import numpy as np
import healpy as hp
from .baseMetric import BaseMetric
# A collection of metrics which are primarily intended to be used as summary statistics.
__all__ = ['fOArea', 'fONv', 'TableFractionMetric', 'IdentityMetric',
'NormalizeMetric', 'ZeropointMetric', 'TotalPowerMetric']
[docs]class fOArea(BaseMetric):
"""
Metric to calculate the FO Area.
"""
def __init__(self, col='metricdata', Asky=18000., Nvisit=825,
metricName='fOArea', nside=128, norm=True, **kwargs):
"""Asky = square degrees """
super(fOArea, self).__init__(col=col, metricName=metricName, **kwargs)
self.Asky = Asky
self.Nvisit = Nvisit
self.nside = nside
self.norm = norm
[docs] def run(self, dataSlice, slicePoint=None):
dataSlice.sort()
name = dataSlice.dtype.names[0]
scale = hp.nside2pixarea(self.nside, degrees=True)
cumulativeArea = np.arange(1,dataSlice.size+1)[::-1]*scale
good = np.where(cumulativeArea >= self.Asky)[0]
if good.size > 0:
nv = np.max(dataSlice[name][good])
if self.norm:
nv = nv/float(self.Nvisit)
return nv
else:
return self.badval
[docs]class fONv(BaseMetric):
"""
Metric to calculate the FO_Nv.
"""
def __init__(self, col='metricdata', Asky=18000., metricName='fONv', Nvisit=825,
nside=128, norm=True, **kwargs):
"""Asky = square degrees """
super(fONv, self).__init__(col=col, metricName=metricName, **kwargs)
self.Asky = Asky
self.Nvisit = Nvisit
self.nside = nside
self.norm = norm
[docs] def run(self, dataSlice, slicePoint=None):
dataSlice.sort()
name = dataSlice.dtype.names[0]
scale = hp.nside2pixarea(self.nside, degrees=True)
cumulativeArea = np.arange(1,dataSlice.size+1)[::-1]*scale
good = np.where(dataSlice[name] >= self.Nvisit)[0]
if good.size > 0:
area = np.max(cumulativeArea[good])
if self.norm:
area = area/float(self.Asky)
return area
else:
return self.badval
[docs]class TableFractionMetric(BaseMetric):
"""
Count the completeness (for many fields) and summarize how many fields have given completeness levels
(within a series of bins). Works with completenessMetric only.
This metric is meant to be used as a summary statistic on something like the completeness metric.
The output is DIFFERENT FROM SSTAR and is:
element matching values
0 0 == P
1 0 < P < .1
2 .1 <= P < .2
3 .2 <= P < .3
...
10 .9 <= P < 1
11 1 == P
12 1 < P
Note the 1st and last elements do NOT obey the numpy histogram conventions.
"""
def __init__(self, col='metricdata', nbins=10):
"""
colname = the column name in the metric data (i.e. 'metricdata' usually).
nbins = number of bins between 0 and 1. Should divide evenly into 100.
"""
super(TableFractionMetric, self).__init__(col=col, metricDtype='float')
self.nbins = nbins
# set this so runSliceMetric knows masked values should be set to zero and passed
self.maskVal = 0.
[docs] def run(self, dataSlice, slicePoint=None):
# Calculate histogram of completeness values that fall between 0-1.
goodVals = np.where((dataSlice[self.colname] > 0) & (dataSlice[self.colname] < 1) )
bins = np.arange(self.nbins+1.)/self.nbins
hist, b = np.histogram(dataSlice[self.colname][goodVals], bins=bins)
# Fill in values for exact 0, exact 1 and >1.
zero = np.size(np.where(dataSlice[self.colname] == 0)[0])
one = np.size(np.where(dataSlice[self.colname] == 1)[0])
overone = np.size(np.where(dataSlice[self.colname] > 1)[0])
hist = np.concatenate((np.array([zero]), hist, np.array([one]), np.array([overone])))
# Create labels for each value
binNames = ['0 == P']
binNames.append('0 < P < 0.1')
for i in np.arange(1, self.nbins):
binNames.append('%.2g <= P < %.2g'%(b[i], b[i+1]) )
binNames.append('1 == P')
binNames.append('1 < P')
# Package the names and values up
result = np.empty(hist.size, dtype=[('name', np.str_, 20), ('value', float)])
result['name'] = binNames
result['value'] = hist
return result
[docs]class IdentityMetric(BaseMetric):
"""
Return the metric value itself .. this is primarily useful as a summary statistic for UniSlicer metrics.
"""
[docs] def run(self, dataSlice, slicePoint=None):
if len(dataSlice[self.colname]) == 1:
result = dataSlice[self.colname][0]
else:
result = dataSlice[self.colname]
return result
[docs]class NormalizeMetric(BaseMetric):
"""
Return a metric values divided by 'normVal'. Useful for turning summary statistics into fractions.
"""
def __init__(self, col='metricdata', normVal=1, **kwargs):
super(NormalizeMetric, self).__init__(col=col, **kwargs)
self.normVal = float(normVal)
[docs] def run(self, dataSlice, slicePoint=None):
result = dataSlice[self.colname]/self.normVal
if len(result) == 1:
return result[0]
else:
return result
[docs]class ZeropointMetric(BaseMetric):
"""
Return a metric values with the addition of 'zp'. Useful for altering the zeropoint for summary statistics.
"""
def __init__(self, col='metricdata', zp=0, **kwargs):
super(ZeropointMetric, self).__init__(col=col, **kwargs)
self.zp = zp
[docs] def run(self, dataSlice, slicePoint=None):
result = dataSlice[self.colname] + self.zp
if len(result) == 1:
return result[0]
else:
return result
[docs]class TotalPowerMetric(BaseMetric):
"""
Calculate the total power in the angular power spectrum between lmin/lmax.
"""
def __init__(self, col='metricdata', lmin=100., lmax=300., removeDipole=True, **kwargs):
self.lmin = lmin
self.lmax = lmax
self.removeDipole = removeDipole
super(TotalPowerMetric, self).__init__(col=col, **kwargs)
self.maskVal = hp.UNSEEN
[docs] def run(self, dataSlice, slicePoint=None):
# Calculate the power spectrum.
if self.removeDipole:
cl = hp.anafast(hp.remove_dipole(dataSlice[self.colname], verbose=False))
else:
cl = hp.anafast(dataSlice[self.colname])
ell = np.arange(np.size(cl))
condition = np.where((ell <= self.lmax) & (ell >= self.lmin))[0]
totalpower = np.sum(cl[condition]*(2*ell[condition]+1))
return totalpower