def setupMetrics(args, tstart, tnow, verbose=False):
"""
Define and instantiate metrics.
Pass 'args' to access 'cumulative' flag, in order to set better limits for max/min.
Could potentially access other 'args' values.
"""
# Define and set up metrics.
# Note that it is useful to set up the plotDict so that the min/max range for the plot
# is the same for all movie frames.
t = time.time()
metricList = []
#Simple metrics: coadded depth and number of visits
nvisitsMin = 0
nvisitsMax = 300
coaddMin = 25
coaddMax = 28
if not args.cumulative:
# Take a guess ... probably will need to be adjusted for your stepsize.
nvisitsMax = 15
coaddMin = 24.0
coaddMax = 26.5
metricList.append(
metrics.Coaddm5Metric('fiveSigmaDepth',
metricName='Coaddm5Metric',
plotDict={
'colorMin': coaddMin,
'colorMax': coaddMax
}))
metricList.append(
metrics.CountMetric('expMJD',
metricName='N_Visits',
plotDict={
'colorMin': nvisitsMin,
'colorMax': nvisitsMax,
'cbarFormat': '%d',
'title': 'Number of Visits '
}))
# Uniformity wants survey length in years.
surveyLength = (tnow - tstart) / 365.0
metricList.append(
metrics.UniformityMetric('expMJD',
surveyLength=surveyLength,
plotDict={
'colorMin': 0,
'colorMax': 1,
'cbarFormat': '%.2f',
'title': 'Survey Uniformity'
}))
dt, t = dtime(t)
if verbose:
print 'Set up metrics %f s' % (dt)
return metricList
def testUniformityMetric(self):
names = ['observationStartMJD']
types = [float]
data = np.zeros(100, dtype=list(zip(names, types)))
metric = metrics.UniformityMetric()
result1 = metric.run(data)
# If all the observations are on the 1st day, should be 1
self.assertEqual(result1, 1)
data['observationStartMJD'] = data['observationStartMJD']+365.25*10
slicePoint = {'sid': 0}
result2 = metric.run(data, slicePoint)
# All on last day should also be 1
self.assertEqual(result2, 1)
# Make a perfectly uniform dist
data['observationStartMJD'] = np.arange(0., 365.25*10, 365.25*10/100)
result3 = metric.run(data, slicePoint)
# Result should be zero for uniform
np.testing.assert_almost_equal(result3, 0.)
# A single obseravtion should give a result of 1
data = np.zeros(1, dtype=list(zip(names, types)))
result4 = metric.run(data, slicePoint)
self.assertEqual(result4, 1)