def copy_table(tbl,
start=0,
stop=None,
blen=None,
storage=None,
create='table',
**kwargs):
"""Copy `tbl` block-wise into a new table."""
# setup
names, columns = _util.check_table_like(tbl)
storage = _util.get_storage(storage)
blen = _util.get_blen_table(tbl, blen)
if stop is None:
stop = len(columns[0])
else:
stop = min(stop, len(columns[0]))
length = stop - start
if length < 0:
raise ValueError('invalid stop/start')
# copy block-wise
out = None
for i in range(start, stop, blen):
j = min(i + blen, stop)
res = [c[i:j] for c in columns]
if out is None:
out = getattr(storage, create)(res,
names=names,
expectedlen=length,
**kwargs)
else:
out.append(res)
return out
def copy_table(tbl, start=0, stop=None, blen=None, storage=None,
create='table', **kwargs):
"""Copy `tbl` block-wise into a new table."""
# setup
names, columns = _util.check_table_like(tbl)
storage = _util.get_storage(storage)
blen = _util.get_blen_table(tbl, blen)
if stop is None:
stop = len(columns[0])
else:
stop = min(stop, len(columns[0]))
length = stop - start
if length < 0:
raise ValueError('invalid stop/start')
# copy block-wise
out = None
for i in range(start, stop, blen):
j = min(i+blen, stop)
res = [np.asanyarray(c[i:j]) for c in columns]
if out is None:
out = getattr(storage, create)(res, names=names,
expectedlen=length, **kwargs)
else:
out.append(res)
return out
def concatenate_table(tup, blen=None, storage=None, create='table', **kwargs):
"""Stack tables in sequence vertically (row-wise)."""
# setup
storage = _util.get_storage(storage)
if not isinstance(tup, (tuple, list)):
raise ValueError('expected tuple or list, found %r' % tup)
if len(tup) < 2:
raise ValueError('expected two or more tables to stack')
# build output
expectedlen = sum(len(t) for t in tup)
out = None
tnames = None
for tdata in tup:
tblen = _util.get_blen_table(tdata, blen)
tnames, tcolumns = _util.check_table_like(tdata, names=tnames)
tlen = len(tcolumns[0])
for i in range(0, tlen, tblen):
j = min(i + tblen, tlen)
bcolumns = [c[i:j] for c in tcolumns]
if out is None:
out = getattr(storage, create)(bcolumns,
names=tnames,
expectedlen=expectedlen,
**kwargs)
else:
out.append(bcolumns)
return out
def vstack_table(tup, blen=None, storage=None, create='table', **kwargs):
"""Stack tables in sequence vertically (row-wise)."""
# setup
storage = _util.get_storage(storage)
if not isinstance(tup, (tuple, list)):
raise ValueError('expected tuple or list, found %r' % tup)
if len(tup) < 2:
raise ValueError('expected two or more tables to stack')
# build output
expectedlen = sum(len(t) for t in tup)
out = None
tnames = None
for tdata in tup:
tblen = _util.get_blen_table(tdata, blen)
tnames, tcolumns = _util.check_table_like(tdata, names=tnames)
tlen = len(tcolumns[0])
for i in range(0, tlen, tblen):
j = min(i+tblen, tlen)
bcolumns = [np.asanyarray(c[i:j]) for c in tcolumns]
if out is None:
out = getattr(storage, create)(bcolumns, names=tnames,
expectedlen=expectedlen,
**kwargs)
else:
out.append(bcolumns)
return out
def compress_table(tbl, condition, blen=None, storage=None, create='table',
**kwargs):
"""Return selected rows of a table."""
# setup
storage = _util.get_storage(storage)
names, columns = _util.check_table_like(tbl)
blen = _util.get_blen_table(tbl, blen)
_util.check_equal_length(columns[0], condition)
length = len(columns[0])
nnz = count_nonzero(condition)
# block iteration
out = None
for i in range(0, length, blen):
j = min(i+blen, length)
bcond = np.asanyarray(condition[i:j])
# don't access any data unless we have to
if np.any(bcond):
bcolumns = [np.asanyarray(c[i:j]) for c in columns]
res = [np.compress(bcond, c, axis=0) for c in bcolumns]
if out is None:
out = getattr(storage, create)(res, names=names,
expectedlen=nnz, **kwargs)
else:
out.append(res)
return out
def compress_table(tbl, condition, blen=None, storage=None, create='table',
**kwargs):
"""Return selected rows of a table."""
# setup
storage = _util.get_storage(storage)
names, columns = _util.check_table_like(tbl)
blen = _util.get_blen_table(tbl, blen)
_util.check_equal_length(columns[0], condition)
length = len(columns[0])
nnz = count_nonzero(condition)
# block iteration
out = None
for i in range(0, length, blen):
j = min(i+blen, length)
bcond = np.asanyarray(condition[i:j])
# don't access any data unless we have to
if np.any(bcond):
bcolumns = [np.asanyarray(c[i:j]) for c in columns]
res = [np.compress(bcond, c, axis=0) for c in bcolumns]
if out is None:
out = getattr(storage, create)(res, names=names,
expectedlen=nnz, **kwargs)
else:
out.append(res)
return out
def eval_table(tbl,
expression,
vm='python',
blen=None,
storage=None,
create='array',
vm_kwargs=None,
**kwargs):
"""Evaluate `expression` against columns of a table."""
# setup
storage = _util.get_storage(storage)
names, columns = _util.check_table_like(tbl)
length = len(columns[0])
if vm_kwargs is None:
vm_kwargs = dict()
# setup vm
if vm == 'numexpr':
import numexpr
evaluate = numexpr.evaluate
elif vm == 'python':
# noinspection PyUnusedLocal
def evaluate(expr, local_dict=None, **kw):
# takes no keyword arguments
return eval(expr, dict(), local_dict)
else:
raise ValueError('expected vm either "numexpr" or "python"')
# compile expression and get required columns
variables = _get_expression_variables(expression, vm)
required_columns = {v: columns[names.index(v)] for v in variables}
# determine block size for evaluation
blen = _util.get_blen_table(required_columns, blen=blen)
# build output
out = None
for i in range(0, length, blen):
j = min(i + blen, length)
blocals = {v: c[i:j] for v, c in required_columns.items()}
res = evaluate(expression, local_dict=blocals, **vm_kwargs)
if out is None:
out = getattr(storage, create)(res, expectedlen=length, **kwargs)
else:
out.append(res)
return out
def eval_table(tbl, expression, vm='python', blen=None, storage=None,
create='array', vm_kwargs=None, **kwargs):
"""Evaluate `expression` against columns of a table."""
# setup
storage = _util.get_storage(storage)
names, columns = _util.check_table_like(tbl)
length = len(columns[0])
if vm_kwargs is None:
vm_kwargs = dict()
# setup vm
if vm == 'numexpr':
import numexpr
evaluate = numexpr.evaluate
elif vm == 'python':
# noinspection PyUnusedLocal
def evaluate(expr, local_dict=None, **kw):
# takes no keyword arguments
return eval(expr, dict(), local_dict)
else:
raise ValueError('expected vm either "numexpr" or "python"')
# compile expression and get required columns
variables = _get_expression_variables(expression, vm)
required_columns = {v: columns[names.index(v)] for v in variables}
# determine block size for evaluation
blen = _util.get_blen_table(required_columns, blen=blen)
# build output
out = None
for i in range(0, length, blen):
j = min(i+blen, length)
blocals = {v: c[i:j] for v, c in required_columns.items()}
res = evaluate(expression, local_dict=blocals, **vm_kwargs)
if out is None:
out = getattr(storage, create)(res, expectedlen=length, **kwargs)
else:
out.append(res)
return out
def compress_table(condition,
tbl,
axis=None,
out=None,
blen=None,
storage=None,
create='table',
**kwargs):
"""Return selected rows of a table."""
# setup
if axis is not None and axis != 0:
raise NotImplementedError('only axis 0 is supported')
if out is not None:
# argument is only there for numpy API compatibility
raise NotImplementedError('out argument is not supported')
storage = _util.get_storage(storage)
names, columns = _util.check_table_like(tbl)
blen = _util.get_blen_table(tbl, blen)
_util.check_equal_length(columns[0], condition)
length = len(columns[0])
nnz = count_nonzero(condition)
# block iteration
out = None
for i in range(0, length, blen):
j = min(i + blen, length)
bcond = condition[i:j]
# don't access any data unless we have to
if np.any(bcond):
bcolumns = [c[i:j] for c in columns]
res = [np.compress(bcond, c, axis=0) for c in bcolumns]
if out is None:
out = getattr(storage, create)(res,
names=names,
expectedlen=nnz,
**kwargs)
else:
out.append(res)
return out
