def infer_datashape(source):
if isinstance(source, np.ndarray):
return from_numpy(source.shape, source.dtype)
elif isinstance(source, list):
# TODO: um yeah, we'd don't actually want to do this
cast = np.array(source)
return from_numpy(cast.shape, cast.dtype)
else:
return dynamic
def infer_datashape(source):
if isinstance(source, np.ndarray):
return from_numpy(source.shape, source.dtype)
elif isinstance(source, list):
# TODO: um yeah, we'd don't actually want to do this
cast = np.array(source)
return from_numpy(cast.shape, cast.dtype)
else:
return dynamic
def infer_datashape(source):
"""
The user has only provided us with a Python object (
could be a buffer interface, a string, a list, list of
lists, etc) try our best to infer what the datashape
should be in the context of this datasource.
"""
if isinstance(source, np.ndarray):
return from_numpy(source.shape, source.dtype)
elif isinstance(source, list):
# TODO: um yeah, we'd don't actually want to do this
cast = np.array(source)
return from_numpy(cast.shape, cast.dtype)
else:
return dynamic
def infer_datashape(source):
"""
The user has only provided us with a Python object (
could be a buffer interface, a string, a list, list of
lists, etc) try our best to infer what the datashape
should be in the context of this datasource.
"""
if isinstance(source, np.ndarray):
return from_numpy(source.shape, source.dtype)
elif isinstance(source, list):
# TODO: um yeah, we'd don't actually want to do this
cast = np.array(source)
return from_numpy(cast.shape, cast.dtype)
else:
return dynamic
def empty(self, dshape):
""" Create a CArraySource from a datashape specification,
downcasts into Numpy dtype and shape tuples if possible
otherwise raises an exception.
"""
shape, dtype = from_numpy(dshape)
return CArraySource(carray([], dtype))
def __init__(self, data=None, dshape=None, params=None):
# need at least one of the three
assert (data is not None) or (dshape is not None) or \
(params.get('storage'))
# Extract the relevant carray parameters from the more
# general Blaze params object.
if params:
cparams, rootdir, format_flavor = to_cparams(params)
else:
rootdir, cparams = None, None
if isinstance(data, CArraySource):
data = data.ca
dshape = dshape if dshape else data.dshape
if dshape:
shape, dtype = to_numpy(dshape)
self.ca = carray.carray(data,
dtype=dtype,
rootdir=rootdir,
cparams=cparams)
self.dshape = dshape
else:
self.ca = carray.carray(data, rootdir=rootdir, cparams=cparams)
self.dshape = from_numpy(self.ca.shape, self.ca.dtype)
def empty(self, dshape):
""" Create a CArraySource from a datashape specification,
downcasts into Numpy dtype and shape tuples if possible
otherwise raises an exception.
"""
shape, dtype = from_numpy(dshape)
return CArraySource(carray([], dtype))
def __init__(self, data, dshape=None, metadata=None, layout=None,
params=None):
# Datashape
# ---------
if isinstance(dshape, basestring):
dshape = _dshape(dshape)
if not dshape:
# The user just passed in a raw data source, try
# and infer how it should be layed out or fail
# back on dynamic types.
self._datashape = dshape = CTableSource.infer_datashape(data)
else:
# The user overlayed their custom dshape on this
# data, check if it makes sense
CTableSource.check_datashape(data, given_dshape=dshape)
self._datashape = dshape
# Source
# ------
if isinstance(data, ByteProvider):
self.data = data
if isinstance(data, dict):
ct = self.from_dict(data)
self._axes = data.keys()
dshape = from_numpy(ct.shape, ct.dtype)
self.data = CTableSource(ct, dshape=dshape, params=params)
self._datashape = dshape
elif isinstance(data, (list, tuple)):
self.data = CTableSource(data, dshape=dshape, params=params)
# Pull the labels from the datashape
self._axes = self._datashape[-1].names
else:
raise ValueError
# children graph nodes
self.children = []
self.space = Space(self.data)
# Layout
# ------
if layout:
self._layout = layout
elif not layout:
self._layout = self.data.default_layout()
# Metadata
# --------
self._metadata = NDTable._metaheader + (metadata or [])
# Parameters
# ----------
self.params = params
def __init__(self, data=None, dshape=None, params=None):
# need at least one of the three
assert (data is not None) or (dshape is not None) or \
(params.get('storage'))
# Extract the relevant carray parameters from the more
# general Blaze params object.
if params:
cparams, rootdir, format_flavor = to_cparams(params)
else:
rootdir,cparams = None, None
if dshape:
shape, dtype = to_numpy(dshape)
self.ca = carray.carray(data, dtype=dtype, rootdir=rootdir, cparams=cparams)
self.dshape = dshape
else:
self.ca = carray.carray(data, rootdir=rootdir, cparams=cparams)
self.dshape = from_numpy(self.ca.shape, self.ca.dtype)
def empty(self, datashape):
shape, dtype = from_numpy(datashape)
return ArraySource(np.ndarray(shape, dtype))
def empty(self, dshape):
shape, dtype = from_numpy(dshape)
return CTableSource(carray([[]], dtype))
def __getitem__(self, mask):
ct = (self.data.ca[mask])
dshape = from_numpy(ct.shape, ct.dtype)
source = CTableSource(ct, dshape=dshape)
return Table(source, dshape=dshape)
def empty(self, dshape):
shape, dtype = from_numpy(dshape)
return CTableSource(carray([[]], dtype))
def empty(self, datashape):
shape, dtype = from_numpy(datashape)
return ArraySource(np.ndarray(shape, dtype))
def __init__(self,
data,
dshape=None,
metadata=None,
layout=None,
params=None):
# Datashape
# ---------
if isinstance(dshape, basestring):
dshape = _dshape(dshape)
if not dshape:
# The user just passed in a raw data source, try
# and infer how it should be layed out or fail
# back on dynamic types.
self._datashape = dshape = CTableSource.infer_datashape(data)
else:
# The user overlayed their custom dshape on this
# data, check if it makes sense
CTableSource.check_datashape(data, given_dshape=dshape)
self._datashape = dshape
# Source
# ------
if isinstance(data, ByteProvider):
self.data = data
if isinstance(data, dict):
ct = self.from_dict(data)
self._axes = data.keys()
dshape = from_numpy(ct.shape, ct.dtype)
self.data = CTableSource(ct, dshape=dshape, params=params)
self._datashape = dshape
elif isinstance(data, (list, tuple)):
self.data = CTableSource(data, dshape=dshape, params=params)
# Pull the labels from the datashape
self._axes = self._datashape[-1].names
else:
raise ValueError
# children graph nodes
self.children = []
self.space = Space(self.data)
# Layout
# ------
if layout:
self._layout = layout
elif not layout:
self._layout = self.data.default_layout()
# Metadata
# --------
self._metadata = NDTable._metaheader + (metadata or [])
# Parameters
# ----------
self.params = params
def __getitem__(self, mask):
ct = (self.data.ca[mask])
dshape = from_numpy(ct.shape, ct.dtype)
source = CTableSource(ct, dshape=dshape)
return Table(source, dshape=dshape)
def append(self, data):
self.data.ca.append(data)
# Update the shape
shape, dtype = self.data.ca.shape, self.data.ca.dtype
self._datashape = from_numpy(shape, dtype)
def append(self, data):
self.data.ca.append(data)
# Update the shape
shape, dtype = self.data.ca.shape, self.data.ca.dtype
self._datashape = from_numpy(shape, dtype)