def exclude(self, other):
"""
Remove coordinates derived from another Source or an array.
If other is an array, will remove coordinates of all
non-zero elements from this source. If other is a source,
will remove any matching coordinates.
Parameters
----------
other : ndarray or Source
Source to remove
"""
if isinstance(other, ndarray):
coordsOther = asarray(where(other)).T
else:
coordsOther = aslist(other.coordinates)
coordsSelf = aslist(self.coordinates)
newid = self.id if hasattr(self, 'id') else None
if hasattr(self, 'values') and self.values is not None:
valuesSelf = self.values
complement = [(c, v) for c, v in zip(coordsSelf, valuesSelf)
if c not in coordsOther]
newcoords, newvalues = zip(*complement)
return Source(coordinates=newcoords, values=newvalues, id=newid)
else:
complement = [a for a in coordsSelf if a not in coordsOther]
return Source(coordinates=complement, id=newid)
def exclude(self, other):
"""
Remove coordinates derived from another Source or an array.
If other is an array, will remove coordinates of all
non-zero elements from this source. If other is a source,
will remove any matching coordinates.
Parameters
----------
other : ndarray or Source
Source to remove
"""
if isinstance(other, ndarray):
coordsOther = asarray(where(other)).T
else:
coordsOther = aslist(other.coordinates)
coordsSelf = aslist(self.coordinates)
newid = self.id if hasattr(self, "id") else None
if hasattr(self, "values") and self.values is not None:
valuesSelf = self.values
complement = [(c, v) for c, v in zip(coordsSelf, valuesSelf) if c not in coordsOther]
newcoords, newvalues = zip(*complement)
return Source(coordinates=newcoords, values=newvalues, id=newid)
else:
complement = [a for a in coordsSelf if a not in coordsOther]
return Source(coordinates=complement, id=newid)
def overlap(self, other, method='support', counts=False, symmetric=True):
"""
Compute the overlap between this source and other, in terms
of either support or similarity of coefficients.
Support computes the number of overlapping pixels relative
to the union of both sources. Correlation computes the similarity
of the weights (not defined for binary masks).
Parameters
----------
other : Source
The source to compute overlap with.
method : str
Compare either support of source coefficients ('support'), or the
source spatial filters (not yet implemented).
counts : boolean, optional, default = True
Whether to return raw counts when computing support, otherwise
return a fraction.
"""
checkParams(method, ['support', 'corr'])
coordsSelf = aslist(self.coordinates)
coordsOther = aslist(other.coordinates)
intersection = [a for a in coordsSelf if a in coordsOther]
complementLeft = [a for a in coordsSelf if a not in intersection]
complementRight = [a for a in coordsOther if a not in intersection]
hits = len(intersection)
if symmetric is True:
misses = len(complementLeft + complementRight)
else:
misses = len(complementLeft)
if method == 'support':
if counts:
return hits, misses
else:
return hits/float(hits+misses)
if method == 'corr':
from scipy.stats import spearmanr
if not (hasattr(self, 'values') and hasattr(other, 'values')):
raise Exception('Sources must have values to compute correlation')
else:
valuesSelf = aslist(self.values)
valuesOther = aslist(other.values)
if len(intersection) > 0:
rho, _ = spearmanr(valuesSelf[intersection], valuesOther[intersection])
else:
rho = 0.0
return (rho * hits)/float(hits + misses)
def overlap(self, other, method="fraction"):
"""
Compute the overlap between this source and other.
Options are a symmetric measure of overlap based on the fraction
of intersecting pixels relative to the union ('fraction'), an assymmetric
measure of overlap that expresses detected intersecting pixels
(relative to this source) using precision and recall rates ('rates'), or
a correlation coefficient of the weights within the intersection
(not defined for binary weights) ('correlation')
Parameters
----------
other : Source
The source to compute overlap with.
method : str
Which estimate of overlap to compute, options are
'fraction' (symmetric) 'rates' (asymmetric) or 'correlation'
"""
checkParams(method, ["fraction", "rates", "correlation"])
coordsSelf = aslist(self.coordinates)
coordsOther = aslist(other.coordinates)
intersection = [a for a in coordsSelf if a in coordsOther]
nhit = float(len(intersection))
ntotal = float(len(set([tuple(x) for x in coordsSelf] + [tuple(x) for x in coordsOther])))
if method == "rates":
recall = nhit / len(coordsSelf)
precision = nhit / len(coordsOther)
return recall, precision
if method == "fraction":
return nhit / float(ntotal)
if method == "correlation":
from scipy.stats import spearmanr
if not (hasattr(self, "values") and hasattr(other, "values")):
raise ValueError("Sources must have values to compute correlation")
else:
valuesSelf = aslist(self.values)
valuesOther = aslist(other.values)
if len(intersection) > 0:
left = [v for v, c in zip(valuesSelf, coordsSelf) if c in coordsOther]
right = [v for v, c in zip(valuesOther, coordsOther) if c in coordsSelf]
rho, _ = spearmanr(left, right)
else:
rho = 0.0
return rho
def overlap(self, other, method='fraction'):
"""
Compute the overlap between this source and other.
Options are a symmetric measure of overlap based on the fraction
of intersecting pixels relative to the union ('fraction'), an assymmetric
measure of overlap that expresses detected intersecting pixels
(relative to this source) using precision and recall rates ('rates'), or
a correlation coefficient of the weights within the intersection
(not defined for binary weights) ('correlation')
Parameters
----------
other : Source
The source to compute overlap with.
method : str
Which estimate of overlap to compute, options are
'fraction' (symmetric) 'rates' (asymmetric) or 'correlation'
"""
checkParams(method, ['fraction', 'rates', 'correlation'])
coordsSelf = aslist(self.coordinates)
coordsOther = aslist(other.coordinates)
intersection = [a for a in coordsSelf if a in coordsOther]
nhit = float(len(intersection))
ntotal = float(
len(
set([tuple(x)
for x in coordsSelf] + [tuple(x) for x in coordsOther])))
if method == 'rates':
recall = nhit / len(coordsSelf)
precision = nhit / len(coordsOther)
return recall, precision
if method == 'fraction':
return nhit / float(ntotal)
if method == 'correlation':
from scipy.stats import spearmanr
if not (hasattr(self, 'values') and hasattr(other, 'values')):
raise ValueError(
'Sources must have values to compute correlation')
else:
valuesSelf = aslist(self.values)
valuesOther = aslist(other.values)
if len(intersection) > 0:
left = [
v for v, c in zip(valuesSelf, coordsSelf)
if c in coordsOther
]
right = [
v for v, c in zip(valuesOther, coordsOther)
if c in coordsSelf
]
rho, _ = spearmanr(left, right)
else:
rho = 0.0
return rho