO', 'CUTMED', 'NGOOD_ALARM_RANGE', 'NGOOD_WARN_RANGE'],
'countpix': ['NPIX_ALARM_RANGE', 'CUTHI', 'NPIX_WARN_RANGE', 'CUTLO'],
'getbias': ['DIFF_ALARM_RANGE', 'PERCENTILES', 'DIFF_WARN_RANGE'],
'getrms': ['RMS_ALARM_RANGE', 'RMS_WARN_RANGE'],
'integ': ['MAGDIFF_ALARM_RANGE', 'MAGDIFF_WARN_RANGE'],
'skycont': ['SKYCONT_ALARM_RANGE', 'SKYCONT_WARN_RANGE', 'B_CONT', 'Z_CONT', 'R_CONT'],
'skypeak': ['B_PEAKS', 'R_PEAKS', 'SUMCOUNT_WARN_RANGE'
, 'SUMCOUNT_ALARM_RANGE', 'Z_PEAKS'],
'skyresid': ['PCHI_RESID', 'PER_RESID', 'SKY_ALARM_RANGE', 'SKY_WARN_RANGE', 'BIN_SZ'],
'snr': ['FIDSNR_WARN_RANGE', 'FIDSNR_ALARM_RANGE', 'FIDMAG'],
'xwsigma': ['B_PEAKS', 'R_PEAKS', 'XSHIFT_ALARM_RANGE', 'WSHIFT_ALARM_RANGE'
, 'Z_PEAKS', 'WSHIFT_WARN_RANGE', 'XSHIFT_WARN_RANGE']}
def __init__(self, cam,exp,night):
self.cam = cam
self.exp = exp
self.night = night
# This is True if the pipeline didn't generate some yaml file
self.error = dict(zip(self.qa_name, ['False']*len(self.qa_name)) )
logger.info('check *rms_over *bias *SUMCOUNT_RMS shouldbe SUMCOUNT_MED_SKY'
+'Resigf skyresid- residrms')
# QA tests and keys to respective values
self.metric_qa_list = ['getbias','getrms','skycont', 'countbins', 'countpix', 'snr'
,'skyresid', 'skypeak', 'integ', 'xsigma', 'wsigma' ] #THIS LINE : TB CHECKED
self.metric_key_list = ['BIAS','RMS_OVER','SKYCONT','NGOODFIBERS', 'NPIX_LOW', 'ELG_FIDMAG_SNR'
,'RESID_RMS', 'SUMCOUNT_MED_SKY', 'MAGDIFF_AVG', 'XSHIFT', 'WSHIFT']
self.metric_dict = dict(zip(self.metric_qa_list, self.metric_key_list))
try: #ff
self.metrics, self.tests = self.Load_metrics_n_tests()
except: #ff
sys.exit("Could not load metrics and tests" )
def Load_qa(self, qa):
"""loads a single yaml file ( rather slow!)
Arguments
---------
qa --
cam --
exp --
night --
Return
------
y2: list
"""
import yaml
cam, exp, night = self.cam, self.exp, self.night
# qlf_folder = '/home/foliveira/'
# exp_folder = 'quicklook/spectro/redux/exposures/'
# aux = '{}{}{}/{}/{}{}-{}-{}.yaml'.format(qlf_folder,exp_folder, night, exp, self.prfx, qa, cam,exp)
exp_zfill = str(exp).zfill(8)
qa_name = '{}{}-{}-{}.yaml'.format(self.prfx, qa, cam, exp_zfill)
api = requests.get(QLF_API_URL).json()
data = requests.get(api['qa'], params={'name': qa_name}).json()
print('qa',qa_name)
#print('***', data['results'].keys() )
data = data['results'][0]
if data != None:
self.error.update({qa:False})
else:
self.error.update({qa:True})
return data
def Load_metrics_n_tests(self):
""" Gathers all the yaml info in 'METRICS' and 'PARAMS'
and returns them in individual dictionaries
Uses: Load_qa
Arguments
---------
qa_name: lst or str
A name or list of names of qa's
Return
------
dic_met: dictionary
A dictionary with the metric values
dic_test: dictionary
A dictionary with the test values
"""
dic_met = {}
dic_tst = {}
if isinstance(self.qa_name, list):
qa_list = self.qa_name
elif isinstance(self.qa_name, string): # for a single qa_name
qa_list = [self.qa_name]
else:
return "Invalid QA format"
for i in qa_list:
aux = self.Load_qa(i)
if aux == None:
dic_met.update({i: aux})
dic_tst.update({i: aux})
self.error.update({i:True})
else:
dic_met.update({i: aux['metrics']})
dic_tst.update({i: aux['params']})
return dic_met, dic_tst
def keys_from_scalars(self, params_keys):
""" Finds the equivalente alert keys in yaml for a metric.
Arguments
---------
qa_name: list
A list of str w/ the QA names
params_keys: list
List of list of str w/ keys names contained in METRICS.
Return
-------
xx: dict
A dictionary of <qa_name>. For each 'qa_name' another dictionary with {kind_of_test>,
addressing a 'kind of test' to its equivalent key inside the yaml file.
"""
xx = {}
#qa_name, params_keys = self.qa_name, self.params_keys
params_keys = self.params_keys
for index, scalar in enumerate(self.qa_name):
if scalar=='xwsigma': # Redistrubuting xsigma and wsigma
xx['xsigma'] = {'alarm':'XSHIFT_ALARM_RANGE', 'warn':'XSHIFT_WARN_RANGE'}
xx['wsigma'] = {'alarm':'WSHIFT_ALARM_RANGE', 'warn':'WSHIFT_WARN_RANGE'}
xx.update({'xsigma':{'alarm':'XSHIFT_ALARM_RANGE', 'warn':'XSHIFT_WARN_RANGE'}
,'wsigma':{'alarm':'WSHIFT_ALARM_RANGE', 'warn':'WSHIFT_WARN_RANGE'}})
else:
for j in params_keys[index]:
#logger.info j
if 'ALARM_RANGE' in j:
alarm_name = j
if 'WARN_RANGE' in j:
warn_name = j
try:
xx.update({scalar: {'alarm':alarm_name, 'warn':warn_name}})
except:
logger.info ('Error during dictionary update')
return xx
def test_ranges(self, qa, kind_of_test):
""" Returns the range of a given test from the yaml file.
Arguments
---------
qa: ?list
?d
kind_of_test: ?list
?d
Return
------
test_range: list
A list representing [min_value, max_value]
"""
self.qa = qa
self.kot = kind_of_test
qa_name = ['countpix', 'getbias', 'getrms'
, 'xwsigma', 'countbins', 'integ'
, 'skycont', 'skypeak', 'skyresid', 'snr']
metrics = self.metrics
tests = self.tests
self.par_k = self.params_keys
#self.d = self.keys_from_scalars(qa_name, self.par_k)#f
self.d = self.keys_from_scalars(self.par_k)
qalist = qa_name + ['xsigma', 'wsigma']
if self.kot not in ['warn', 'alarm']:
raise Exception('Error: Invalid test value:', self.kot)
if self.qa == 'xwsigma':
raise Exception('Error: Please use either xsigma or wsigma.')
elif self.qa not in qalist:
raise Exception('Error: Invalid QA name:', qa)
elif(self.qa in ['xsigma', 'wsigma']):
qa_2 = 'xwsigma'
test_range = ast.literal_eval(tests[qa_2])[self.d[self.qa][self.kot]]
else:
test_range = ast.literal_eval(tests[qa])[self.d[self.qa][self.kot]]
return test_range
def qa_status(self, qa):
"""Returns the alert of a given qa
Arguments
---------
qa: str
Return
------
status: str
Possible values: 'NORMAL', 'WARN' or 'ALARM'
"""
#self.qa = qa
if qa == 'xwsigma':
alarm_x = self.test_ranges('xsigma','alarm')
warn_x = self.test_ranges('xsigma','warn')
val_x = ast.literal_eval(self.metrics[qa])[self.metric_dict['xsigma']]
alarm_w = self.test_ranges('wsigma','alarm')
warn_w = self.test_ranges('wsigma','warn')
val_w = ast.literal_eval(self.metrics[qa])[self.metric_dict['wsigma']]
if isinstance(val_w,float) or isinstance(val_w, int) or isinstance(val_x,float) or isinstance(val_x, int):
pass
else:
raise Exception ("Invalid variable type in xwsigma: {} or {}".format(val_x, val_w))
if( val_w <= alarm_w[0] or val_w >= alarm_w[1] or val_x <= alarm_x[0] or val_x >= alarm_x[1]):
# ">=" comes from pipeline definition!
return 'ALARM'
elif(val_x <= warn_x[0] or val_x >= warn_x[1] or val_w <= warn_w[0] or val_w >= warn_w[1] ):
return 'WARN'
else:
return 'NORMAL'
elif(qa == 'xsigma' or qa == 'wsigma'):
logger.info('Please, use xwsigma')
return 'Error'
# Original
else:
alarm = self.test_ranges(qa,'alarm')
warn = self.test_ranges(qa,'warn')
val = ast.literal_eval(self.metrics[qa])[self.metric_dict[qa]]
#dblogger.info(qa, self.metric_dict[qa])
if isinstance(val,float) or isinstance(val, int):
pass
else:
self.error.update({qa:True})
raise Exception ("Invalid variable type:{} in".format(val), qa)
if ( val <= alarm[0] or val >= alarm[1]): # ">=" comes from pipeline definition!
return 'ALARM'
elif (val <= warn[0] or val >= warn[1] ):
return 'WARN'
else:
return 'NORMAL'
def step_colorOld(self, step_name):
""" This was the version used before tag 0.17.1 of desispec
Missing: Xsigma and Wsigma
Arguments
---------
step_name: str
The abbreviated name of one of the four QA steps
Return
------
color: str
Wedge color Alert
"""
self.step_name = step_name
steps_list = ['preproc', 'extract', 'fiberfl', 'skysubs']
if not isinstance(self.step_name, str):
return "{} is not a String".format(self.step_name)
if self.step_name not in steps_list:
return "Invalid step: please return a value in {}".format(steps_list)
steps_dic = {'preproc':['countpix', 'getbias','getrms', 'xwsigma'],
'extract':['countbins'],
'fiberfl':['integ','skycont','skypeak','skyresid'],
'skysubs':['snr']}
steps_status = []
# logger.info ('init_error', self.error)
for i in steps_dic[self.step_name]:
if(self.error[i]):
logger.info ('QL FAILURE')
steps_status.append('FAILURE')
else:
try:
aux1 = self.qa_status(i)
steps_status.append(aux1)
except:
steps_status.append('FAILURE')
# logger.info('Steps_status:', steps_status)
if any(x=='FAILURE' for x in steps_status):
color = 'magenta' # Pick a color for failure case
elif any( x == 'ALARM' for x in steps_status):
color = "red"
logger.info( color )
elif any( x == 'WARN' for x in steps_status):
color = "yellow"
logger.info( color )
elif all(x=='NORMAL' for x in steps_status): #intentionally redundant
color = "green"
logger.info( color )
result = {'color': color, 'steps_status': steps_status }
return result
def step_color(self, step_name):
""" Reading step color produced in desispec 0.17.1
Arguments
---------
step_name: str
The abbreviated name of one of the four QA steps
Return
------
color: str
Wedge color Alert
"""
alert_keys = {'getrms': 'NOISE_STAT', 'countpix': 'NPIX_STAT',
'getbias': 'BIAS_STAT', 'countbins': 'NGOODFIB_STAT', 'integ': 'MAGDIFF_STAT',
'xwsigma': 'XWSIGMA_STAT', 'snr': 'FIDSNR_STAT', 'skycont': 'SKYCONT_STAT',
'skypeak': 'PEAKCOUNT_STAT', 'skyresid': 'RESIDRMS_STAT'}
self.step_name = step_name
steps_list = ['preproc', 'extract', 'fiberfl', 'skysubs']
if not isinstance(self.step_name, str):
return "{} is not a String".format(self.step_name)
if self.step_name not in steps_list:
return "Invalid step: please return a value in {}".format(steps_list)
steps_dic = {'preproc':['countpix', 'getbias','getrms', 'xwsigma'],
'extract':['countbins'],
'fiberfl':['integ','skycont','skypeak','skyresid'],
'skysubs':['snr']}
# begin for desispec >= 0.17.1
steps_status = []
for i in steps_dic[self.step_name]:
#print('%13s %s' % (i, lm.metrics[i][alert_keys[i]]))
try:
aux1 = self.metrics[i][alert_keys[i]]
except:
aux1 = 'FAILURE'
steps_status.append(aux1)
# end
if any(x=='FAILURE' for x in steps_status):
color = 'magenta' # Pick a color for failure case
elif any( x == 'ALARM' for x in steps_status):
color = "red"
logger.info( color )
elif any( x == 'WARN' for x in steps_status):
color = "yellow"
logger.info( color )
elif all(x=='NORMAL' for x in steps_status):
color = "green"
logger.info( color )
result = {'color': color, 'steps_status': steps_status }
return result
if __name__=='__main__':