def __init__(self, world):
# call base
Metric.__init__(self, world)
self.name = "compressibility"
self.value = 0.0
self.format = "%.2f%%"
self.co = zlib.compressobj()
def __init__(self, world):
# call base
Metric.__init__(self, world)
self.name = "exploration"
self.value = 0.0
self.format = "%.2f%%"
# dictionary mapping hashes to ticks used for cycle detection
self.rule_count = np.zeros(self.world.rule.size, dtype=int)
def __init__(self, world):
# call base
Metric.__init__(self, world)
self.name = "cyclic"
self.value = False
# dictionary mapping hashes to ticks used for cycle detection
self.hist = {}
self.cycle_start = None
self.cycle_period = None
def __init__(self, name, fn):
Metric.__init__(self, name)
self._fn = fn
return
def __init__(self, world):
# call base
Metric.__init__(self, world)
self.name = "population"
self.value = self.world.get_population()
def __init__(self, name):
Metric.__init__(self, name)
self._count = 0
return
def __init__(self, ministry, zbins=None, magbins=None,
catalog_type=['galaxycatalog'], tag=None,
appmag=True, lower_limit=True, cutband=None,
normed=True, selection_dict=None, **kwargs):
"""
Angular Number density of objects as a function of redshift.
inputs
--------
ministry - Ministry
keywords
------
zbins - np.array
An array containing the edges of the redshift bins to use for dn/dz
lower_limit - boolean
Whether or not the magnitudes in magbins should be interpreted
as bin edges or lower limits of cuts (i.e. mag<magbins[i])
magbins - np.array
A list of magnitudes to use as bin edges or lower limits of cuts
catalog_type - list
A list of catalog types, ususally not used
tag - string
A name for the metric to be used when making comparisons using bb-compare
appmag - boolean
True if we want to use apparent magnitude for cuts, false for absolute magnitudes
cutband - int
The index of the column of a vector of magnitudes to use
normed - boolean
Whether the metric integrates to N/deg^2 or not. Usually want True.
"""
Metric.__init__(self, ministry, tag=tag, **kwargs)
self.catalog_type = catalog_type
if zbins is None:
self.zbins = np.linspace(0, 2.0, 61)
else:
self.zbins = zbins
self.zbins = np.array(self.zbins)
self.nzbins = len(self.zbins)-1
if appmag:
self.mkey = 'appmag'
defmbins = np.array([18, 19, 20, 21])
else:
self.mkey = 'luminosity'
defmbins = np.array([-24, -23, -22, -21])
if cutband is None:
self.cutband = 0
else:
self.cutband = cutband
self.lower_limit = lower_limit
if magbins is None:
self.magbins = [None]
self.nmagbins = 0
self.nomags = True
else:
self.nomags = False
self.magbins = magbins
if self.lower_limit:
self.nmagbins = len(self.magbins)
else:
self.nmagbins = len(self.magbins) - 1
self.normed = normed
self.aschema = 'galaxyonly'
if self.nmagbins > 0:
self.mapkeys = [self.mkey, 'redshift']
self.unitmap = {self.mkey :'mag'}
else:
self.mapkeys = ['redshift']
self.unitmap = {}
#Make selection dict here
if (selection_dict is None) & lower_limit:
selection_dict = {'mag':{'selection_type':'cut1d',
'mapkeys':['appmag'],
'bins':self.magbins,
'selection_ind':self.cutband,
'lower':True}}
elif (selection_dict is None):
selection_dict = {'mag':{'selection_type':'binned1d',
'mapkeys':['appmag'],
'bins':self.magbins,
'selection_ind':self.cutband}}
nmkeys = []
for s in selection_dict:
if 'mapkeys' in selection_dict[s]:
ss = selection_dict[s]
for m in ss['mapkeys']:
if m not in self.mapkeys:
self.mapkeys.append(m)
if m not in self.unitmap:
self.unitmap[m] = self.defaultUnits(m)
self.zcounts = None
self.selector = Selector(selection_dict)
def __init__(self, world):
# call base
Metric.__init__(self, world)
self.name = "patterns"
self.value = 0
