def load_pytables(cls, filename, copy=False):
import numpy as np
from csc.divisi.pyt_utils import get_pyt_handle
fileh = get_pyt_handle(filename)
root = fileh.root
def wrapped_tensor(name):
data = getattr(root, name)
if copy:
data = data.read()
return DenseTensor(data)
def wrapped_labeled_tensor(name, label_name):
return wrapped_tensor(name).labeled(list(getattr(root, label_name)))
u = wrapped_labeled_tensor("u", "u_labels")
v = wrapped_labeled_tensor("v", "v_labels")
svals = wrapped_tensor("svals")
svd = cls(u, v, svals)
svd._weighted_u = wrapped_tensor("weighted_u").labeled(u.label_lists())
svd._weighted_v = wrapped_tensor("weighted_v").labeled(v.label_lists())
svd._core = wrapped_tensor("core")
return svd
def save_pytables(self, filename, title='SVD results', filters=None, **kw):
from csc.divisi.pyt_utils import get_pyt_handle
from tables import ObjectAtom, Filters, Atom
fileh = get_pyt_handle(filename, title)
if filters is None and kw:
filters = Filters(**kw)
try:
root = fileh.root
def store_tensor(name, tensor):
data = tensor._data
arr = fileh.createCArray(root, name, Atom.from_dtype(data.dtype), tensor.shape, filters=filters)
arr[:] = data
# Labeled stuff
for name in ('u', 'v', 'weighted_u', 'weighted_v'):
store_tensor(name, getattr(self, name).tensor)
# Unlabeled stuff
for name in ('svals', 'core'):
store_tensor(name, getattr(self, name))
# Ordered sets
def write_labels(name, view):
arr = fileh.createVLArray(root, name, ObjectAtom(), filters=filters)
for label in view.label_lists():
arr.append(label)
write_labels('u_labels', self.u)
write_labels('v_labels', self.v)
finally:
fileh.close()
def save_pytables(self, filename, title="SVD results", filters=None, **kw):
from csc.divisi.pyt_utils import get_pyt_handle
from tables import ObjectAtom, Filters, Atom
fileh = get_pyt_handle(filename, title)
if filters is None and kw:
filters = Filters(**kw)
try:
root = fileh.root
def store_tensor(name, tensor):
data = tensor._data
arr = fileh.createCArray(root, name, Atom.from_dtype(data.dtype), tensor.shape, filters=filters)
arr[:] = data
# Labeled stuff
for name in ("u", "v", "weighted_u", "weighted_v"):
store_tensor(name, getattr(self, name).tensor)
# Unlabeled stuff
for name in ("svals", "core"):
store_tensor(name, getattr(self, name))
# Ordered sets
def write_labels(name, view):
arr = fileh.createVLArray(root, name, ObjectAtom(), filters=filters)
for label in view.label_lists():
arr.append(label)
write_labels("u_labels", self.u)
write_labels("v_labels", self.v)
finally:
fileh.close()
def create(cls, filename, ndim, pt_path='/', pt_name='tensor', filters=None):
'''
Create a new PyTables tensor.
pt_path and pt_name are the "path" and "filename" within the
PyTables file.
Raises tables.NodeError if the table already exists.
'''
fileh = get_pyt_handle(filename)
table = fileh.createTable(pt_path, pt_name, cls.descriptor(ndim), filters=filters)
return cls(table)
def open(cls, filename, pt_path='/', pt_name='tensor'):
'''
Open an existing PyTables tensor.
pt_path and pt_name are the "path" and "filename" within the
PyTables file.
Raises a tables.NoSuchNodeError if the table doesn't exist.
(FIXME: it will still create a new, empty file.)
'''
fileh = get_pyt_handle(filename)
table = fileh.getNode(pt_path, pt_name)
return cls(table)
def open(cls, filename, pt_path='/', pt_name='tensor'):
'''
Open an existing PyTables tensor.
pt_path and pt_name are the "path" and "filename" within the
PyTables file.
Raises a tables.NoSuchNodeError if the table doesn't exist.
(FIXME: it will still create a new, empty file.)
'''
fileh = get_pyt_handle(filename)
table = fileh.getNode(pt_path, pt_name)
return cls(table)
def load_pytables(cls, filename, copy=False):
import numpy as np
from csc.divisi.pyt_utils import get_pyt_handle
fileh = get_pyt_handle(filename)
root = fileh.root
def wrapped_tensor(name):
data = getattr(root, name)
if copy: data = data.read()
return DenseTensor(data)
def wrapped_labeled_tensor(name, label_name):
return wrapped_tensor(name).labeled(list(getattr(root, label_name)))
u = wrapped_labeled_tensor('u', 'u_labels')
v = wrapped_labeled_tensor('v', 'v_labels')
svals = wrapped_tensor('svals')
svd = cls(u, v, svals)
svd._weighted_u = wrapped_tensor('weighted_u').labeled(u.label_lists())
svd._weighted_v = wrapped_tensor('weighted_v').labeled(v.label_lists())
svd._core = wrapped_tensor('core')
return svd
def create(cls,
filename,
ndim,
pt_path='/',
pt_name='tensor',
filters=None):
'''
Create a new PyTables tensor.
pt_path and pt_name are the "path" and "filename" within the
PyTables file.
Raises tables.NodeError if the table already exists.
'''
fileh = get_pyt_handle(filename)
table = fileh.createTable(pt_path,
pt_name,
cls.descriptor(ndim),
filters=filters)
return cls(table)
def create(cls, filename, pt_path, pt_name, filters=None):
fileh = get_pyt_handle(filename)
array = fileh.createVLArray(pt_path, pt_name, tables.ObjectAtom(), filters=filters)
return cls(array)
def open(cls, filename, pt_path, pt_name):
fileh = get_pyt_handle(filename)
array = fileh.getNode(pt_path, pt_name)
return cls(array)
