def __init__(self, name, array, conditions=None):
self.data = array
if conditions is not None and isinstance(conditions, list):
self.conditions = conditions
Dataset.__init__(self, '{}'.format(name))
def __init__(self, name, garray, gindexer, alphabetsize, channel_last):
self.garray = garray
self.gindexer = gindexer
self._alphabetsize = alphabetsize
self._rcindex = [_complement_index(idx, garray.order)
for idx in range(pow(alphabetsize, garray.order))]
self._channel_last = channel_last
Dataset.__init__(self, '{}'.format(name))
def __init__(self, name, array, conditions=None):
if conditions is not None:
assert array.shape[-1] == len(conditions), \
"array shape and condition number does not agree: {} != {}" \
.format(array.shape[-1], len(conditions))
self.data = copy.copy(array)
self.conditions = conditions
Dataset.__init__(self, '{}'.format(name))
def __init__(self, name, garray, gindexer, alphabet):
self.garray = garray
self.gindexer = gindexer
self._alphabet = alphabet
self.conditions = [
''.join(item) for item in product(sorted(self._alphabet),
repeat=self.garray.order)
]
self._alphabetsize = len(self._alphabet)
self._rcindex = [
_complement_index(idx, garray.order)
for idx in range(pow(self._alphabetsize, garray.order))
]
Dataset.__init__(self, '{}'.format(name))
def __init__(
self,
name,
garray,
gindexer, # indices of pointing to region start
padding_value,
dimmode,
channel_last): # padding value
self.garray = garray
self.gindexer = gindexer
self.padding_value = padding_value
self.dimmode = dimmode
self._channel_last = channel_last
Dataset.__init__(self, name)
def __init__(self, name, filename, conditions=None, dtype='int8', sep=','):
self.filename = filename
if conditions is None:
conditions = [
os.path.splitext(os.path.basename(f))[0] for f in filename
]
self.conditions = conditions
data = []
for _file in self.filename:
data.append(pd.read_csv(_file, header=None, sep=sep, dtype=dtype))
self.data = pd.concat(data, axis=1, ignore_index=True).values
Dataset.__init__(self, name)
def __init__(self, array, aggregator=None, axis=None):
self.data = copy.copy(array)
if aggregator is None:
aggregator = 'sum'
def _get_aggregator(name):
if callable(name):
return name
if name == 'sum':
return np.sum
elif name == 'mean':
return np.mean
elif name == 'max':
return np.max
raise ValueError('ReduceDim aggregator="{}" not known.'.format(name) +
'Must be "sum", "mean" or "max" or a callable.')
self.aggregator = _get_aggregator(aggregator)
self.axis = axis if axis is not None else (1, 2)
Dataset.__init__(self, array.name)
def __init__(self, array):
self.data = copy.copy(array)
Dataset.__init__(self, array.name)
def __init__(self, array, deviance):
self.data = copy.copy(array)
self.deviance = deviance
Dataset.__init__(self, array.name)
def __init__(self, array, axis):
self.data = copy.copy(array)
self.axis = axis
Dataset.__init__(self, array.name)