class NetCDFPlus(netCDF4.Dataset):
"""
Extension of the python netCDF wrapper for easier storage of python objects
"""
support_simtk_unit = True
_type_conversion = {
'float': np.float32,
'int': np.int32,
'long': np.int64,
'index': np.int32,
'length': np.int32,
'bool': np.int16,
'str': str,
'json': str,
'numpy.float32': np.float32,
'numpy.float64': np.float64,
'numpy.int8': np.int8,
'numpy.int16': np.int16,
'numpy.int32': np.int32,
'numpy.int64': np.int64,
'numpy.uint8': np.uint8,
'numpy.uint16': np.uint16,
'numpy.uint32': np.uint32,
'numpy.uint64': np.uint64,
'store': str
}
class ValueDelegate(object):
"""
Value delegate for objects that implement __getitem__ and __setitem__
It will basically just wrap values that are used in a dict like structure
with getter and setter function to allow easier conversion
delegate[x] is equivalent to delegate.getter(delegate.variable[x])
Attributes
----------
variable : dict-like
the dict to be wrapped
getter : function
the function applied to results from running the __getitem__ on the variable
setter : function
the function applied to the value to be stored using __setitem__ on the variable
store : openpathsampling.storage.ObjectStore
a reference to an object store used for convenience in some cases
"""
def __init__(self, variable, getter=None, setter=None, store=None):
self.variable = variable
self.store = store
if setter is None:
setter = lambda v: v
self.setter = setter
if getter is None:
getter = lambda v: v
self.getter = getter
def __setitem__(self, key, value):
self.variable[key] = self.setter(value)
def __getitem__(self, key):
return self.getter(self.variable[key])
def __getattr__(self, item):
return getattr(self.variable, item)
def __str__(self):
return str(self.variable)
def __repr__(self):
return repr(self.variable)
class KeyDelegate(object):
"""
Value delegate for objects that implement __getitem__ and __setitem__
It will basically just wrap keys for objects that are used in a dict like structure
with getter and setter function to allow easier conversion
delegate[x] is equivalent to delegate[x.idx(store)]
Attributes
----------
variable : dict-like
the dict to be wrapped
store : openpathsampling.storage.ObjectStore
a reference to an object store used
"""
def __init__(self, variable, store):
self.variable = variable
self.store = store
def __setitem__(self, key, value):
if hasattr(key, '__iter__'):
idxs = [
item if type(item) is int else self.store.index[item]
for item in key
]
sorted_idxs = list(set(idxs))
sorted_values = [value[idxs.index(val)] for val in sorted_idxs]
self.variable[sorted_idxs] = sorted_values
else:
self.variable[key if type(key) is int else self.store.
index[key]] = value
def __getitem__(self, key):
if hasattr(key, '__iter__'):
idxs = [
item if type(item) is int else self.store.index[item]
for item in key
]
sorted_idxs = sorted(list(set(idxs)))
sorted_values = self.variable[sorted_idxs]
return [sorted_values[sorted_idxs.index(idx)] for idx in idxs]
else:
return self.variable[key if type(key) is int else self.store.
index[key]]
@property
def objects(self):
"""
Return a dictionary of all objects stored.
This is similar to the netcdf `.variables` for all stored variables. This
allows to write `storage.objects['samples'][idx]` like we
write `storage.variables['ensemble_json'][idx]`
"""
return self._objects
def update_storable_classes(self):
self.simplifier.update_class_list()
def _register_storages(self):
"""
Function to be called automatically to register all object stores
This will usually only be called in subclassed storages.
"""
pass
def __init__(self, filename, mode=None, units=None):
"""
Create a storage for complex objects in a netCDF file
Parameters
----------
filename : string
filename of the netcdf file to be used or created
mode : string, default: None
the mode of file creation, one of 'w' (write), 'a' (append) or 'r' (read-only)
None, which will append any existing files.
units : dict of {str : simtk.unit.Unit } or None
representing a dict of string representing a dimension
('length', 'velocity', 'energy') pointing to
the simtk.unit.Unit to be used. If not `None` it overrides the
standard units used
Notes
-----
A single file can be opened by multiple storages, but only one can be used for writing
"""
if mode is None:
mode = 'a'
exists = os.path.isfile(filename)
if exists and mode == 'a':
logger.info(
"Open existing netCDF file '%s' for appending - appending existing file",
filename)
elif exists and mode == 'w':
logger.info(
"Create new netCDF file '%s' for writing - deleting existing file",
filename)
elif not exists and mode == 'a':
logger.info(
"Create new netCDF file '%s' for appending - appending non-existing file",
filename)
elif not exists and mode == 'w':
logger.info(
"Create new netCDF file '%s' for writing - creating new file",
filename)
elif not exists and mode == 'r':
logger.info(
"Open existing netCDF file '%s' for reading - file does not exist",
filename)
raise RuntimeError("File '%s' does not exist." % filename)
elif exists and mode == 'r':
logger.info(
"Open existing netCDF file '%s' for reading - reading from existing file",
filename)
self.filename = filename
# call netCDF4-python to create or open .nc file
super(NetCDFPlus, self).__init__(filename, mode)
self._setup_class()
if units is not None:
self.dimension_units.update(units)
self._register_storages()
self.simplifier.update_class_list()
if mode == 'w':
logger.info("Setup netCDF file and create variables")
# add shared scalar dimension for everyone
self.create_dimension('scalar', 1)
self._initialize()
logger.info("Finished setting up netCDF file")
elif mode == 'a' or mode == 'r+' or mode == 'r':
logger.debug("Restore the dict of units from the storage")
# Create a dict of simtk.Unit() instances for all netCDF.Variable()
for variable_name in self.variables:
variable = self.variables[variable_name]
if self.support_simtk_unit:
import simtk.unit as u
if hasattr(variable, 'unit_simtk'):
unit_dict = self.simplifier.from_json(
getattr(variable, 'unit_simtk'))
if unit_dict is not None:
unit = self.simplifier.unit_from_dict(unit_dict)
else:
unit = self.simplifier.unit_from_dict(u.Unit({}))
self.units[str(variable_name)] = unit
self.update_delegates()
# After we have restored the units we can load objects from the storage
self._restore()
self.sync()
def _setup_class(self):
"""
Sets the basic properties for the storage
"""
self._objects = {}
self._storages = {}
self._storages_base_cls = {}
self._obj_store = {}
self.simplifier = StorableObjectJSON(self)
self.vars = dict()
self.units = dict()
self.dimension_units = dict()
def add(self, name, store, register_attr=True):
"""
Add a object store to the file
An object store is a special type of variable that allows to store python objects
Parameters
----------
name : str
the name of the store under which the objects are accessible like `store.{name}`
store : openpathsampling.storages.ObjectStore
instance of the object store
register_attr : bool, default: True
if set to false the store will not be accesible as an attribute
"""
store.register(self, name)
if register_attr:
if hasattr(self, name):
raise ValueError('Attribute name %s is already in use!' % name)
setattr(self, store.prefix, store)
self._objects[name] = store
self._storages[store.content_class] = store
self._obj_store[store.content_class] = store
self._obj_store.update(
{cls: store
for cls in store.content_class.descendants()})
def _initialize(self):
"""
Function run after a new file is created.
This is used to setup all variables in the storage
"""
pass
def _restore(self):
"""
Function run after an existing file is opened.
This is used in special storages to complete reading existing files.
"""
pass
def __repr__(self):
return "Storage @ '" + self.filename + "'"
def __getattr__(self, item):
return self.__dict__[item]
def __setattr__(self, key, value):
self.__dict__[key] = value
def _init_storages(self):
"""
Run the initialization on all added classes
Notes
-----
Only runs when the storage is created.
"""
for storage in self._objects.values():
storage._init()
self.update_delegates()
def _restore_storages(self):
"""
Run the restore method on all added classes
Notes
-----
Only runs when an existing storage is opened.
"""
for storage in self._objects.values():
storage._restore()
def _initialize_netcdf(self):
"""
Initialize the netCDF+ file for storage itself.
"""
pass
def list_stores(self):
"""
Return a list of registered stores
Returns
-------
list of str
list of stores that can be accessed using `storage.[store]`
"""
return [store.prefix for store in self.objects.values()]
def list_storable_objects(self):
"""
Return a list of storable object base classes
Returns
-------
list of class
list of base classes that can be stored using `storage.save(obj)`
"""
return [store.content_class for store in self.objects.values()]
def find_store(self, obj):
return self._obj_store.get(obj.__class__, None)
def save(self, obj, idx=None):
"""
Save a storable object into the correct Storage in the netCDF file
Parameters
----------
obj : the object to store
Returns
-------
str
the class name of the BaseClass of the stored object, which is
needed when loading the object to identify the correct storage
"""
if type(obj) is list:
# a list of objects will be stored one by one
[self.save(part, idx) for part in obj]
return
elif type(obj) is tuple:
# a tuple will store all parts
[self.save(part, idx) for part in obj]
return
elif obj.__class__ in self._obj_store:
# to store we just check if the base_class is present in the storages
# also we assume that if a class has no base_cls
store = self._obj_store[obj.__class__]
store.save(obj, idx)
return
# Could not save this object.
raise RuntimeWarning("Objects of type '%s' cannot be stored!" %
obj.__class__.__name__)
def load(self, obj_type, idx):
"""
Load an object of the specified type from the storage
Parameters
----------
obj_type : str or class
the store or class of the base object to be loaded.
Returns
-------
object
the object loaded from the storage
Notes
-----
If you want to load a sub-classed Ensemble you need to load using
`Ensemble` or `"Ensemble"` and not use the subclass
"""
if obj_type in self._storages:
store = self._storages[obj_type]
return store.load(idx)
elif obj_type in self._obj_store:
# check if a store for the base_cls exists and use this one
store = self._obj_store[obj_type]
return store.load(idx)
elif obj_type in self.simplifier.class_list:
store = self._obj_store[self.simplifier.class_list[obj_type]]
return store.load(idx)
raise RuntimeError("No store registered to load variable type '%s'" %
obj_type)
def idx(self, obj):
"""
Return the index used to store the given object in this storage
Parameters
----------
obj : object
The stored object from which the index is to be returned
"""
if hasattr(obj, 'base_cls'):
store = self._storages[obj.base_cls]
return store.idx(obj)
def repr_json(self, obj):
if hasattr(obj, 'base_cls'):
store = self._storages[obj.base_cls]
if store.json:
return store.variables['json'][store.idx(obj)]
return None
def clone_storage(self, storage_to_copy, new_storage):
"""
Clone a store from one storage to another. Mainly used as a helper
for the cloning of a store
Parameters
----------
store_to_copy : [..]Store
the store to be copied
new_storage : Storage
the new Storage object
"""
if type(storage_to_copy) is str:
storage_name = storage_to_copy
else:
storage_name = storage_to_copy.prefix
copied_storages = 0
for variable in self.variables.keys():
if variable.startswith(storage_name + '_'):
copied_storages += 1
if variable not in new_storage.variables:
# collectivevariables have additional variables in the storage that need to be copied
# TODO: copy chunksizes?
var = self.variables[variable]
new_storage.createVariable(variable,
str(var.dtype),
var.dimensions,
chunksizes=var.chunk)
for attr in self.variables[variable].ncattrs():
setattr(new_storage.variables[variable], attr,
getattr(self.variables[variable], attr))
new_storage.variables[variable][:] = self.variables[
variable][:]
else:
for idx in range(0, len(self.variables[variable])):
new_storage.variables[variable][idx] = self.variables[
variable][idx]
if copied_storages == 0:
raise RuntimeWarning(
'Potential error in storage name. No storage variables copied from '
+ storage_name)
def create_dimension(self, dim_name, size=None):
"""
Initialize a new dimension in the storage.
Wraps the netCDF createDimension
Parameters
----------
name : str
the name for the new dimension
length : int
the number of elements in this dimension. None (default) means
an infinite dimension that extends when more objects are stored
"""
if dim_name not in self.dimensions:
self.createDimension(dim_name, size)
def cache_image(self):
"""
Return an dict containing information about all caches
Returns
-------
dict
a nested dict containing information about the number and types of
cached objects
"""
image = {
'weak': {},
'strong': {},
'total': {},
'file': {},
'index': {}
}
total_strong = 0
total_weak = 0
total_file = 0
total_index = 0
for name, store in self.objects.iteritems():
size = store.cache.size
count = store.cache.count
profile = {
'count': count[0] + count[1],
'count_strong': count[0],
'count_weak': count[1],
'max': size[0],
'size_strong': size[0],
'size_weak': size[1],
}
total_strong += count[0]
total_weak += count[1]
total_file += len(store)
total_index += len(store.index)
image[name] = profile
image['strong'][name] = count[0]
image['weak'][name] = count[1]
image['total'][name] = count[0] + count[1]
image['file'][name] = len(store)
# if hasattr(store, 'index'):
image['index'][name] = len(store.index)
# else:
# image['index'][name] = 0
image['full'] = total_weak + total_strong
image['total_strong'] = total_strong
image['total_weak'] = total_weak
image['file'] = total_file
image['index'] = total_index
return image
def get_var_types(self):
"""
List all allowed variable type to be used in `create_variable`
Returns
-------
list of str
the list of variable types
"""
types = NetCDFPlus._type_conversion.keys()
types += ['obj.' + x for x in self.objects.keys()]
types += ['lazyobj.' + x for x in self.objects.keys()]
return sorted(types)
@staticmethod
def var_type_to_nc_type(var_type):
"""
Return the compatible netCDF variable type for var_type
Returns
-------
object
A object of netcdf compatible varible types
"""
if var_type.startswith('obj.') or var_type.startswith('lazyobj.'):
nc_type = np.int32
else:
nc_type = NetCDFPlus._type_conversion[var_type]
return nc_type
def create_type_delegate(self, var_type):
"""
Create a variable value delegator for var_type
The delegator will convert automatically between the given variable type
and the netcdf compatible one
Parameters
----------
var_type : str
the variable type
Returns
-------
NetCDFPlus.Value_Delegate
the delegator instance
"""
getter = None
setter = None
store = None
if var_type == 'int':
getter = lambda v: v.tolist()
setter = lambda v: np.array(v)
elif var_type == 'bool':
getter = lambda v: v.astype(np.bool).tolist()
setter = lambda v: np.array(v, dtype=np.int8)
elif var_type == 'index':
getter = lambda v: \
[None if int(w) < 0 else int(w) for w in v.tolist()] \
if hasattr(v, '__iter__') else None if int(v) < 0 else int(v)
setter = lambda v: \
[-1 if w is None else w for w in v] \
if hasattr(v, '__iter__') else -1 if v is None else v
elif var_type == 'float':
getter = lambda v: v.tolist()
setter = lambda v: np.array(v)
elif var_type.startswith('numpy.'):
pass
elif var_type == 'json':
setter = lambda v: self.simplifier.to_json_object(v)
getter = lambda v: self.simplifier.from_json(v)
elif var_type.startswith('obj.'):
store = getattr(self, var_type[4:])
base_type = store.content_class
iterable = lambda v: \
not v.base_cls is base_type if hasattr(v, 'base_cls') else hasattr(v, '__iter__')
getter = lambda v: \
[None if int(w) < 0 else store.load(int(w)) for w in v.tolist()] \
if iterable(v) else None if int(v) < 0 else store.load(int(v))
setter = lambda v: \
np.array([-1 if w is None else store.save(w) for w in v], dtype=np.int32) \
if iterable(v) else -1 if v is None else store.save(v)
elif var_type.startswith('lazyobj.'):
store = getattr(self, var_type[8:])
base_type = store.content_class
iterable = lambda v: \
not v.base_cls is base_type if hasattr(v, 'base_cls') else hasattr(v, '__iter__')
getter = lambda v: \
[None if int(w) < 0 else LoaderProxy(store, int(w)) for w in v.tolist()] \
if iterable(v) else None if int(v) < 0 else LoaderProxy(store, int(v))
setter = lambda v: \
np.array([-1 if w is None else store.save(w) for w in v], dtype=np.int32) \
if iterable(v) else -1 if v is None else store.save(v)
elif var_type == 'store':
setter = lambda v: v.prefix
getter = lambda v: self.objects[v]
return getter, setter, store
def create_variable_delegate(self, var_name):
"""
Create a delegate property that wraps the netcdf.Variable and takes care
of type conversions
"""
if var_name not in self.vars:
var = self.variables[var_name]
if not hasattr(var, 'var_type'):
return
getter, setter, store = self.create_type_delegate(var.var_type)
if True or self.support_simtk_unit:
if hasattr(var, 'unit_simtk'):
if var_name not in self.units:
self.update_simtk_unit(var_name)
unit = self.units[var_name]
def _get(my_getter):
import simtk.unit as u
if my_getter is None:
return lambda v: u.Quantity(v, unit)
else:
return lambda v: u.Quantity(my_getter(v), unit)
def _set(my_setter):
if my_setter is None:
return lambda v: v / unit
else:
return lambda v: my_setter(v / unit)
getter = _get(getter)
setter = _set(setter)
if True:
if hasattr(var, 'maskable'):
def _get2(my_getter):
return lambda v: \
[None if hasattr(w, 'mask') else my_getter(w) for w in v] \
if type(v) is not str and len(v.shape) > 0 else \
(None if hasattr(v, 'mask') else my_getter(v))
if getter is not None:
getter = _get2(getter)
else:
getter = _get2(lambda v: v)
self.vars[var_name] = NetCDFPlus.ValueDelegate(
var, getter, setter, store)
else:
raise ValueError("Variable '%s' is already taken!")
def create_variable(self,
var_name,
var_type,
dimensions,
description=None,
chunksizes=None,
simtk_unit=None,
maskable=False):
"""
Create a new variable in the netCDF storage.
This is just a helper function to structure the code better and add some convenience to
creating more complex variables
Parameters
==========
name : str
The name of the variable to be created
var_type : str
The string representing the type of the data stored in the variable.
Allowed are strings of native python types in which case the variables
will be treated as python or a string of the form 'numpy.type' which
will refer to the numpy data types. Numpy is preferred sinec the api
to netCDF uses numpy and thus it is faster. Possible input strings are
`int`, `float`, `long`, `str`, `numpy.float32`, `numpy.float64`,
`numpy.int8`, `numpy.int16`, `numpy.int32`, `numpy.int64`, `json`,
`obj.<store>`, `lazyobj.<store>`
dimensions : str or tuple of str
A tuple representing the dimensions used for the netcdf variable.
If not specified then the default dimension of the storage is used.
If the last dimension is `'...'` then it is assumed that the objects are of
variable length. In netCDF this is usually referred to as a VLType.
We will treat is just as another dimension, but it can only be the last dimension.
description : str
A string describing the variable in a readable form.
chunksizes : tuple of int
A tuple of ints per number of dimensions. This specifies in what
block sizes a variable is stored. Usually for object related stuff
we want to store everything of one object at once so this is often
(1, ..., ...)
simtk_units : str
A string representing the units used for this variable. Can be used with
all var_types although it makes sense only for numeric ones.
maskable : bool, default: False
If set to `True` the values in this variable can only partially exist and if
they have not yet been written they are filled with a fill_value which is
treated as a non-set variable. The created variable will interprete this values
as `None` when returned
"""
ncfile = self
if type(dimensions) is str:
dimensions = [dimensions]
dimensions = list(dimensions)
new_dimensions = dict()
for ix, dim in enumerate(dimensions):
if type(dim) is int:
dimensions[ix] = var_name + '_dim_' + str(ix)
new_dimensions[dimensions[ix]] = dim
if dimensions[-1] == '...':
# last dimension is simply [] so we allow arbitrary length and remove the last dimension
variable_length = True
dimensions = dimensions[:-1]
else:
variable_length = False
nc_type = NetCDFPlus.var_type_to_nc_type(var_type)
for dim_name, size in new_dimensions.items():
ncfile.create_dimension(dim_name, size)
dimensions = tuple(dimensions)
if variable_length:
vlen_t = ncfile.createVLType(nc_type, var_name + '_vlen')
ncvar = ncfile.createVariable(var_name,
vlen_t,
dimensions,
zlib=False,
chunksizes=chunksizes)
else:
ncvar = ncfile.createVariable(var_name,
nc_type,
dimensions,
zlib=False,
chunksizes=chunksizes)
setattr(ncvar, 'var_type', var_type)
if self.support_simtk_unit and simtk_unit is not None:
import simtk.unit as u
if isinstance(simtk_unit, u.Unit):
unit_instance = simtk_unit
symbol = unit_instance.get_symbol()
elif isinstance(simtk_unit, u.BaseUnit):
unit_instance = u.Unit({simtk_unit: 1.0})
symbol = unit_instance.get_symbol()
elif type(simtk_unit) is str and hasattr(u, simtk_unit):
unit_instance = getattr(u, simtk_unit)
symbol = unit_instance.get_symbol()
elif type(
simtk_unit) is str and simtk_unit in self.dimension_units:
unit_instance = self.dimension_units[simtk_unit]
symbol = unit_instance.get_symbol()
else:
raise NotImplementedError(
'Unit by abbreviated string representation is not yet supported'
)
json_unit = self.simplifier.unit_to_json(unit_instance)
# store the unit in the dict inside the Storage object
self.units[var_name] = unit_instance
# Define units for a float variable
setattr(ncvar, 'unit_simtk', json_unit)
setattr(ncvar, 'unit', symbol)
if maskable:
setattr(ncvar, 'maskable', 'True')
if description is not None:
if type(dimensions) is str:
dim_names = [dimensions]
else:
dim_names = map(lambda p: '#ix{0}:{1}'.format(*p),
enumerate(dimensions))
idx_desc = '[' + ']['.join(dim_names) + ']'
description = var_name + idx_desc + ' is ' + description.format(
idx=dim_names[0], ix=dim_names)
# Define long (human-readable) names for variables.
setattr(ncvar, "long_str", description)
return ncvar
def update_delegates(self):
"""
Updates the set of delegates in `self.vars`
Should be called after new variables have been created or loaded.
"""
for name in self.variables:
if name not in self.vars:
self.create_variable_delegate(name)
class NetCDFPlus(netCDF4.Dataset):
"""
Extension of the python netCDF wrapper for easier storage of python objects
"""
support_simtk_unit = True
@property
def _netcdfplus_version_(self):
import openpathsampling.netcdfplus.version as v
version = v.short_version
return version
_type_conversion = {
'float': np.float32,
'int': np.int32,
'long': np.int64,
'index': np.int32,
'length': np.int32,
'bool': np.int16,
'str': str,
'json': str,
'jsonobj': str,
'numpy.float32': np.float32,
'numpy.float64': np.float64,
'numpy.int8': np.int8,
'numpy.int16': np.int16,
'numpy.int32': np.int32,
'numpy.int64': np.int64,
'numpy.uint8': np.uint8,
'numpy.uint16': np.uint16,
'numpy.uint32': np.uint32,
'numpy.uint64': np.uint64,
'store': str,
'obj': np.int32,
'lazyobj': np.int32,
'uuid': str
}
class ValueDelegate(object):
"""
Value delegate for objects that implement __getitem__ and __setitem__
It will basically just wrap values that are used in a dict like
structure with getter and setter function to allow easier conversion
delegate[x] is equivalent to delegate.getter(delegate.variable[x])
Attributes
----------
variable : dict-like
the dict to be wrapped
getter : function
the function applied to results from running the __getitem__
on the variable
setter : function
the function applied to the value to be stored using __setitem__
on the variable
store : openpathsampling.netcdfplus.ObjectStore
a reference to an object store used for convenience in some cases
"""
def __init__(self, variable, getter=None, setter=None, store=None):
self.variable = variable
self.store = store
if setter is None:
setter = lambda v: v
self.setter = setter
if getter is None:
getter = lambda v: v
self.getter = getter
def __setitem__(self, key, value):
self.variable[key] = self.setter(value)
def __getitem__(self, key):
return self.getter(self.variable[key])
def __getattr__(self, item):
return getattr(self.variable, item)
def __str__(self):
return str(self.variable)
def __repr__(self):
return repr(self.variable)
def __len__(self):
return len(self.variable)
@property
def objects(self):
"""
Return a dictionary of all objects stored.
This is similar to the netcdf `.variables` for all stored variables.
This allows to write `storage.objects['samples'][idx]` like we
write `storage.variables['ensemble_json'][idx]`
"""
return self._stores
def find_store(self, obj):
"""
Return the default store used for an storable object
Parameters
----------
obj : :class:`openpathsampling.netcdfplus.StorableObject`
the storable object to be tested
Returns
-------
:class:`openpathsampling.netcdfplus.ObjectStore`
the store that is used by default to store the given storable obj
"""
if type(obj) is type or type(obj) is abc.ABCMeta:
if obj not in self._obj_store:
raise ValueError(
'Objects of class "%s" are not storable in this store.' %
obj.__name__)
return self._obj_store[obj]
else:
if obj.__class__ not in self._obj_store:
raise ValueError(
'Objects of class "%s" are not storable in this store.' %
obj.__class__.__name__)
return self._obj_store[obj.__class__]
def update_storable_classes(self):
self.simplifier.update_class_list()
def _create_storages(self):
"""
Function to be called automatically to register all object stores
This will usually only be called in subclassed storages.
"""
pass
def __init__(self, filename, mode=None, use_uuid=False, fallback=None):
"""
Create a storage for complex objects in a netCDF file
Parameters
----------
filename : string
filename of the netcdf file to be used or created
mode : str
the mode of file creation, one of 'w' (write), 'a' (append) or
'r' (read-only) None, which will append any existing files
(equal to append), is the default.
Notes
-----
A single file can be opened by multiple storages, but only one can be
used for writing
"""
if mode is None:
mode = 'a'
exists = os.path.isfile(filename)
if exists and mode == 'a':
logger.info(
"Open existing netCDF file '%s' for appending - "
"appending existing file", filename)
elif exists and mode == 'w':
logger.info(
"Create new netCDF file '%s' for writing - "
"deleting existing file", filename)
elif not exists and mode == 'a':
logger.info(
"Create new netCDF file '%s' for appending - "
"appending non-existing file", filename)
elif not exists and mode == 'w':
logger.info(
"Create new netCDF file '%s' for writing - "
"creating new file", filename)
elif not exists and mode == 'r':
logger.info(
"Open existing netCDF file '%s' for reading - "
"file does not exist", filename)
raise RuntimeError("File '%s' does not exist." % filename)
elif exists and mode == 'r':
logger.info(
"Open existing netCDF file '%s' for reading - "
"reading from existing file", filename)
self.filename = filename
self.fallback = fallback
# call netCDF4-python to create or open .nc file
super(NetCDFPlus, self).__init__(filename, mode)
self._setup_class()
if mode == 'w':
logger.info("Setup netCDF file and create variables")
self.setncattr('format', 'netcdf+')
self.setncattr('ncplus_version', self._netcdfplus_version_)
self.write_meta()
# add shared scalar dimension for everyone
self.create_dimension('scalar', 1)
self.create_dimension('pair', 2)
self.reference_by_uuid = use_uuid
if use_uuid:
self.setncattr('use_uuid', 'True')
self._create_simplifier()
# create the store that holds stores
store_stores = NamedObjectStore(ObjectStore)
store_stores.name = 'stores'
self.register_store('stores', store_stores)
self.stores.initialize()
self.stores.set_caching(True)
self.update_delegates()
# now create all storages in subclasses
self._create_storages()
# call the subclass specific initialization
self._initialize()
# this will create all variables in the storage for all new
# added stores this is often already call inside of _initialize.
# If not we just make sure
self.finalize_stores()
logger.info("Finished setting up netCDF file")
elif mode == 'a' or mode == 'r+' or mode == 'r':
logger.debug("Restore the dict of units from the storage")
self.check_version()
self.reference_by_uuid = hasattr(self, 'use_uuid')
self._create_simplifier()
# open the store that contains all stores
self.register_store('stores', NamedObjectStore(ObjectStore))
self.stores.set_caching(True)
self.create_variable_delegate('stores_json')
self.create_variable_delegate('stores_name')
if self.reference_by_uuid:
self.create_variable_delegate('stores_uuid')
self.stores.restore()
# Create a dict of simtk.Unit() instances for all netCDF.Variable()
for variable_name in self.variables:
variable = self.variables[variable_name]
if self.support_simtk_unit:
import simtk.unit as u
if hasattr(variable, 'unit_simtk'):
unit_dict = self.simplifier.from_json(
getattr(variable, 'unit_simtk'))
if unit_dict is not None:
unit = self.simplifier.unit_from_dict(unit_dict)
else:
unit = self.simplifier.unit_from_dict(u.Unit({}))
self.units[str(variable_name)] = unit
# register all stores that are listed in self.stores
for store in self.stores:
logger.debug("Register store %s in the storage" % store.name)
self.register_store(store.name, store)
store.register(self, store.name)
self.update_delegates()
self._restore_storages()
# call the subclass specific restore in case there is more stuff
# to prepare
self._restore()
self.sync()
def _create_simplifier(self):
if self.reference_by_uuid:
self.simplifier = UUIDObjectJSON(self)
else:
self.simplifier = StorableObjectJSON(self)
@property
def file_size(self):
return os.path.getsize(self.filename)
@property
def file_size_str(self):
current = float(self.file_size)
output_prefix = ''
for prefix in ["k", "M", "G"]:
if current >= 1024:
output_prefix = prefix
current /= 1024.0
return "{0:.2f}{1}B".format(current, output_prefix)
@staticmethod
def _cmp_version(v1, v2):
q1 = v1.split('-')[0].split('.')
q2 = v2.split('-')[0].split('.')
for v1, v2 in zip(q1, q2):
if int(v1) > int(v2):
return +1
elif int(v1) < int(v2):
return -1
return 0
def check_version(self):
try:
s1 = self.getncattr('ncplus_version')
except AttributeError:
logger.info(
'Using netcdfplus Pre 1.0 version. '
'No version detected using 0.0.0')
s1 = '0.0.0'
s2 = self._netcdfplus_version_
cp = self._cmp_version(s1, s2)
if cp != 0:
logger.info('Loading different netcdf version. '
'Installed version is '
'%s and loaded version is %s' % (s2, s1))
if cp > 0:
logger.info(
'Loaded version is newer consider upgrading your '
'conda package!')
else:
logger.info(
'Loaded version is older. Should be no problem other then '
'missing features and information')
def write_meta(self):
pass
def _setup_class(self):
"""
Sets the basic properties for the storage
"""
self._stores = OrderedDict()
self._objects = {}
self._obj_store = {}
self._storages_base_cls = {}
self.vars = dict()
self.units = dict()
def create_store(self, name, store, register_attr=True):
"""
Create a special variable type `obj.name` that can hold storable objects
Parameters
----------
name : str
the name of the store inside this storage
store : :class:`openpathsampling.netcdf.ObjectStore`
the store to be added to this storage
register_attr : bool
if `True` the store will be added to the storage as an
attribute with name `name`
"""
self.register_store(name, store, register_attr=register_attr)
store.name = name
self.stores.save(store)
def finalize_stores(self):
"""
Run initializations for all added stores.
This will make sure that all previously added stores are now useable.
If you add more stores you need to call this again. The reason this is
done at all is that stores might reference each other and so no
unique order of creation can be found. Thus you first create stores
with all their dependencies and then finalize all of them together.
"""
for store in self._stores.values():
if not store.is_created():
logger.info("Initializing store '%s'" % store.name)
store.initialize()
for store in self._stores.values():
if not store.is_created():
logger.info("Initializing store '%s'" % store.name)
store.initialize()
self.update_delegates()
self.simplifier.update_class_list()
def register_store(self, name, store, register_attr=True):
"""
Add a object store to the file
An object store is a special type of variable that allows to store
python objects
Parameters
----------
name : str
the name of the store under which the objects are accessible
like `store.{name}`
store : :class:`openpathsampling.storages.ObjectStore`
instance of the object store
register_attr : bool
if set to false the store will not be accesible as an attribute.
`True` is the default.
"""
store.register(self, name)
if register_attr:
if hasattr(self, name):
raise ValueError('Attribute name %s is already in use!' % name)
setattr(self, store.prefix, store)
self._stores[name] = store
if store.content_class is not None:
self._objects[store.content_class] = store
self._obj_store[store.content_class] = store
self._obj_store.update(
{cls: store for cls in store.content_class.descendants()})
def _initialize(self):
"""
Function run after a new file is created.
This is used to setup all variables in the storage
"""
pass
def _restore(self):
"""
Function run after an existing file is opened.
This is used in special storages to complete reading existing files.
"""
pass
def __repr__(self):
return "Storage @ '" + self.filename + "'"
def __getattr__(self, item):
try:
return self.__dict__[item]
except KeyError:
return self.__class__.__dict__[item]
def __setattr__(self, key, value):
self.__dict__[key] = value
def _init_storages(self):
"""
Run the initialization on all added classes
Notes
-----
Only runs when the storage is created.
"""
for storage in self._stores.values():
storage.initialize()
self.update_delegates()
def _restore_storages(self):
"""
Run the restore method on all added classes
Notes
-----
Only runs when an existing storage is opened.
"""
for storage in self._stores.values():
storage.restore()
storage._created = True
def list_stores(self):
"""
Return a list of registered stores
Returns
-------
list of str
list of stores that can be accessed using `storage.[store]`
"""
return [store.prefix for store in self._stores.values()]
def list_storable_objects(self):
"""
Return a list of storable object base classes
Returns
-------
list of type
list of base classes that can be stored using `storage.save(obj)`
"""
return [
store.content_class
for store in self.objects.values()
if store.content_class is not None]
def save(self, obj, idx=None):
"""
Save a storable object into the correct Storage in the netCDF file
Parameters
----------
obj : :class:`StorableObject`
the object to store
idx : str
the name to be stored by
Returns
-------
str
the class name of the BaseClass of the stored object, which is
needed when loading the object to identify the correct storage
"""
if type(obj) is list:
# a list of objects will be stored one by one
return [self.save(part, idx) for part in obj]
elif type(obj) is tuple:
# a tuple will store all parts
return [self.save(part, idx) for part in obj]
elif obj.__class__ in self._obj_store:
# to store we just check if the base_class is present in the
# storages also we assume that if a class has no base_cls
store = self.find_store(obj)
store_idx = self.stores.index[store]
return store, store_idx, store.save(obj, idx)
# Could not save this object.
raise RuntimeWarning("Objects of type '%s' cannot be stored!" %
obj.__class__.__name__)
def load(self, uuid):
"""
Load an object from the storage
Parameters
----------
uuid : uuid.UUID
the uuid to be loaded
Returns
-------
:class:`openpathsampling.netcdfplus.StorableObject`
the object loaded from the storage
Notes
-----
this only works in storages with uuids otherwise load directly from the
substores
"""
if self.reference_by_uuid:
for store in self.objects.values():
if uuid in store.index:
return store[uuid]
raise KeyError("UUID %s not found in storage" % uuid)
else:
raise RuntimeError(
"Can only load directly from stores that support UUIDs.")
def idx(self, obj):
"""
Return the index used to store the given object in this storage
Parameters
----------
obj : object
The stored object from which the index is to be returned
"""
if hasattr(obj, 'base_cls'):
store = self._objects[obj.base_cls]
return store.idx(obj)
def repr_json(self, obj):
if hasattr(obj, 'base_cls'):
store = self._objects[obj.base_cls]
if store.json:
return store.variables['json'][store.idx(obj)]
return None
def create_dimension(self, dim_name, size=None):
"""
Initialize a new dimension in the storage.
Wraps the netCDF createDimension
Parameters
----------
dim_name : str
the name for the new dimension
size : int
the number of elements in this dimension. None (default) means
an infinite dimension that extends when more objects are stored
"""
if dim_name not in self.dimensions:
self.createDimension(dim_name, size)
def cache_image(self):
"""
Return an dict containing information about all caches
Returns
-------
dict
a nested dict containing information about the number and types of
cached objects
"""
image = {
'weak': {},
'strong': {},
'total': {},
'file': {},
'index': {}
}
total_strong = 0
total_weak = 0
total_file = 0
total_index = 0
for name, store in self.objects.iteritems():
size = store.cache.size
count = store.cache.count
profile = {
'count': count[0] + count[1],
'count_strong': count[0],
'count_weak': count[1],
'max': size[0],
'size_strong': size[0],
'size_weak': size[1],
}
total_strong += count[0]
total_weak += count[1]
total_file += len(store)
total_index += len(store.index)
image[name] = profile
image['strong'][name] = count[0]
image['weak'][name] = count[1]
image['total'][name] = count[0] + count[1]
image['file'][name] = len(store)
# if hasattr(store, 'index'):
image['index'][name] = len(store.index)
# else:
# image['index'][name] = 0
image['full'] = total_weak + total_strong
image['total_strong'] = total_strong
image['total_weak'] = total_weak
image['file'] = total_file
image['index'] = total_index
return image
def get_var_types(self):
"""
List all allowed variable type to be used in `create_variable`
Returns
-------
list of str
the list of variable types
"""
types = NetCDFPlus._type_conversion.keys()
types += ['obj.' + x for x in self.objects.keys()]
types += ['lazyobj.' + x for x in self.objects.keys()]
return sorted(types)
def var_type_to_nc_type(self, var_type):
"""
Return the compatible netCDF variable type for var_type
Parameters
----------
var_type :
Returns
-------
object
A object of netcdf compatible varible types
"""
if var_type.startswith('obj.') or var_type.startswith('lazyobj.'):
if self.reference_by_uuid:
nc_type = str
else:
nc_type = np.int32
else:
nc_type = NetCDFPlus._type_conversion[var_type]
return nc_type
def create_type_delegate(self, var_type):
"""
Create a variable value delegator for var_type
The delegator will convert automatically between the given variable type
and the netcdf compatible one
Parameters
----------
var_type : str
the variable type
Returns
-------
NetCDFPlus.Value_Delegate
the delegator instance
"""
getter = None
setter = None
store = None
to_uuid_chunks = lambda x: [x[i:i + 36] for i in range(0, len(x), 36)]
if var_type.startswith('obj.') or var_type.startswith('lazyobj.'):
store_name = str(var_type.split('.')[1])
store = self._stores[store_name]
base_type = store.content_class
get_is_iterable = lambda v: \
v.base_cls is not base_type if hasattr(v, 'base_cls') else \
hasattr(v, '__iter__')
set_is_iterable = lambda v: \
v.base_cls is not base_type if hasattr(v, 'base_cls') else \
hasattr(v, '__iter__')
if var_type == 'int':
getter = lambda v: v.tolist()
setter = lambda v: np.array(v)
elif var_type == 'bool':
getter = lambda v: v.astype(np.bool).tolist()
setter = lambda v: np.array(v, dtype=np.int8)
elif var_type == 'index':
getter = lambda v: \
[None if int(w) < 0 else int(w) for w in v.tolist()] \
if hasattr(v, '__iter__') else None if int(v) < 0 else int(v)
setter = lambda v: \
[-1 if w is None else w for w in v] \
if hasattr(v, '__iter__') else -1 if v is None else v
elif var_type == 'float':
getter = lambda v: v.tolist()
setter = lambda v: np.array(v)
elif var_type.startswith('numpy.'):
pass
elif var_type == 'jsonobj':
setter = lambda v: self.simplifier.to_json_object(v)
getter = lambda v: self.simplifier.from_json(v)
elif var_type == 'json':
setter = lambda v: self.simplifier.to_json(v)
getter = lambda v: self.simplifier.from_json(v)
elif var_type.startswith('obj.'):
if not self.reference_by_uuid:
getter = lambda v: [
None if int(w) < 0 else store.load(int(w))
for w in v.tolist()
] if get_is_iterable(v) else \
None if int(v) < 0 else store.load(int(v))
setter = lambda v: \
np.array(
[-1 if w is None else store.save(w) for w in v],
dtype=np.int32
) if set_is_iterable(v) else \
-1 if v is None else store.save(v)
else:
getter = lambda v: [
None if w[0] == '-' else store.load(UUID(w))
for w in to_uuid_chunks(v)
] if get_is_iterable(v) else \
None if v[0] == '-' else store.load(UUID(v))
setter = lambda v: \
''.join(['-' * 36 if w is None else str(store.save(w))
for w in list.__iter__(v)]) \
if set_is_iterable(v) else \
'-' * 36 if v is None else str(store.save(v))
elif var_type == 'obj':
# arbitrary object
set_iterable_simple = lambda v: \
False if hasattr(v, 'base_cls') else hasattr(v, '__iter__')
getter = lambda v: [
None if int(w[1]) < 0 else
self.stores[int(w[0])].load(int(w[1]))
for w in v.tolist()] \
if len(v.shape) > 1 else None if int(v[1]) < 0 else \
self.stores[int(v[0])].load(int(v[1]))
setter = lambda v: \
np.array(
[(-1, -1) if w is None else self.save(w)[1:] for w in v],
dtype=np.int32) if set_iterable_simple(v) else \
(-1, -1) if v is None else self.save(v)[1:]
elif var_type.startswith('lazyobj.'):
if not self.reference_by_uuid:
getter = lambda v: [
None if int(w) < 0 else LoaderProxy(store, int(w))
for w in v.tolist()
] if get_is_iterable(v) else \
None if int(v) < 0 else LoaderProxy(store, int(v))
setter = lambda v: \
np.array(
[-1 if w is None else store.save(w) for w in v],
dtype=np.int32) if set_is_iterable(v) else \
-1 if v is None else store.save(v)
else:
getter = lambda v: [
None if w[0] == '-' else LoaderProxy(store, UUID(w))
for w in to_uuid_chunks(v)
] if get_is_iterable(v) else \
None if v[0] == '-' else LoaderProxy(store, UUID(v))
setter = lambda v: \
''.join([
'-' * 36 if w is None else str(store.save(w))
for w in list.__iter__(v)
]) if set_is_iterable(v) else \
'-' * 36 if v is None else str(store.save(v))
elif var_type == 'uuid':
getter = lambda v: \
[None if w[0] == '-' else UUID(w) for w in v] \
if type(v) is not unicode else None if v[0] == '-' else UUID(v)
setter = lambda v: \
''.join([
'-' * 36 if w is None else str(w)
for w in list.__iter__(v)
]) if hasattr(v, '__iter__') else \
'-' * 36 if v is None else str(v)
elif var_type == 'lazyobj':
# arbitrary object
set_iterable_simple = lambda v: \
False if hasattr(v, 'base_cls') else hasattr(v, '__iter__')
getter = lambda v: [
None if int(w[1]) < 0 else
LoaderProxy(self.stores[int(w[0])], int(w[1]))
for w in v.tolist()
] if len(v.shape) > 1 else \
None if int(v[1]) < 0 else \
LoaderProxy(self.stores[int(v[0])], int(v[1]))
setter = lambda v: \
np.array(
[(-1, -1) if w is None else self.save(w)[1:] for w in v],
dtype=np.int32
) if set_iterable_simple(v) else \
(-1, -1) if v is None else self.save(v)[1:]
elif var_type == 'store':
setter = lambda v: v.prefix
getter = lambda v: self.objects[v]
return getter, setter, store
to_uuid_chunks = staticmethod(
lambda x: [x[i:i + 36] for i in range(0, len(x), 36)])
def create_variable_delegate(self, var_name):
"""
Create a delegate property that wraps the netcdf.Variable and takes care
of type conversions
Parameters
----------
var_name : str
the name of the variable for which a delegate should be created
"""
if var_name not in self.vars:
var = self.variables[var_name]
if not hasattr(var, 'var_type'):
return
getter, setter, store = self.create_type_delegate(var.var_type)
if self.reference_by_uuid:
to_uuid_chunks = NetCDFPlus.to_uuid_chunks
if hasattr(var, 'var_vlen'):
if var.var_type.startswith('obj.'):
getter = lambda v: [
None if w[0] == '-' else store.load(UUID(w))
for w in to_uuid_chunks(v)
]
elif var.var_type.startswith('lazyobj.'):
getter = lambda v: [
None if w[0] == '-' else LoaderProxy(store, UUID(w))
for w in to_uuid_chunks(v)
]
else:
if var.var_type.startswith('obj.'):
getter = lambda v: \
None if v[0] == '-' else store.load(UUID(v))
elif var.var_type.startswith('lazyobj.'):
getter = lambda v: None if v[0] == '-' \
else LoaderProxy(store, UUID(v))
if True or self.support_simtk_unit:
if hasattr(var, 'unit_simtk'):
if var_name not in self.units:
self.update_simtk_unit(var_name)
unit = self.units[var_name]
def _get(my_getter):
import simtk.unit as u
if my_getter is None:
return lambda v: u.Quantity(v, unit)
else:
return lambda v: u.Quantity(my_getter(v), unit)
def _set(my_setter):
if my_setter is None:
return lambda v: v / unit
else:
return lambda v: my_setter(v / unit)
getter = _get(getter)
setter = _set(setter)
if True:
if hasattr(var, 'maskable'):
def _get2(my_getter):
return lambda v: [
None if hasattr(w, 'mask') else
my_getter(w) for w in v
] if type(v) is not str and len(v.shape) > 0 else \
None if hasattr(v, 'mask') else my_getter(v)
if getter is not None:
getter = _get2(getter)
else:
getter = _get2(lambda v: v)
self.vars[var_name] = \
NetCDFPlus.ValueDelegate(var, getter, setter, store)
else:
raise ValueError("Variable '%s' is already taken!" % var_name)
def create_variable(self, var_name,
var_type,
dimensions,
description=None,
chunksizes=None,
simtk_unit=None,
maskable=False):
"""
Create a new variable in the netCDF storage.
This is just a helper function to structure the code better and add
some convenience to creating more complex variables
Parameters
==========
var_name : str
The name of the variable to be created
var_type : str
The string representing the type of the data stored in the
variable. Allowed are strings of native python types in which
case the variables will be treated as python or a string of the
form 'numpy.type' which will refer to the numpy data types.
Numpy is preferred sinec the api to netCDF uses numpy and thus
it is faster. Possible input strings are
`int`, `float`, `long`, `str`, `numpy.float32`, `numpy.float64`,
`numpy.int8`, `numpy.int16`, `numpy.int32`, `numpy.int64`, `json`,
`obj.<store>`, `lazyobj.<store>`
dimensions : str or tuple of str
A tuple representing the dimensions used for the netcdf variable.
If not specified then the default dimension of the storage is used.
If the last dimension is `'...'` then it is assumed that the
objects are of variable length. In netCDF this is usually
referred to as a VLType. We will treat is just as another
dimension, but it can only be the last dimension.
description : str
A string describing the variable in a readable form.
chunksizes : tuple of int
A tuple of ints per number of dimensions. This specifies in what
block sizes a variable is stored. Usually for object related stuff
we want to store everything of one object at once so this is often
(1, ..., ...)
simtk_unit : str
A string representing the units used for this variable. Can be
used with all var_types although it makes sense only for numeric
ones.
maskable : bool, default: False
If set to `True` the values in this variable can only partially
exist and if they have not yet been written they are filled with
a fill_value which is treated as a non-set variable. The created
variable will interpret this values as `None` when returned
"""
ncfile = self
if type(dimensions) is str:
dimensions = [dimensions]
dimensions = list(dimensions)
new_dimensions = dict()
for ix, dim in enumerate(dimensions):
if type(dim) is int:
dimensions[ix] = var_name + '_dim_' + str(ix)
new_dimensions[dimensions[ix]] = dim
if dimensions[-1] == '...':
# last dimension is simply [] so we allow arbitrary length
# and remove the last dimension
variable_length = True
dimensions = dimensions[:-1]
else:
variable_length = False
if var_type == 'obj' or var_type == 'lazyobj':
dimensions.append('pair')
if chunksizes is not None:
chunksizes = tuple(list(chunksizes) + [2])
nc_type = self.var_type_to_nc_type(var_type)
for dim_name, size in new_dimensions.items():
ncfile.create_dimension(dim_name, size)
dimensions = tuple(dimensions)
# if chunk sizes are strings then replace it by
# the actual size of the dimension
if chunksizes is not None:
chunksizes = list(chunksizes)
for ix, dim in enumerate(chunksizes):
if dim == -1:
chunksizes[ix] = len(ncfile.dimensions[dimensions[ix]])
if type(dim) is str:
chunksizes[ix] = len(ncfile.dimensions[dim])
chunksizes = tuple(chunksizes)
if variable_length:
vlen_t = ncfile.createVLType(nc_type, var_name + '_vlen')
ncvar = ncfile.createVariable(
var_name, vlen_t, dimensions, chunksizes=chunksizes
)
setattr(ncvar, 'var_vlen', 'True')
else:
ncvar = ncfile.createVariable(
var_name, nc_type, dimensions, chunksizes=chunksizes,
)
setattr(ncvar, 'var_type', var_type)
if self.support_simtk_unit and simtk_unit is not None:
import simtk.unit as u
if isinstance(simtk_unit, u.Unit):
unit_instance = simtk_unit
symbol = unit_instance.get_symbol()
elif isinstance(simtk_unit, u.BaseUnit):
unit_instance = u.Unit({simtk_unit: 1.0})
symbol = unit_instance.get_symbol()
elif type(simtk_unit) is str and hasattr(u, simtk_unit):
unit_instance = getattr(u, simtk_unit)
symbol = unit_instance.get_symbol()
else:
raise NotImplementedError(
'Unit by abbreviated string representation '
'is not yet supported')
json_unit = self.simplifier.unit_to_json(unit_instance)
# store the unit in the dict inside the Storage object
self.units[var_name] = unit_instance
# Define units for a float variable
setattr(ncvar, 'unit_simtk', json_unit)
setattr(ncvar, 'unit', symbol)
if maskable:
setattr(ncvar, 'maskable', 'True')
if description is not None:
if type(dimensions) is str:
dim_names = [dimensions]
else:
dim_names = map(
lambda p: '#ix{0}:{1}'.format(*p),
enumerate(dimensions))
idx_desc = '[' + ']['.join(dim_names) + ']'
description = var_name + idx_desc + ' is ' + \
description.format(idx=dim_names[0], ix=dim_names)
# Define long (human-readable) names for variables.
setattr(ncvar, "long_str", description)
self.update_delegates()
return ncvar
def update_delegates(self):
"""
Updates the set of delegates in `self.vars`
Should be called after new variables have been created or loaded.
"""
for name in self.variables:
if name not in self.vars:
self.create_variable_delegate(name)
@staticmethod
def get_value_parameters(value):
"""
Compute netcdfplus compatible parameters to store a value
Parameters
----------
value
Returns
-------
dict
A dictionary containing the approriate input parameters for
`var_type`, `dimensions`, `simtk_unit`
Notes
-----
This is a utility function to create a CV using a template
"""
dimensions = None
storable = True
simtk_unit = None
test_value = value
test_type = value
if NetCDFPlus.support_simtk_unit:
import simtk.unit as u
if type(test_type) is u.Quantity:
# could be a Quantity([..])
simtk_unit = test_type.unit
test_type = test_type._value
else:
u = None
if type(test_type) is np.ndarray:
dimensions = test_type.shape
else:
if hasattr(test_value, '__len__'):
dimensions = len(test_value)
test_type = test_value[0]
if NetCDFPlus.support_simtk_unit and type(test_type) \
is u.Quantity:
for val in test_value:
if isinstance(val._value, type(test_value._value)):
# all values must be of same type
storable = False
else:
for val in test_value:
if type(val) is not type(test_value):
# all values must be of same type
storable = False
if NetCDFPlus.support_simtk_unit and type(test_type) is u.Quantity:
# could also be [Quantity, ...]
simtk_unit = test_type.unit
test_type = test_type._value
if storable:
var_type = NetCDFPlus.identify_var_type(test_type)
return {
'var_type': var_type,
'dimensions': dimensions,
'simtk_unit': simtk_unit
}
return {
}
@staticmethod
def identify_var_type(instance):
"""
Identify common python and numpy types
Parameters
----------
instance
python variable instance to be tested for it numeric type
Returns
-------
str
a string representation of the variable type
"""
ty = type(instance)
known_types = [float, int, bool, str]
if ty in known_types:
return ty.__name__
elif hasattr(instance, 'dtype'):
return 'numpy.' + instance.dtype.type.__name__
else:
return 'None'
class NetCDFPlus(netCDF4.Dataset):
"""
Extension of the python netCDF wrapper for easier storage of python objects
"""
support_simtk_unit = True
@property
def _netcdfplus_version_(self):
import openpathsampling.netcdfplus.version as v
version = v.short_version
return version
_type_conversion = {
'float': np.float32,
'int': np.int32,
'long': np.int64,
'index': np.int32,
'length': np.int32,
'bool': np.int16,
'str': str,
'json': str,
'jsonobj': str,
'numpy.float32': np.float32,
'numpy.float64': np.float64,
'numpy.int8': np.int8,
'numpy.int16': np.int16,
'numpy.int32': np.int32,
'numpy.int64': np.int64,
'numpy.uint8': np.uint8,
'numpy.uint16': np.uint16,
'numpy.uint32': np.uint32,
'numpy.uint64': np.uint64,
'store': str,
'obj': np.int32,
'lazyobj': np.int32,
'uuid': str
}
class ValueDelegate(object):
"""
Value delegate for objects that implement __getitem__ and __setitem__
It will basically just wrap values that are used in a dict like
structure with getter and setter function to allow easier conversion
delegate[x] is equivalent to delegate.getter(delegate.variable[x])
Attributes
----------
variable : dict-like
the dict to be wrapped
getter : function
the function applied to results from running the __getitem__
on the variable
setter : function
the function applied to the value to be stored using __setitem__
on the variable
store : openpathsampling.netcdfplus.ObjectStore
a reference to an object store used for convenience in some cases
"""
def __init__(self, variable, getter=None, setter=None, store=None):
self.variable = variable
self.store = store
if setter is None:
setter = lambda v: v
self.setter = setter
if getter is None:
getter = lambda v: v
self.getter = getter
def __setitem__(self, key, value):
self.variable[key] = self.setter(value)
def __getitem__(self, key):
return self.getter(self.variable[key])
def __getattr__(self, item):
return getattr(self.variable, item)
def __str__(self):
return str(self.variable)
def __repr__(self):
return repr(self.variable)
def __len__(self):
return len(self.variable)
@property
def objects(self):
"""
Return a dictionary of all objects stored.
This is similar to the netcdf `.variables` for all stored variables.
This allows to write `storage.objects['samples'][idx]` like we
write `storage.variables['ensemble_json'][idx]`
"""
return self._stores
def find_store(self, obj):
"""
Return the default store used for an storable object
Parameters
----------
obj : :class:`openpathsampling.netcdfplus.StorableObject`
the storable object to be tested
Returns
-------
:class:`openpathsampling.netcdfplus.ObjectStore`
the store that is used by default to store the given storable obj
"""
if type(obj) is type or type(obj) is abc.ABCMeta:
if obj not in self._obj_store:
raise ValueError(
'Objects of class "%s" are not storable in this store.' %
obj.__name__)
return self._obj_store[obj]
else:
if obj.__class__ not in self._obj_store:
raise ValueError(
'Objects of class "%s" are not storable in this store.' %
obj.__class__.__name__)
return self._obj_store[obj.__class__]
def update_storable_classes(self):
self.simplifier.update_class_list()
def _create_storages(self):
"""
Function to be called automatically to register all object stores
This will usually only be called in subclassed storages.
"""
pass
def __init__(self, filename, mode=None, use_uuid=True, fallback=None):
"""
Create a storage for complex objects in a netCDF file
Parameters
----------
filename : string
filename of the netcdf file to be used or created
mode : str
the mode of file creation, one of 'w' (write), 'a' (append) or
'r' (read-only) None, which will append any existing files
(equal to append), is the default.
use_uuid : bool
if `True` the store will use UUIDs to identify all objects.
This is slower but allows arbitrary splitting and merging of objects
fallback : :class:`openpathsampling.Storage`
the _fallback_ storage to be loaded from if an object is not present
in this storage. By default you will not try to resave objects
that could be found in the fallback. Note that the fall back does
only work if `use_uuid` is enabled
Notes
-----
A single file can be opened by multiple storages, but only one can be
used for writing
"""
if mode is None:
mode = 'a'
exists = os.path.isfile(filename)
if exists and mode == 'a':
logger.info(
"Open existing netCDF file '%s' for appending - "
"appending existing file", filename)
elif exists and mode == 'w':
logger.info(
"Create new netCDF file '%s' for writing - "
"deleting existing file", filename)
elif not exists and mode == 'a':
logger.info(
"Create new netCDF file '%s' for appending - "
"appending non-existing file", filename)
elif not exists and mode == 'w':
logger.info(
"Create new netCDF file '%s' for writing - "
"creating new file", filename)
elif not exists and mode == 'r':
logger.info(
"Open existing netCDF file '%s' for reading - "
"file does not exist", filename)
raise RuntimeError("File '%s' does not exist." % filename)
elif exists and mode == 'r':
logger.info(
"Open existing netCDF file '%s' for reading - "
"reading from existing file", filename)
self.filename = filename
self.fallback = fallback
# this can be set to false to re-store objects present in the fallback
self.exclude_from_fallback = True
# this can be set to false to re-store proxies from other stores
self.exclude_proxy_from_other = False
# call netCDF4-python to create or open .nc file
super(NetCDFPlus, self).__init__(filename, mode)
self._setup_class()
if mode == 'w':
logger.info("Setup netCDF file and create variables")
self.setncattr('format', 'netcdf+')
self.setncattr('ncplus_version', self._netcdfplus_version_)
self.write_meta()
# add shared scalar dimension for everyone
self.create_dimension('scalar', 1)
self.create_dimension('pair', 2)
self.reference_by_uuid = use_uuid
if use_uuid:
self.setncattr('use_uuid', 'True')
self._create_simplifier()
# create the store that holds stores
store_stores = NamedObjectStore(ObjectStore)
store_stores.name = 'stores'
self.register_store('stores', store_stores)
self.stores.initialize()
self.stores.set_caching(True)
self.update_delegates()
# now create all storages in subclasses
self._create_storages()
# call the subclass specific initialization
self._initialize()
# this will create all variables in the storage for all new
# added stores this is often already call inside of _initialize.
# If not we just make sure
self.finalize_stores()
logger.info("Finished setting up netCDF file")
elif mode == 'a' or mode == 'r+' or mode == 'r':
logger.debug("Restore the dict of units from the storage")
self.check_version()
self.reference_by_uuid = hasattr(self, 'use_uuid')
self._create_simplifier()
# open the store that contains all stores
self.register_store('stores', NamedObjectStore(ObjectStore))
self.stores.set_caching(True)
self.create_variable_delegate('stores_json')
self.create_variable_delegate('stores_name')
if self.reference_by_uuid:
self.create_variable_delegate('stores_uuid')
self.stores.restore()
# Create a dict of simtk.Unit() instances for all netCDF.Variable()
for variable_name in self.variables:
variable = self.variables[variable_name]
if self.support_simtk_unit:
import simtk.unit as u
if hasattr(variable, 'unit_simtk'):
unit_dict = self.simplifier.from_json(
getattr(variable, 'unit_simtk'))
if unit_dict is not None:
unit = self.simplifier.unit_from_dict(unit_dict)
else:
unit = self.simplifier.unit_from_dict(u.Unit({}))
self.units[str(variable_name)] = unit
# register all stores that are listed in self.stores
for store in self.stores:
logger.debug("Register store %s in the storage" % store.name)
self.register_store(store.name, store)
store.register(self, store.name)
self.update_delegates()
self._restore_storages()
# call the subclass specific restore in case there is more stuff
# to prepare
self._restore()
self.sync()
def _create_simplifier(self):
if self.reference_by_uuid:
self.simplifier = UUIDObjectJSON(self)
else:
self.simplifier = StorableObjectJSON(self)
@property
def file_size(self):
return os.path.getsize(self.filename)
@property
def file_size_str(self):
current = float(self.file_size)
output_prefix = ''
for prefix in ["k", "M", "G"]:
if current >= 1024:
output_prefix = prefix
current /= 1024.0
return "{0:.2f}{1}B".format(current, output_prefix)
@staticmethod
def _cmp_version(v1, v2):
q1 = v1.split('-')[0].split('.')
q2 = v2.split('-')[0].split('.')
for v1, v2 in zip(q1, q2):
if int(v1) > int(v2):
return +1
elif int(v1) < int(v2):
return -1
return 0
def check_version(self):
try:
s1 = self.getncattr('ncplus_version')
except AttributeError:
logger.info(
'Using netcdfplus Pre 1.0 version. '
'No version detected using 0.0.0')
s1 = '0.0.0'
s2 = self._netcdfplus_version_
cp = self._cmp_version(s1, s2)
if cp != 0:
logger.info('Loading different netcdf version. '
'Installed version is '
'%s and loaded version is %s' % (s2, s1))
if cp > 0:
logger.info(
'Loaded version is newer consider upgrading your '
'conda package!')
else:
logger.info(
'Loaded version is older. Should be no problem other then '
'missing features and information')
def write_meta(self):
pass
def _setup_class(self):
"""
Sets the basic properties for the storage
"""
self._stores = OrderedDict()
self._objects = {}
self._obj_store = {}
self._storages_base_cls = {}
self.vars = dict()
self.units = dict()
def create_store(self, name, store, register_attr=True):
"""
Create a special variable type `obj.name` that can hold storable objects
Parameters
----------
name : str
the name of the store inside this storage
store : :class:`openpathsampling.netcdf.ObjectStore`
the store to be added to this storage
register_attr : bool
if `True` the store will be added to the storage as an
attribute with name `name`
"""
self.register_store(name, store, register_attr=register_attr)
store.name = name
self.stores.save(store)
def finalize_stores(self):
"""
Run initializations for all added stores.
This will make sure that all previously added stores are now useable.
If you add more stores you need to call this again. The reason this is
done at all is that stores might reference each other and so no
unique order of creation can be found. Thus you first create stores
with all their dependencies and then finalize all of them together.
"""
for store in self._stores.values():
if not store.is_created():
logger.info("Initializing store '%s'" % store.name)
store.initialize()
for store in self._stores.values():
if not store.is_created():
logger.info("Initializing store '%s'" % store.name)
store.initialize()
self.update_delegates()
self.simplifier.update_class_list()
def register_store(self, name, store, register_attr=True):
"""
Add a object store to the file
An object store is a special type of variable that allows to store
python objects
Parameters
----------
name : str
the name of the store under which the objects are accessible
like `store.{name}`
store : :class:`openpathsampling.storages.ObjectStore`
instance of the object store
register_attr : bool
if set to false the store will not be accesible as an attribute.
`True` is the default.
"""
store.register(self, name)
if register_attr:
if hasattr(self, name):
raise ValueError('Attribute name %s is already in use!' % name)
setattr(self, store.prefix, store)
self._stores[name] = store
if store.content_class is not None:
self._objects[store.content_class] = store
self._obj_store[store.content_class] = store
self._obj_store.update(
{cls: store for cls in store.content_class.descendants()})
def _initialize(self):
"""
Function run after a new file is created.
This is used to setup all variables in the storage
"""
pass
def _restore(self):
"""
Function run after an existing file is opened.
This is used in special storages to complete reading existing files.
"""
pass
def __repr__(self):
return "Storage @ '" + self.filename + "'"
def __getattr__(self, item):
try:
return self.__dict__[item]
except KeyError:
return self.__class__.__dict__[item]
def __setattr__(self, key, value):
self.__dict__[key] = value
def _init_storages(self):
"""
Run the initialization on all added classes
Notes
-----
Only runs when the storage is created.
"""
for storage in self._stores.values():
storage.initialize()
self.update_delegates()
def _restore_storages(self):
"""
Run the restore method on all added classes
Notes
-----
Only runs when an existing storage is opened.
"""
for storage in self._stores.values():
storage.restore()
storage._created = True
def list_stores(self):
"""
Return a list of registered stores
Returns
-------
list of str
list of stores that can be accessed using `storage.[store]`
"""
return [store.prefix for store in self._stores.values()]
def list_storable_objects(self):
"""
Return a list of storable object base classes
Returns
-------
list of type
list of base classes that can be stored using `storage.save(obj)`
"""
return [
store.content_class
for store in self.objects.values()
if store.content_class is not None]
def save(self, obj, idx=None):
"""
Save a storable object into the correct Storage in the netCDF file
Parameters
----------
obj : :class:`StorableObject`
the object to store
idx : str
the name to be stored by
Returns
-------
str
the class name of the BaseClass of the stored object, which is
needed when loading the object to identify the correct storage
"""
if type(obj) is list:
# a list of objects will be stored one by one
return [self.save(part, idx) for part in obj]
elif type(obj) is tuple:
# a tuple will store all parts
return [self.save(part, idx) for part in obj]
elif obj.__class__ in self._obj_store:
# to store we just check if the base_class is present in the
# storages also we assume that if a class has no base_cls
store = self.find_store(obj)
store_idx = self.stores.index[store]
return store, store_idx, store.save(obj, idx)
# Could not save this object.
raise RuntimeWarning("Objects of type '%s' cannot be stored!" %
obj.__class__.__name__)
def __contains__(self, item):
if type(item) is list:
# a list of objects will be stored one by one
return [part in self for part in item]
elif type(item) is tuple:
# a tuple will store all parts
return tuple([part in self for part in item])
elif item.__class__ in self._obj_store:
# to store we just check if the base_class is present in the
# storages also we assume that if a class has no base_cls
store = self.find_store(item)
return item in store
return False
def load(self, uuid):
"""
Load an object from the storage
Parameters
----------
uuid : uuid.UUID
the uuid to be loaded
Returns
-------
:class:`openpathsampling.netcdfplus.StorableObject`
the object loaded from the storage
Notes
-----
this only works in storages with uuids otherwise load directly from the
substores
"""
if self.reference_by_uuid:
for store in self.objects.values():
if uuid in store.index:
return store[uuid]
raise KeyError("UUID %s not found in storage" % uuid)
else:
raise RuntimeError(
"Can only load directly from stores that support UUIDs.")
def idx(self, obj):
"""
Return the index used to store the given object in this storage
Parameters
----------
obj : object
The stored object from which the index is to be returned
"""
if hasattr(obj, 'base_cls'):
store = self._objects[obj.base_cls]
return store.idx(obj)
def repr_json(self, obj):
if hasattr(obj, 'base_cls'):
store = self._objects[obj.base_cls]
if store.json:
return store.variables['json'][store.idx(obj)]
return None
def create_dimension(self, dim_name, size=None):
"""
Initialize a new dimension in the storage.
Wraps the netCDF createDimension
Parameters
----------
dim_name : str
the name for the new dimension
size : int
the number of elements in this dimension. None (default) means
an infinite dimension that extends when more objects are stored
"""
if dim_name not in self.dimensions:
self.createDimension(dim_name, size)
def cache_image(self):
"""
Return an dict containing information about all caches
Returns
-------
dict
a nested dict containing information about the number and types of
cached objects
"""
image = {
'weak': {},
'strong': {},
'total': {},
'file': {},
'index': {}
}
total_strong = 0
total_weak = 0
total_file = 0
total_index = 0
for name, store in self.objects.iteritems():
size = store.cache.size
count = store.cache.count
profile = {
'count': count[0] + count[1],
'count_strong': count[0],
'count_weak': count[1],
'max': size[0],
'size_strong': size[0],
'size_weak': size[1],
}
total_strong += count[0]
total_weak += count[1]
total_file += len(store)
total_index += len(store.index)
image[name] = profile
image['strong'][name] = count[0]
image['weak'][name] = count[1]
image['total'][name] = count[0] + count[1]
image['file'][name] = len(store)
# if hasattr(store, 'index'):
image['index'][name] = len(store.index)
# else:
# image['index'][name] = 0
image['full'] = total_weak + total_strong
image['total_strong'] = total_strong
image['total_weak'] = total_weak
image['file'] = total_file
image['index'] = total_index
return image
def get_var_types(self):
"""
List all allowed variable type to be used in `create_variable`
Returns
-------
list of str
the list of variable types
"""
types = NetCDFPlus._type_conversion.keys()
types += ['obj.' + x for x in self.objects.keys()]
types += ['lazyobj.' + x for x in self.objects.keys()]
return sorted(types)
def var_type_to_nc_type(self, var_type):
"""
Return the compatible netCDF variable type for var_type
Parameters
----------
var_type :
Returns
-------
object
A object of netcdf compatible varible types
"""
if var_type.startswith('obj.') or var_type.startswith('lazyobj.'):
if self.reference_by_uuid:
nc_type = str
else:
nc_type = np.int32
else:
nc_type = NetCDFPlus._type_conversion[var_type]
return nc_type
def create_type_delegate(self, var_type):
"""
Create a variable value delegator for var_type
The delegator will convert automatically between the given variable type
and the netcdf compatible one
Parameters
----------
var_type : str
the variable type
Returns
-------
NetCDFPlus.Value_Delegate
the delegator instance
"""
getter = None
setter = None
store = None
to_uuid_chunks = lambda x: [x[i:i + 36] for i in range(0, len(x), 36)]
if var_type.startswith('obj.') or var_type.startswith('lazyobj.'):
store_name = str(var_type.split('.')[1])
store = self._stores[store_name]
base_type = store.content_class
get_is_iterable = lambda v: \
v.base_cls is not base_type if hasattr(v, 'base_cls') else \
hasattr(v, '__iter__')
set_is_iterable = lambda v: \
v.base_cls is not base_type if hasattr(v, 'base_cls') else \
hasattr(v, '__iter__')
if var_type == 'int':
getter = lambda v: v.tolist()
setter = lambda v: np.array(v)
elif var_type == 'bool':
getter = lambda v: v.astype(np.bool).tolist()
setter = lambda v: np.array(v, dtype=np.int8)
elif var_type == 'index':
getter = lambda v: \
[None if int(w) < 0 else int(w) for w in v.tolist()] \
if hasattr(v, '__iter__') else None if int(v) < 0 else int(v)
setter = lambda v: \
[-1 if w is None else w for w in v] \
if hasattr(v, '__iter__') else -1 if v is None else v
elif var_type == 'float':
getter = lambda v: v.tolist()
setter = lambda v: np.array(v)
elif var_type.startswith('numpy.'):
pass
elif var_type == 'jsonobj':
setter = lambda v: self.simplifier.to_json_object(v)
getter = lambda v: self.simplifier.from_json(v)
elif var_type == 'json':
setter = lambda v: self.simplifier.to_json(v)
getter = lambda v: self.simplifier.from_json(v)
elif var_type.startswith('obj.'):
if not self.reference_by_uuid:
getter = lambda v: [
None if int(w) < 0 else store.load(int(w))
for w in v.tolist()
] if get_is_iterable(v) else \
None if int(v) < 0 else store.load(int(v))
setter = lambda v: \
np.array(
[-1 if w is None else store.save(w) for w in v],
dtype=np.int32
) if set_is_iterable(v) else \
-1 if v is None else store.save(v)
else:
getter = lambda v: [
None if w[0] == '-' else store.load(UUID(w))
for w in to_uuid_chunks(v)
] if get_is_iterable(v) else \
None if v[0] == '-' else store.load(UUID(v))
setter = lambda v: \
''.join(['-' * 36 if w is None else str(store.save(w))
for w in list.__iter__(v)]) \
if set_is_iterable(v) else \
'-' * 36 if v is None else str(store.save(v))
elif var_type == 'obj':
# arbitrary object
set_iterable_simple = lambda v: \
False if hasattr(v, 'base_cls') else hasattr(v, '__iter__')
getter = lambda v: [
None if int(w[1]) < 0 else
self.stores[int(w[0])].load(int(w[1]))
for w in v.tolist()] \
if len(v.shape) > 1 else None if int(v[1]) < 0 else \
self.stores[int(v[0])].load(int(v[1]))
setter = lambda v: \
np.array(
[(-1, -1) if w is None else self.save(w)[1:] for w in v],
dtype=np.int32) if set_iterable_simple(v) else \
(-1, -1) if v is None else self.save(v)[1:]
elif var_type.startswith('lazyobj.'):
if not self.reference_by_uuid:
getter = lambda v: [
None if int(w) < 0 else LoaderProxy(store, int(w))
for w in v.tolist()
] if get_is_iterable(v) else \
None if int(v) < 0 else LoaderProxy(store, int(v))
setter = lambda v: \
np.array(
[-1 if w is None else store.save(w) for w in v],
dtype=np.int32) if set_is_iterable(v) else \
-1 if v is None else store.save(v)
else:
getter = lambda v: [
None if w[0] == '-' else LoaderProxy(store, UUID(w))
for w in to_uuid_chunks(v)
] if get_is_iterable(v) else \
None if v[0] == '-' else LoaderProxy(store, UUID(v))
setter = lambda v: \
''.join([
'-' * 36 if w is None else str(store.save(w))
for w in list.__iter__(v)
]) if set_is_iterable(v) else \
'-' * 36 if v is None else str(store.save(v))
elif var_type == 'uuid':
getter = lambda v: \
[None if w[0] == '-' else UUID(w) for w in v] \
if type(v) is not unicode else None if v[0] == '-' else UUID(v)
setter = lambda v: \
''.join([
'-' * 36 if w is None else str(w)
for w in list.__iter__(v)
]) if hasattr(v, '__iter__') else \
'-' * 36 if v is None else str(v)
elif var_type == 'lazyobj':
# arbitrary object
set_iterable_simple = lambda v: \
False if hasattr(v, 'base_cls') else hasattr(v, '__iter__')
getter = lambda v: [
None if int(w[1]) < 0 else
LoaderProxy(self.stores[int(w[0])], int(w[1]))
for w in v.tolist()
] if len(v.shape) > 1 else \
None if int(v[1]) < 0 else \
LoaderProxy(self.stores[int(v[0])], int(v[1]))
setter = lambda v: \
np.array(
[(-1, -1) if w is None else self.save(w)[1:] for w in v],
dtype=np.int32
) if set_iterable_simple(v) else \
(-1, -1) if v is None else self.save(v)[1:]
elif var_type == 'store':
setter = lambda v: v.prefix
getter = lambda v: self.objects[v]
return getter, setter, store
to_uuid_chunks = staticmethod(
lambda x: [x[i:i + 36] for i in range(0, len(x), 36)])
def create_variable_delegate(self, var_name):
"""
Create a delegate property that wraps the netcdf.Variable and takes care
of type conversions
Parameters
----------
var_name : str
the name of the variable for which a delegate should be created
"""
if var_name not in self.vars:
var = self.variables[var_name]
if not hasattr(var, 'var_type'):
return
getter, setter, store = self.create_type_delegate(var.var_type)
if self.reference_by_uuid:
to_uuid_chunks = NetCDFPlus.to_uuid_chunks
if hasattr(var, 'var_vlen'):
if var.var_type.startswith('obj.'):
getter = lambda v: [
None if w[0] == '-' else store.load(UUID(w))
for w in to_uuid_chunks(v)
]
elif var.var_type.startswith('lazyobj.'):
getter = lambda v: [
None if w[0] == '-' else LoaderProxy(store, UUID(w))
for w in to_uuid_chunks(v)
]
else:
if var.var_type.startswith('obj.'):
getter = lambda v: \
None if v[0] == '-' else store.load(UUID(v))
elif var.var_type.startswith('lazyobj.'):
getter = lambda v: None if v[0] == '-' \
else LoaderProxy(store, UUID(v))
if True or self.support_simtk_unit:
if hasattr(var, 'unit_simtk'):
if var_name not in self.units:
self.update_simtk_unit(var_name)
unit = self.units[var_name]
def _get(my_getter):
import simtk.unit as u
if my_getter is None:
return lambda v: u.Quantity(v, unit)
else:
return lambda v: u.Quantity(my_getter(v), unit)
def _set(my_setter):
if my_setter is None:
return lambda v: v / unit
else:
return lambda v: my_setter(v / unit)
getter = _get(getter)
setter = _set(setter)
if True:
if hasattr(var, 'maskable'):
def _get2(my_getter):
return lambda v: [
None if hasattr(w, 'mask') else
my_getter(w) for w in v
] if type(v) is not str and len(v.shape) > 0 else \
None if hasattr(v, 'mask') else my_getter(v)
if getter is not None:
getter = _get2(getter)
else:
getter = _get2(lambda v: v)
self.vars[var_name] = \
NetCDFPlus.ValueDelegate(var, getter, setter, store)
else:
raise ValueError("Variable '%s' is already taken!" % var_name)
def create_variable(self, var_name,
var_type,
dimensions,
description=None,
chunksizes=None,
simtk_unit=None,
maskable=False):
"""
Create a new variable in the netCDF storage.
This is just a helper function to structure the code better and add
some convenience to creating more complex variables
Parameters
==========
var_name : str
The name of the variable to be created
var_type : str
The string representing the type of the data stored in the
variable. Allowed are strings of native python types in which
case the variables will be treated as python or a string of the
form 'numpy.type' which will refer to the numpy data types.
Numpy is preferred sinec the api to netCDF uses numpy and thus
it is faster. Possible input strings are
`int`, `float`, `long`, `str`, `numpy.float32`, `numpy.float64`,
`numpy.int8`, `numpy.int16`, `numpy.int32`, `numpy.int64`, `json`,
`obj.<store>`, `lazyobj.<store>`
dimensions : str or tuple of str
A tuple representing the dimensions used for the netcdf variable.
If not specified then the default dimension of the storage is used.
If the last dimension is `'...'` then it is assumed that the
objects are of variable length. In netCDF this is usually
referred to as a VLType. We will treat is just as another
dimension, but it can only be the last dimension.
description : str
A string describing the variable in a readable form.
chunksizes : tuple of int
A tuple of ints per number of dimensions. This specifies in what
block sizes a variable is stored. Usually for object related stuff
we want to store everything of one object at once so this is often
(1, ..., ...)
simtk_unit : str
A string representing the units used for this variable. Can be
used with all var_types although it makes sense only for numeric
ones.
maskable : bool, default: False
If set to `True` the values in this variable can only partially
exist and if they have not yet been written they are filled with
a fill_value which is treated as a non-set variable. The created
variable will interpret this values as `None` when returned
"""
ncfile = self
if type(dimensions) is str:
dimensions = [dimensions]
dimensions = list(dimensions)
new_dimensions = dict()
for ix, dim in enumerate(dimensions):
if type(dim) is int:
dimensions[ix] = var_name + '_dim_' + str(ix)
new_dimensions[dimensions[ix]] = dim
if dimensions[-1] == '...':
# last dimension is simply [] so we allow arbitrary length
# and remove the last dimension
variable_length = True
dimensions = dimensions[:-1]
else:
variable_length = False
if var_type == 'obj' or var_type == 'lazyobj':
dimensions.append('pair')
if chunksizes is not None:
chunksizes = tuple(list(chunksizes) + [2])
nc_type = self.var_type_to_nc_type(var_type)
for dim_name, size in new_dimensions.items():
ncfile.create_dimension(dim_name, size)
dimensions = tuple(dimensions)
# if chunk sizes are strings then replace it by
# the actual size of the dimension
if chunksizes is not None:
chunksizes = list(chunksizes)
for ix, dim in enumerate(chunksizes):
if dim == -1:
chunksizes[ix] = len(ncfile.dimensions[dimensions[ix]])
if type(dim) is str:
chunksizes[ix] = len(ncfile.dimensions[dim])
chunksizes = tuple(chunksizes)
if variable_length:
vlen_t = ncfile.createVLType(nc_type, var_name + '_vlen')
ncvar = ncfile.createVariable(
var_name, vlen_t, dimensions, chunksizes=chunksizes
)
setattr(ncvar, 'var_vlen', 'True')
else:
ncvar = ncfile.createVariable(
var_name, nc_type, dimensions, chunksizes=chunksizes,
)
setattr(ncvar, 'var_type', var_type)
if self.support_simtk_unit and simtk_unit is not None:
import simtk.unit as u
if isinstance(simtk_unit, u.Unit):
unit_instance = simtk_unit
symbol = unit_instance.get_symbol()
elif isinstance(simtk_unit, u.BaseUnit):
unit_instance = u.Unit({simtk_unit: 1.0})
symbol = unit_instance.get_symbol()
elif type(simtk_unit) is str and hasattr(u, simtk_unit):
unit_instance = getattr(u, simtk_unit)
symbol = unit_instance.get_symbol()
else:
raise NotImplementedError(
'Unit by abbreviated string representation '
'is not yet supported')
json_unit = self.simplifier.unit_to_json(unit_instance)
# store the unit in the dict inside the Storage object
self.units[var_name] = unit_instance
# Define units for a float variable
setattr(ncvar, 'unit_simtk', json_unit)
setattr(ncvar, 'unit', symbol)
if maskable:
setattr(ncvar, 'maskable', 'True')
if description is not None:
if type(dimensions) is str:
dim_names = [dimensions]
else:
dim_names = map(
lambda p: '#ix{0}:{1}'.format(*p),
enumerate(dimensions))
idx_desc = '[' + ']['.join(dim_names) + ']'
description = var_name + idx_desc + ' is ' + \
description.format(idx=dim_names[0], ix=dim_names)
# Define long (human-readable) names for variables.
setattr(ncvar, "long_str", description)
self.update_delegates()
return ncvar
def update_delegates(self):
"""
Updates the set of delegates in `self.vars`
Should be called after new variables have been created or loaded.
"""
for name in self.variables:
if name not in self.vars:
self.create_variable_delegate(name)
@staticmethod
def get_value_parameters(value):
"""
Compute netcdfplus compatible parameters to store a value
Parameters
----------
value
Returns
-------
dict
A dictionary containing the approriate input parameters for
`var_type`, `dimensions`, `simtk_unit`
Notes
-----
This is a utility function to create a CV using a template
"""
dimensions = None
storable = True
simtk_unit = None
test_value = value
test_type = value
if NetCDFPlus.support_simtk_unit:
import simtk.unit as u
if type(test_type) is u.Quantity:
# could be a Quantity([..])
simtk_unit = test_type.unit
test_type = test_type._value
else:
u = None
if type(test_type) is np.ndarray:
dimensions = test_type.shape
else:
if hasattr(test_value, '__len__'):
dimensions = len(test_value)
test_type = test_value[0]
if NetCDFPlus.support_simtk_unit and type(test_type) \
is u.Quantity:
for val in test_value:
if isinstance(val._value, type(test_value._value)):
# all values must be of same type
storable = False
else:
for val in test_value:
if type(val) is not type(test_value):
# all values must be of same type
storable = False
if NetCDFPlus.support_simtk_unit and type(test_type) is u.Quantity:
# could also be [Quantity, ...]
simtk_unit = test_type.unit
test_type = test_type._value
if storable:
var_type = NetCDFPlus.identify_var_type(test_type)
return {
'var_type': var_type,
'dimensions': dimensions,
'simtk_unit': simtk_unit
}
return {
}
@staticmethod
def identify_var_type(instance):
"""
Identify common python and numpy types
Parameters
----------
instance
python variable instance to be tested for it numeric type
Returns
-------
str
a string representation of the variable type
"""
ty = type(instance)
known_types = [float, int, bool, str]
if ty in known_types:
return ty.__name__
elif hasattr(instance, 'dtype'):
return 'numpy.' + instance.dtype.type.__name__
else:
return 'None'