def test_misc():
# Sparsity function
s = SparseArray(10, 0)
assert s.sparsity() == 1.0
s[0] = 1
assert s.sparsity() == 0.9
s[1] = 1
assert s.sparsity() == 0.8
# items() generator
s = SparseArray(10, 0)
s[5] = 1
s[2] = 4
assert list(s.items()) == [(2,4), (5,1)]
class SparseAlleles(AlleleContainer):
'''
An object representing a set of haploid genotypes efficiently by
storing allele differences from a reference. Useful for manipulating
genotypes from sequence data (e.g. VCF files)
In the interest of conserving memory for sequencing data, all alleles must
be represented by a signed 8-bit integer (i.e. between -128 and 127).
Negative values are interpreted as missing.
'''
def __init__(self, data=None, refcode=0, missingcode=-1, size=None, template=None):
self.template = template
self.refcode = refcode
if data is None:
if template is None and size is None:
raise ValueError('No template or size')
elif template is not None and size is None:
size = self.template.nmark()
self.container = SparseArray(size, refcode)
return
if type(data) is SparseArray:
raise NotImplementedError
else:
if not isinstance(data, np.ndarray):
data = np.array(data)
self.container = SparseArray.from_dense(data, refcode)
self.size = len(self.container)
def __getitem__(self, key):
return self.container[key]
def __setitem__(self, key, value):
self.container[key] = value
def keys(self):
return self.container.keys()
def values(self):
return self.container.values()
@property
def missingcode(self):
return -1
@property
def missing(self):
" Returns a numpy array indicating which markers have missing data "
missingindices = [i for i,v in self.container.items() if v == self.missingcode]
base = np.zeros(self.size, dtype=np.bool_)
base[missingindices] = 1
return base
def __eq__(self, other):
if type(other) is SparseAlleles:
return self.container == other.container
else:
return self.container == other
def __ne__(self, other):
if type(other) is SparseAlleles:
return self.container != other.container
else:
return self.container != other
def nmark(self):
'''
Return the number of markers (both reference and non-reference)
represented by the SparseAlleles object
'''
return self.container.size
def todense(self):
dense = Alleles(self.container.tolist(), template=self.template)
return dense
def empty_like(self):
output = SparseAlleles(template=self.template,
missingcode=self.missingcode,
refcode=self.refcode, size=self.nmark())
return output
def copy_span(self, template, copy_start, copy_stop):
if isinstance(template, SparseAlleles):
self.container[copy_start:copy_stop] = template.container[copy_start:copy_stop]
else:
self.container = template[copy_start:copy_stop]
def copy(self):
outp = self.empty_like()
outp.container = self.container.copy()
return outp
@staticmethod
def empty(reference=None, template=None, missingcode=''):
out = SparseAlleles(size, template=template, missingcode=missingcode)
return out
