[docs]class Trajectory(DerivedParameterGroup, ResultGroup, ParameterGroup, ConfigGroup):
"""The trajectory manages results and parameters.
The trajectory provides all functionality to define how the parameter space
of your simulation should be explored. During single runs based on a particular
parameter point, the functionality fo the trajectory is reduced.
You can add four types of data to the trajectory:
* Config:
These are special parameters specifying modalities of how to run your simulations.
Changing a config parameter should NOT have any influence on the results you
obtain from your simulations.
They specify runtime environment parameters like how many CPUs you use for
multiprocessing etc.
In fact, if you use the default runtime environment of this project, the environment
will add some config parameters to your trajectory.
The method to add more config is :func:`~pypet.naturalnaming.ConfigGroup.f_add_config`
Config parameters are put into the subtree `traj.config` (with `traj` being your
trajectory instance).
* Parameters:
These are your primary ammunition in numerical simulations. They specify
how your simulation works. They can only be added before the actual
running of the simulation exploring the parameter space. They can be
added via :func:`~pypet.naturalnaming.ParameterGroup.f_add_parameter`
and be explored using :func:`~pypet.trajectory.Trajectory.f_explore`.
Or to expand an existing trajectory use :func:`~pypet.trajectory.Trajectory.f_expand`.
Your parameters should encompass all values that completely define your simulation,
I recommend also storing random number generator seeds as parameters to
guarantee that a simulation can be repeated exactly the way it was run the first time.
Parameters are put into the subtree `traj.parameters`.
* Derived Parameters:
They are not much different from parameters except that they can be added anytime.
Conceptually this encompasses stuff that is intermediately
computed from the original parameters. For instance, as your original
parameters you have a random number seed and some other parameters.
From these you compute a connection matrix for a neural network.
This connection matrix could be stored as a derived parameter.
Derived parameters are added via
:func:`~pypet.naturalnaming.DerivedParameterGroup.f_add_derived_parameter`.
Derived parameters are put into the subtree `traj.derived_parameters`.
They are further sorted into `traj.derived_parameters.runs.run_XXXXXXXX` if they were
added during a single run. `XXXXXXXX` is replaced by the index of the corresponding run,
for example `run_00000001`.
* Results:
Result are added via the :func:`~pypet.naturalnaming.ResultGroup.f_add_result`.
They are kept under the subtree `traj.results` and are further sorted into
`traj.results.runs.run_XXXXXXXX` if they are added during a single run.
There are several ways to access the parameters and results, to learn about these, fast access,
and natural naming see :ref:`more-on-access`.
In case you create a new trajectory you can pass the following arguments:
:param name:
Name of the trajectory, if `add_time=True` the current time is added as a string
to the parameter name.
:param add_time:
Boolean whether to add the current time in human readable format to the trajectory name.
:param comment:
A useful comment describing the trajectory.
:param dynamic_imports:
If you've written a custom parameter that needs to be loaded dynamically during runtime,
this needs to be specified here as a list of classes or strings naming classes
and there module paths. For example:
`dynamic_imports = ['pypet.parameter.PickleParameter',MyCustomParameter]`
If you only have a single class to import, you do not need the list brackets:
`dynamic_imports = 'pypet.parameter.PickleParameter'`
:param storage_service:
Pass a storage service used by the Trajectory. Alternatively, pass a constructor
and other ``**kwargs`` are passed onto the constructor.
:param kwargs:
Other arguments passed to the storage service constructor
:raises:
ValueError: If the name of the trajectory contains invalid characters or
not all additional keyword arguments are used.
TypeError: If the dynamically imported classes are not classes or strings.
Example usage:
>>> traj = Trajectory('ExampleTrajectory', dynamic_imports=['Some.custom.class'],\
comment = 'I am a neat example!', storage_service=HDF5StorageService, \
filename='experiment.hdf5', file_title='Experiments')
"""
@kwargs_api_change('dynamically_imported_classes', 'dynamic_imports')
def __init__(self, name='my_trajectory', add_time=True, comment='',
dynamic_imports=None, storage_service=None, **kwargs):
# This is true during the actual runs:
self._is_run = False
# Helper variable: During a multiprocessing single run, the trajectory is usually
# pickled without all the parameter exploration ranges and all run information
# As a consequence, __len__ would return 1, so we need to store the length in this
# helper variable to return the correct length during single runs.
self._length = 1
super(Trajectory, self).__init__()
self._version = VERSION
self._python = '.'.join([str(x) for x in sys.version_info[0:3]])
init_time = time.time()
formatted_time = datetime.datetime.fromtimestamp(init_time).strftime('%Y_%m_%d_%Hh%Mm%Ss')
self._timestamp = init_time
self._time = formatted_time
# Times related to single runs
self._runtime_run = None
self._timestamp_run = None
self._time_run = None
self._finish_timestamp_run = None
if add_time:
self._name = name + '_' + str(formatted_time)
else:
self._name = name
self._parameters = {} # Contains all parameters
self._derived_parameters = {} # Contains all derived parameters
self._results = {} # Contains all results
self._explored_parameters = {} # Contains all explored parameters
self._config = {} # Contains all config parameters
self._all_groups = {} # Contains ALL groups regardless in which subtree they are
self._run_parent_groups = {} # Contains all groups which are parents of run groups
self._other_leaves = {} # Contains ALL other user added leaves
self._linked_by = {} #Contains all nodes that are linked to
self._changed_default_parameters = {} # Needed for paremeter presetting
self._single_run_ids = {} # A bidrectional dictionary conataining the mapping between
# a run name and the run index (e.g. `1 <-> 'run_00000001'`), in both directions
self._run_information = {} # Nested Dictionary with run names as keys and
# dictionaries as values. The inner dictionaries contain meta information about the runs
# like time of creation, whether they have been completed and so on.
# Check function 'f_get_run_information' for a description
# Per Definition the length is set to be 1. Even with an empty trajectory
# in principle you could make a single run
self._add_run_info(0)
self._nn_interface = NaturalNamingInterface(root_instance=self)
self._fast_access = True
self._shortcuts = True
self._iter_recursive = False
self._max_depth = None
self._auto_load = False
self._with_links = True
self._fast_adding = True
self._expansion_not_stored = False
self._environment_hexsha = None
self._environment_name = None
# Index of a trajectory is -1, if the trajectory should behave like a single run
# and blind out other single run results, this can be changed via 'v_crun'.
self._idx = -1
self._crun = None
self._standard_parameter = Parameter
self._standard_result = Result
self._standard_leaf = Result
self._full_copy = False
self._dynamic_imports = ['pypet.parameter.PickleParameter']
if not dynamic_imports is None:
self.f_add_to_dynamic_imports(dynamic_imports)
faulty_names = self._nn_interface._check_names([name])
if '.' in name:
faulty_names += ' colons >>.<< are not allowed in trajectory names,'
if faulty_names:
raise ValueError('Your Trajectory %s f_contains the following not admissible names: '
'%s please choose other names.'
% (name, faulty_names))
self._set_logger()
self._comment = ''
self.v_comment = comment
self._storage_service, unused_kwargs = storage_factory(storage_service=storage_service,
trajectory=self, **kwargs)
if len(unused_kwargs) > 0:
raise ValueError('The following keyword arguments were not used: `%s`' %
str(unused_kwargs))
def __getstate__(self):
result = self.__dict__.copy()
# Do not copy run information in case `v_full_copy` is `False`.
# Accordingly, we need to store the length in the helper variable
# `_length_during_nfc`.
if not self.v_full_copy and self.v_crun is not None:
runname = self._single_run_ids[self.v_idx]
result['_run_information'] = {runname: self._run_information[runname]}
result['_single_run_ids'] = {self.v_idx: runname, runname: self.v_idx}
del result['_logger']
return result
def __setstate__(self, statedict):
self.__dict__.update(statedict)
self._set_logger()
def __str__(self):
if self.v_comment:
commentstring = '`%s`, ' % self.v_comment
else:
commentstring = ''
info_string = '(%slen:%d)' % (commentstring, len(self))
children_string = self._get_children_representation()
if self._is_run:
runstr = ' (during single run)'
else:
runstr = ''
return '%s%s %s %s: %s' % (self.f_get_class_name(), runstr, self.v_name, info_string,
children_string)
@property
@deprecated('No longer supported, please refer to the storage service instead.')
[docs] def v_filename(self):
"""The name and path of the hdf5 file in case you use the HDF4StorageService"""
try:
return self.v_storage_serivce.filename
except AttributeError:
return None
@property
[docs] def v_version(self):
"""The version of *pypet* that was used to create the trajectory"""
return self._version
@property
[docs] def v_python(self):
"""The version of python as a string that was used to create the trajectory"""
return self._python
@property
def v_comment(self):
"""Should be a nice descriptive comment"""
return self._comment
@v_comment.setter
@not_in_run
@property
def v_storage_service(self):
"""The service that can store the trajectory to disk or wherever.
Default is None or if a filename was provided on construction
the :class:`~pypet.storageservice.HDF5StorageService`.
"""
return self._storage_service
@v_storage_service.setter
@not_in_run
[docs] def v_storage_service(self, service):
"""Sets the storage service"""
self._storage_service = service
@property
[docs] def v_is_run(self):
""" ``True`` mak if trajectory is used during a single run initiated by an environment.
Accordingly, the functionality of the trajectory is reduced.
"""
return self._is_run
@property
def v_idx(self):
"""Index if you want to access the trajectory as during a single run.
You can turn the trajectory to behave as if during the execution of your runs if you set
`v_idx` to a particular index. Note that only integer values are appropriate here,
not names of runs.
Alternatively instead of directly setting `v_idx` you can call
:func:`~pypet.trajectory.Trajectory.f_set_crun:`.
Set to `-1` to make the trajectory turn everything back to default.
"""
return self._idx
@v_idx.setter
@not_in_run
[docs] def v_idx(self, idx):
"""Changes the index to make the trajectory behave as a single run"""
self.f_set_crun(idx)
@property
@deprecated(msg='Please use `v_crun` instead')
def v_as_run(self):
return self.v_crun
@v_as_run.setter
@not_in_run
@deprecated(msg='Please use `v_crun` instead')
def v_as_run(self, run_name):
"""Changes the run name to make the trajectory behave as a single run"""
self.v_crun = run_name
@property
def v_crun(self):
"""Run name if you want to access the trajectory as a single run.
You can turn the trajectory to behave as during a single run if you set
`v_crun` to a particular run name. Note that only string values are appropriate here,
not indices. Check the `v_idx` property if you want to provide an index.
Alternatively instead of directly setting `v_crun` you can call
:func:`~pypet.trajectory.Trajectory.f_set_crun:`.
Set to `None` to make the trajectory to turn everything back to default.
"""
return self._crun
@v_crun.setter
@not_in_run
[docs] def v_crun(self, run_name):
"""Changes the run name to make the trajectory behave as during a single run"""
self.f_set_crun(run_name)
@property
def v_full_copy(self):
"""Whether trajectory is copied fully during pickling or only the current
parameter space point.
Note if the trajectory is copied as a whole, also during a single run
you can access the full parameter space.
Changing `v_full_copy` will also change `v_full_copy` of all explored parameters!
"""
return self._full_copy
@v_full_copy.setter
@not_in_run
[docs] def v_full_copy(self, val):
""" Sets full copy mode of trajectory and (!) ALL explored parameters!"""
if val != self._full_copy:
self._full_copy = val
for param in compat.itervalues(self._explored_parameters):
param.v_full_copy = val
@property
def v_with_links(self):
"""Whether links should be considered in case using natural naming
or squared bracket indexing"""
return self._with_links
@ v_with_links.setter
[docs] def v_with_links(self, val):
"""Sets the usage of links"""
self._with_links = bool(val)
[docs] def f_set_properties(self, **kwargs):
"""Sets properties like ``v_fast_access``.
For example: ``traj.f_set_properties(v_fast_access=True, v_auto_load=False)``
"""
for name in kwargs:
val = kwargs[name]
if not name.startswith('v_'):
name = 'v_' + name
if not name in self._nn_interface._not_admissible_names:
raise AttributeError('Cannot set property `%s` does not exist.' % name)
else:
setattr(self, name, val)
@not_in_run
[docs] def f_add_to_dynamic_imports(self, dynamic_imports):
"""Adds classes or paths to classes to the trajectory to create custom parameters.
:param dynamic_imports:
If you've written custom parameter that needs to be loaded dynamically during runtime,
this needs to be specified here as a list of classes or strings naming classes
and there module paths. For example:
`dynamic_imports = ['pypet.parameter.PickleParameter',MyCustomParameter]`
If you only have a single class to import, you do not need the list brackets:
`dynamic_imports = 'pypet.parameter.PickleParameter'`
"""
if not isinstance(dynamic_imports, (list, tuple)):
dynamic_imports = [dynamic_imports]
for item in dynamic_imports:
if not (isinstance(item, compat.base_type) or inspect.isclass(item)):
raise TypeError('Your dynamic import `%s` is neither a class nor a string.' %
str(item))
self._dynamic_imports.extend(dynamic_imports)
@not_in_run
@deprecated(msg='Please use `pypet.trajectory.Trajectory.f_set_crun` instead.')
def f_as_run(self, name_or_idx):
return self.f_set_crun(name_or_idx)
@not_in_run
[docs] def f_set_crun(self, name_or_idx):
"""Can make the trajectory behave as during a particular single run.
It allows easier data analysis.
Has the following effects:
*
`v_idx` and `v_crun` are set to the appropriate index and run name
*
All explored parameters are set to the corresponding value in the exploration
ranges, i.e. when you call :func:`~pypet.parameter.Parameter.f_get` (or fast access)
on them you will get in return the value at the corresponding `v_idx` position
in the exploration range.
*
If you perform a search in the trajectory tree, the trajectory will
only search the run subtree under *results* and *derived_parameters* with the
corresponding index.
For instance, if you use `f_set_crun('run_00000007')` or `f_set_crun(7)`
and search for `traj.results.z` this will search for `z` only in the subtree
`traj.results.run_00000007`. Yet, you can still explicitly name other subtrees,
i.e. `traj.results.run_00000004.z` will still work.
"""
if (name_or_idx is None or name_or_idx == pypetconstants.RUN_NAME_DUMMY or
name_or_idx == -1):
self.f_restore_default()
else:
if isinstance(name_or_idx, compat.base_type):
self._idx = self.f_idx_to_run(name_or_idx)
self._crun = name_or_idx
else:
self._crun = self.f_idx_to_run(name_or_idx)
self._idx = name_or_idx
self._set_explored_parameters_to_idx(self.v_idx)
@not_in_run
[docs] def f_iter_runs(self):
"""Makes the trajectory iterate over all runs.
Note that after a full iteration, the trajectory is set back to normal.
Thus, the following code snippet
::
for run_name in traj.f_iter_runs():
# Do some stuff here...
is equivalent to
::
for run_name in traj.f_get_run_names(sort=True):
traj.f_set_crun(run_name)
# Do some stuff here...
traj.f_set_crun(None)
:return:
Iterator over runs. The iterator itself will return the run names but modify
the trajectory in each iteration and set it back do normal in the end.
"""
for run_name in self.f_get_run_names(sort=True):
self.f_set_crun(run_name)
yield run_name
self.f_set_crun(None)
def _remove_incomplete_runs(self, start_timestamp, run_indices, snapshot_traj):
"""Requests the storage service to delete incomplete runs.
Called by the environment if you resume a crashed trajectory to allow
re-running of non-completed runs.
:param finished_runs:
Number of actually finished runs
:param nruns:
Number of runs that were supposed to be executed with this environment
:param snapshot_traj:
The trajectory that was part of the snapshot, we need it to make sure, that
we do not remove anything that was already part of the snapshot.
:return
List of indices that were completed
"""
self._logger.info('Removing incomplete runs.')
index_set = set(run_indices)
# First check if the completed runs are also finished runs (i.e. are part of the snapshot)
# If not remove these
for run_name in self._run_information:
info_dict = self._run_information[run_name]
completed = info_dict['completed']
timestamp = info_dict['timestamp']
idx = info_dict['idx']
name = info_dict['name']
if completed and timestamp >= start_timestamp and idx not in index_set:
self._run_information[name]['completed'] = 0
cleaned_run_indices = []
for index in run_indices:
if self.f_is_completed(index):
cleaned_run_indices.append(index)
# Next, delete all the data for not completed runs
for run_name in self._run_information:
info_dict = self._run_information[run_name]
completed = info_dict['completed']
if not completed:
for where in snapshot_traj._run_parent_groups:
run_parent = snapshot_traj._run_parent_groups[where]
if run_parent.f_contains(run_name, shortcuts=False):
run_node = run_parent.f_get(run_name, shortcuts=False)
items = []
if run_node.v_is_group:
for grp in run_node.f_iter_nodes(with_links=False):
if (not grp.v_is_leaf and grp.f_has_links() and
not self.f_contains(grp.v_full_name,
shortcuts=False,
with_links=False)):
link_tuples = zip(itools.repeat(grp),
compat.listkeys(grp._links))
snapshot_traj.f_delete_links(link_tuples,
remove_from_trajectory=True)
for node in run_node.f_iter_nodes(with_links=False):
if not self.f_contains(node.v_full_name, shortcuts=False,
with_links=False):
items.append(node)
items = reversed(items) # Reverse so that we delete
# leaf nodes before group nodes, i.e. group nodes
# are empty when whe delete them
elif not self.f_contains(run_node.v_full_name, shortcuts=False,
with_links=False):
items = [run_node]
if items:
snapshot_traj.f_delete_items(items,
remove_from_trajectory=True)
if (snapshot_traj.f_contains(run_node.v_full_name,
shortcuts=False) and
run_node.f_has_children()):
for node in run_node.f_iter_nodes(with_links=False):
if not self.f_contains(node.v_full_name,
shortcuts=False,
with_links=False):
raise RuntimeError('Something is wrong')
return cleaned_run_indices
@not_in_run
[docs] def f_shrink(self, force=False):
""" Shrinks the trajectory and removes all exploration ranges from the parameters.
Only possible if the trajectory has not been stored to disk before or was loaded as new.
:param force:
Usually you cannot shrink the trajectory if it has been stored to disk,
because there's no guarantee that it is actually shrunk if there
still exist explored parameters on disk. In case you are certain that
you did not store explored parameters to disk set or you deleted all
of them from disk set `force=True`.
:raises: TypeError if the trajectory was stored before.
"""
if self._stored and not force:
raise TypeError('Your trajectory is already stored to disk or database, shrinking is '
'not allowed.')
for param in compat.itervalues(self._explored_parameters):
param.f_unlock()
try:
param._shrink()
except Exception as e:
self._logger.error('Could not shrink `%s` because of:`%s`' %
(param.v_full_name, str(e)))
# If we shrink, we do not have any explored parameters left and we can erase all
# run information, and the length of the trajectory is 1 again.
self._explored_parameters = {}
self._run_information = {}
self._single_run_ids = {}
self._add_run_info(0)
def _preset(self, name, args, kwargs):
"""Generic preset function, marks a parameter or config for presetting."""
if self.f_contains(name, shortcuts=False):
raise ValueError('Parameter `%s` is already part of your trajectory, use the normal'
'accessing routine to change config.' % name)
else:
self._changed_default_parameters[name] = (args, kwargs)
@not_in_run
[docs] def f_preset_config(self, config_name, *args, **kwargs):
"""Similar to func:`~pypet.trajectory.Trajectory.f_preset_parameter`"""
if not config_name.startswith('config.'):
config_name = 'config.' + config_name
self._preset(config_name, args, kwargs)
@not_in_run
[docs] def f_preset_parameter(self, param_name, *args, **kwargs):
"""Presets parameter value before a parameter is added.
Can be called before parameters are added to the Trajectory in order to change the
values that are stored into the parameter on creation.
After creation of a parameter, the instance of the parameter is called
with `param.f_set(*args,**kwargs)` with `*args`, and `**kwargs` provided by the user
with `f_preset_parameter`.
Before an experiment is carried out it is checked if all parameters that were
marked were also preset.
:param param_name:
The full name (!) of the parameter that is to be changed after its creation.
:param args:
Arguments that will be used for changing the parameter's data
:param kwargs:
Keyword arguments that will be used for changing the parameter's data
Example:
>>> traj.f_preset_parameter('groupA.param1', data=44)
>>> traj.f_add_parameter('groupA.param1', data=11)
>>> traj.parameters.groupA.param1
44
"""
if not param_name.startswith('parameters.'):
param_name = 'parameters.' + param_name
self._preset(param_name, args, kwargs)
def _prepare_experiment(self, clean_up_runs):
"""Called by the environment to make some initial configurations before performing the
individual runs.
Checks if all parameters marked for presetting were preset. If not raises a
DefaultReplacementError.
Locks all parameters.
Removal of potential results of previous runs in case the trajectory was expanded to avoid
mixing up undesired shortcuts in natural naming.
"""
if len(self._changed_default_parameters):
raise pex.PresettingError(
'The following parameters were supposed to replace a '
'default value, but it was never tried to '
'add default values with these names: %s' %
str(self._changed_default_parameters))
self.f_lock_parameters()
self.f_lock_derived_parameters()
if clean_up_runs:
# If the trajectory is ought to be expanded we remove the subtrees of previous results
# since they won't be used during an experiment
self._remove_run_data()
@not_in_run
@kwargs_api_change('backwards_search')
@kwargs_api_change('where')
[docs] def f_get_from_runs(self, name, include_default_run=True, use_indices=False,
fast_access=False, with_links = True,
shortcuts=True, max_depth=None, auto_load=False):
"""Searches for all occurrences of `name` in each run.
Generates an ordered dictionary with the run names or indices as keys and
found items as values.
Example:
>>> traj.f_get_from_runs(self, 'deep.universal_answer', use_indices=True, fast_access=True)
OrderedDict([(0, 42), (1, 42), (2, 'fortytwo), (3, 43)])
:param name:
String description of the item(s) to find.
Cannot be full names but the part of the names that are below
a `run_XXXXXXXXX` group.
:param include_default_run:
If results found under ``run_ALL`` should be accounted for every run or simply be
ignored.
:param use_indices:
If `True` the keys of the resulting dictionary are the run indices
(e.g. 0,1,2,3), otherwise the keys are run names (e.g. `run_00000000`,
`run_000000001`)
:param fast_access:
Whether to return parameter or result instances or the values handled by these.
:param with_links:
If links should be considered
:param shortcuts:
If shortcuts are allowed and the trajectory can *hop* over nodes in the
path.
:param max_depth:
Maximum depth (relative to start node) how search should progress in tree.
`None` means no depth limit. Only relevant if `shortcuts` are allowed.
:param auto_load:
If data should be loaded from the storage service if it cannot be found in the
current trajectory tree. Auto-loading will load group and leaf nodes currently
not in memory and it will load data into empty leaves. Be aware that auto-loading
does not work with shortcuts.
:return:
Ordered dictionary with run names or indices as keys and found items as values.
Will only include runs where an item was actually found.
"""
result_dict = OrderedDict()
old_crun = self.v_crun
try:
for run_name in self.f_iter_runs():
# Iterate over all runs
old_value = None
value = None
if len(self._run_parent_groups) > 0:
for run_parent_group in compat.itervalues(self._run_parent_groups):
try:
value = run_parent_group.f_get(name,
fast_access=False,
with_links=with_links,
shortcuts=shortcuts,
max_depth=max_depth,
auto_load=auto_load)
if old_value is not None:
raise pex.NotUniqueNodeError('`%s` has been found twice: '
'`%s` and `%s`' % (name,
old_value.v_full_name,
value.v_full_name))
if pypetconstants.RUN_NAME not in value.v_full_name:
value = old_value
raise AttributeError('Item does not belong to run')
elif (not include_default_run
and pypetconstants.RUN_NAME_DUMMY in value.v_full_name):
value = old_value
raise AttributeError('I was told to ignore default runs')
old_value = value
except (AttributeError, pex.DataNotInStorageError):
pass
if value is not None:
if value.v_is_leaf:
value = self._nn_interface._apply_fast_access(value, fast_access)
if use_indices:
key = self.f_idx_to_run(run_name)
else:
key = run_name
result_dict[key] = value
return result_dict
finally:
self.v_crun = old_crun
def __len__(self):
"""Length of trajectory, minimum length is 1"""
return self._length
@not_in_run
[docs] def f_is_completed(self, name_or_id=None):
"""Whether or not a given run is completed.
If no run is specified it is checked whether all runs were completed.
:param name_or_id: Nam or id of a run to check
:return: True or False
"""
return self._is_completed(name_or_id)
def _is_completed(self, name_or_id=None):
"""Private function such that it can still be called by the environment during
a single run"""
if name_or_id is None:
return all(
(runinfo['completed'] for runinfo in compat.itervalues(self._run_information)))
else:
return self.f_get_run_information(name_or_id)['completed']
@not_in_run
[docs] def f_expand(self, build_dict, fail_safe=True):
"""Similar to :func:`~pypet.trajectory.Trajectory.f_explore`, but can be used to enlarge
already completed trajectories.
Please ensure before usage, that all explored parameters are loaded!
:param build_dict:
Dictionary containing the expansion
:param fail_safe:
If old ranges should be **deep-copied** in order to allow to restore
the original exploration if something fails during expansion.
Set to `False` if deep-copying your parameter ranges causes errors.
:raises:
TypeError: If not all explored parameters are enlarged
AttributeError: If keys of dictionary cannot be found in the trajectory
NotUniqueNodeError:
If dictionary keys do not unambiguously map to single parameters
ValueError: If not all explored parameter ranges are of the same length
"""
enlarge_set = set([self.f_get(key).v_full_name
for key in build_dict.keys()])
# Check if all explored parameters will be enlarged, otherwise
# We cannot enlarge the trajectory
if not set(self._explored_parameters.keys()) == enlarge_set:
raise TypeError('You have to enlarge dimensions you have explored before! Currently'
' explored parameters are not the ones you specified in your building'
' dictionary, i.e. %s != %s' %
(str(set(self._explored_parameters.keys())),
str(set(build_dict.keys()))))
old_ranges = None
if fail_safe:
old_ranges = {}
for param_name in self._explored_parameters:
old_ranges[param_name] = self._explored_parameters[param_name].f_get_range()
try:
old_ranges = deepcopy(old_ranges)
except Exception:
self._logger.error('Cannot deepcopy old parameter ranges, if '
'something fails during `f_expand` I cannot revert the '
'trajectory to old settings.')
old_ranges = None
try:
count = 0
length = None
for key, builditerable in build_dict.items():
act_param = self.f_get(key)
act_param.f_unlock()
act_param._expand(builditerable)
name = act_param.v_full_name
self._explored_parameters[name] = act_param
# Compare the length of two consecutive parameters in the `build_dict`
if count == 0:
length = act_param.f_get_range_length()
elif not length == act_param.f_get_range_length():
raise ValueError('The parameters to explore have not the same size!')
count += 1
original_length = len(self)
for irun in range(original_length, length):
self._add_run_info(irun)
# We need to update the explored parameters:
if self._stored:
self._expansion_not_stored = True
except Exception:
if old_ranges is not None:
# Try to restore the original parameter exploration
for param_name in old_ranges:
param_range = old_ranges[param_name]
param = self._explored_parameters[param_name]
param.f_unlock()
try:
param._shrink()
except Exception as e:
self._logger.error('Could not shrink parameter `%s` '
'because of:`%s`' % (param_name, str(e)))
param._explore(param_range)
raise
def _store_expansion(self):
""" Called if trajectory is expanded, deletes all explored parameters from disk """
for param in compat.itervalues(self._explored_parameters):
try:
self._storage_service.store(pypetconstants.DELETE, param,
trajectory_name=self.v_name)
except Exception:
pass # We end up here if the parameter was not stored to disk, yet
self.f_store_item(param)
@not_in_run
[docs] def f_explore(self, build_dict):
"""Prepares the trajectory to explore the parameter space.
To explore the parameter space you need to provide a dictionary with the names of the
parameters to explore as keys and iterables specifying the exploration ranges as values.
All iterables need to have the same length otherwise a ValueError is raised.
A ValueError is also raised if the names from the dictionary map to groups or results
and not parameters.
If your trajectory is already explored but not stored yet and your parameters are
not locked you can add new explored parameters to the current ones if their
iterables match the current length of the trajectory.
Raises an AttributeError if the names from the dictionary are not found at all in
the trajectory and NotUniqueNodeError if the keys not unambiguously map
to single parameters.
Raises a TypeError if the trajectory has been stored already, please use
:func:`~pypet.trajectory.Trajectory.f_expand` then instead.
Example usage:
>>> traj.f_explore({'groupA.param1' : [1,2,3,4,5], 'groupA.param2':['a','b','c','d','e']})
Could also be called consecutively:
>>> traj.f_explore({'groupA.param1' : [1,2,3,4,5]})
>>> traj.f_explore({'groupA.param2':['a','b','c','d','e']})
NOTE:
Since parameters are very conservative regarding the data they accept
(see :ref:`type_conservation`), you sometimes won't be able to use Numpy arrays
for exploration as iterables.
For instance, the following code snippet won't work:
::
import numpy a np
from pypet.trajectory import Trajectory
traj = Trajectory()
traj.f_add_parameter('my_float_parameter', 42.4,
comment='My value is a standard python float')
traj.f_explore( { 'my_float_parameter': np.arange(42.0, 44.876, 0.23) } )
This will result in a `TypeError` because your exploration iterable
`np.arange(42.0, 44.876, 0.23)` contains `numpy.float64` values
whereas you parameter is supposed to use standard python floats.
Yet, you can use Numpys `tolist()` function to overcome this problem:
::
traj.f_explore( { 'my_float_parameter': np.arange(42.0, 44.876, 0.23).tolist() } )
Or you could specify your parameter directly as a numpy float:
::
traj.f_add_parameter('my_float_parameter', np.float64(42.4),
comment='My value is a numpy 64 bit float')
"""
for run_idx in range(len(self)):
if self.f_is_completed(run_idx):
raise TypeError('You cannot explore a trajectory which has been explored before, '
'please use `f_expand` instead.')
added_explored_parameters = []
try:
length = len(self)
for key, builditerable in build_dict.items():
act_param = self.f_get(key)
if not act_param.v_is_leaf or not act_param.v_is_parameter:
raise ValueError('%s is not an appropriate search string for a parameter.' % key)
act_param._explore(builditerable)
added_explored_parameters.append(act_param)
full_name = act_param.v_full_name
self._explored_parameters[full_name] = act_param
# Compare the length of two consecutive parameters in the `build_dict`
if len(self._explored_parameters) == 1:
length = act_param.f_get_range_length()
elif not length == act_param.f_get_range_length():
raise ValueError('The parameters to explore have not the same size!')
for irun in range(length):
self._add_run_info(irun)
except Exception:
# Remove the added parameters again
for param in added_explored_parameters:
param.f_unlock()
param._shrink()
full_name = param.v_full_name
del self._explored_parameters[full_name]
if len(self._explored_parameters) == 0:
self.f_shrink(force=True)
raise
def _add_run_info(self, idx, name=None, timestamp=42.0, finish_timestamp=1.337,
runtime='forever and ever', time='>>Maybe time`s gone on strike',
completed=0, parameter_summary='Not yet my friend!',
short_environment_hexsha='notyet!'):
"""Adds a new run to the `_run_information` dict."""
if name is None:
name = pypetconstants.FORMATTED_RUN_NAME % idx
# The `_single_run_ids` dict is bidirectional and maps indices to run names and vice versa
self._single_run_ids[name] = idx
self._single_run_ids[idx] = name
info_dict = {}
info_dict['idx'] = idx
info_dict['timestamp'] = timestamp
info_dict['finish_timestamp'] = finish_timestamp
info_dict['runtime'] = runtime
info_dict['time'] = time
info_dict['completed'] = completed
info_dict['name'] = name
info_dict['parameter_summary'] = parameter_summary
info_dict['short_environment_hexsha'] = short_environment_hexsha
self._run_information[name] = info_dict
self._length = len(self._run_information)
@not_in_run
[docs] def f_lock_parameters(self):
"""Locks all non-empty parameters"""
for par in compat.itervalues(self._parameters):
if not par.f_is_empty():
par.f_lock()
@not_in_run
[docs] def f_lock_derived_parameters(self):
"""Locks all non-empty derived parameters"""
for par in compat.itervalues(self._derived_parameters):
if not par.f_is_empty():
par.f_lock()
def _remove_run_data(self):
""" Removes all data of completed runs.
Keeps data that is externally linked.
"""
_keep_linked_lambda = lambda node: _keep_linked(self, node)
for group_name in self._run_parent_groups:
group = self._run_parent_groups[group_name]
for child_name in compat.listkeys(group.f_get_children(copy=False)):
if (child_name.startswith(pypetconstants.RUN_NAME) and
child_name != pypetconstants.RUN_NAME_DUMMY and
self.f_is_completed(child_name)):
group.f_remove_child(child_name, recursive=True,
predicate=_keep_linked_lambda)
def _finalize(self):
"""Final rollback initiated by the environment
Restores the trajectory as root of the tree, and loads meta data from disk.
This updates the trajectory's information about single runs, i.e. if they've been
completed, when they were started, etc.
"""
self._is_run = False
self.f_set_crun(None)
self.f_load(self.v_name, None, False, load_parameters=pypetconstants.LOAD_NOTHING,
load_derived_parameters=pypetconstants.LOAD_NOTHING,
load_results=pypetconstants.LOAD_NOTHING,
load_other_data=pypetconstants.LOAD_NOTHING)
@deprecated('Please use `f_load_skeleton` instead.')
def f_update_skeleton(self):
return self.f_load_skeleton()
@not_in_run
[docs] def f_load_skeleton(self):
"""Loads the full skeleton from the storage service.
This needs to be done after a successful exploration in order to update the
trajectory tree with all results and derived parameters from the individual single runs.
This will only add empty results and derived parameters (i.e. the skeleton)
and load annotations.
"""
self.f_load(self.v_name, as_new=False, load_parameters=pypetconstants.LOAD_SKELETON,
load_derived_parameters=pypetconstants.LOAD_SKELETON,
load_results=pypetconstants.LOAD_SKELETON,
load_other_data=pypetconstants.LOAD_SKELETON)
@not_in_run
@kwargs_api_change('load_all', 'load_data')
@kwargs_api_change('dynamically_imported_classes', 'dynamic_imports')
[docs] def f_load(self, name=None, index=None, as_new=False, load_parameters=pypetconstants.LOAD_DATA,
load_derived_parameters=pypetconstants.LOAD_SKELETON,
load_results=pypetconstants.LOAD_SKELETON,
load_other_data=pypetconstants.LOAD_SKELETON, load_data=None, force=False,
dynamic_imports=None, storage_service=None, **kwargs):
"""Loads a trajectory via the storage service.
If you want to load individual results or parameters manually, you can take
a look at :func:`~pypet.trajectory.Trajectory.f_load_items`.
To only load subtrees check out :func:`~pypet.naturalnaming.NNGroupNode.f_load_child`.
For `f_load` you can pass the following arguments:
:param name:
Name of the trajectory to be loaded. If no name or index is specified
the current name of the trajectory is used.
:param index:
If you don't specify a name you can specify an integer index instead.
The corresponding trajectory in the hdf5 file at the index
position is loaded (counting starts with 0). Negative indices are also allowed
counting in reverse order. For instance, `-1` refers to the last trajectory in
the file, `-2` to the second last, and so on.
:param as_new:
Whether you want to rerun the experiments. So the trajectory is loaded only
with parameters. The current trajectory name is kept in this case, which should be
different from the trajectory name specified in the input parameter `name`.
If you load `as_new=True` all parameters are unlocked.
If you load `as_new=False` the current trajectory is replaced by the one on disk,
i.e. name, timestamp, formatted time etc. are all taken from disk.
:param load_parameters: How parameters and config items are loaded
:param load_derived_parameters: How derived parameters are loaded
:param load_results: How results are loaded
You can specify how to load the parameters, derived parameters and results
as follows:
* :const:`pypet.pypetconstants.LOAD_NOTHING`: (0)
Nothing is loaded.
* :const:`pypet.pypetconstants.LOAD_SKELETON`: (1)
The skeleton is loaded including annotations (See :ref:`more-on-annotations`).
This means that only empty
*parameter* and *result* objects will
be created and you can manually load the data into them afterwards.
Note that :class:`pypet.annotations.Annotations` do not count as data and they
will be loaded because they are assumed to be small.
* :const:`pypet.pypetconstants.LOAD_DATA`: (2)
The whole data is loaded. Note in case you have non-empty leaves
already in RAM, these are left untouched.
* :const:`pypet.pypetconstants.OVERWRITE_DATA`: (3)
As before, but non-empty nodes are emptied and reloaded.
Note that in all cases except :const:`pypet.pypetconstants.LOAD_NOTHING`,
annotations will be reloaded if the corresponding instance
is created or the annotations of an existing instance were emptied before.
:param load_data:
As the above, per default set to `None`. If not `None` the setting of `load_data`
will overwrite the settings of `load_parameters`, `load_derived_parameters`,
`load_results`, and `load_other_data`. This is more or less or shortcut if all
types should be loaded the same.
:param force:
*pypet* will refuse to load trajectories that have been created using *pypet* with a
different version number or a different python version.
To force the load of a trajectory from a previous version
simply set ``force = True``. Note that it is not checked if other versions of packages
differ from previous experiments, i.e. numpy, scipy, etc. But you can check
for this manually. The versions of other packages can be found under
``'config.environment.name_of_environment.versions.package_name'``.
:param dynamic_imports:
If you've written a custom parameter that needs to be loaded dynamically
during runtime, this needs to be specified here as a list of classes or
strings naming classes and there module paths. For example:
`dynamic_imports = ['pypet.parameter.PickleParameter',MyCustomParameter]`
If you only have a single class to import, you do not need the list brackets:
`dynamic_imports = 'pypet.parameter.PickleParameter'`
The classes passed here are added for good and will be kept by the trajectory.
Please add your dynamically imported classes only once.
:param storage_service:
Pass a storage service used by the trajectory. Alternatively pass a constructor
and other ``**kwargs`` are passed onto the constructor. Leave `None` in combination
with using no other kwargs, if you don't want to change the service
the trajectory is currently using.
:param kwargs:
Other arguments passed to the storage service constructor. Don't pass any
other kwargs and ``storage_service=None``,
if you don't want to change the current service.
"""
# Do some argument validity checks first
if name is None and index is None:
name = self.v_name
if as_new:
load_parameters = pypetconstants.LOAD_DATA
load_derived_parameters = pypetconstants.LOAD_NOTHING
load_results = pypetconstants.LOAD_NOTHING
load_other_data = pypetconstants.LOAD_NOTHING
unused_kwargs = set(kwargs.keys())
if self.v_storage_service is None or storage_service is not None or len(kwargs) > 0:
self._storage_service, unused_kwargs = storage_factory(storage_service=storage_service,
trajectory=self, **kwargs)
if len(unused_kwargs) > 0:
raise ValueError('The following keyword arguments were not used: `%s`' %
str(unused_kwargs))
if not dynamic_imports is None:
self.f_add_to_dynamic_imports(dynamic_imports)
if load_data is not None:
load_parameters = load_data
load_derived_parameters = load_data
load_results = load_data
load_other_data = load_data
self._storage_service.load(pypetconstants.TRAJECTORY, self, trajectory_name=name,
trajectory_index=index,
as_new=as_new, load_parameters=load_parameters,
load_derived_parameters=load_derived_parameters,
load_results=load_results,
load_other_data=load_other_data,
force=force)
# If a trajectory is newly loaded, all parameters are unlocked.
if as_new:
for param in compat.itervalues(self._parameters):
param.f_unlock()
self._length = len(self._run_information)
def _check_if_both_have_same_parameters(self, other_trajectory,
ignore_trajectory_derived_parameters):
""" Checks if two trajectories live in the same space and can be merged. """
if not isinstance(other_trajectory, Trajectory):
raise TypeError('Can only merge trajectories, the other trajectory'
' is of type `%s`.' % str(type(other_trajectory)))
if self._stored:
self.f_load_skeleton()
if other_trajectory._stored:
other_trajectory.f_load_skeleton()
# Load all parameters of the current and the other trajectory
if self._stored:
# To suppress warnings if nothing needs to be loaded
with self._nn_interface._disable_logger:
self.f_load_items(compat.itervalues(self._parameters), only_empties=True)
if other_trajectory._stored:
with self._nn_interface._disable_logger:
other_trajectory.f_load_items(compat.itervalues(other_trajectory._parameters),
only_empties=True)
self.f_restore_default()
other_trajectory.f_restore_default()
allmyparams = self._parameters.copy()
allotherparams = other_trajectory._parameters.copy()
# If not ignored, add also the trajectory derived parameters to check for merging
if not ignore_trajectory_derived_parameters and 'derived_parameters' in self:
my_traj_dpars = self._get_traj_dpars_or_results(self, 'derived_parameters')
if self._stored:
self.f_load_items(compat.itervalues(my_traj_dpars), only_empties=True)
allmyparams.update(my_traj_dpars)
other_traj_dpars = self._get_traj_dpars_or_results(other_trajectory,
'derived_parameters')
if other_trajectory._stored:
other_trajectory.f_load_items(other_traj_dpars.values(),
only_empties=True)
allotherparams.update(other_traj_dpars)
# Check if the trajectories have the same parameters:
my_keyset = set(allmyparams.keys())
other_keyset = set(allotherparams.keys())
if not my_keyset == other_keyset:
diff1 = my_keyset - other_keyset
diff2 = other_keyset - my_keyset
run_difference_can_be_resolved = True
for full_name in diff1:
if pypetconstants.RUN_NAME not in full_name:
run_difference_can_be_resolved = False
break
if run_difference_can_be_resolved:
for full_name in diff2:
if pypetconstants.RUN_NAME not in full_name:
run_difference_can_be_resolved = False
break
if not run_difference_can_be_resolved:
raise TypeError('Cannot merge trajectories, '
'they do not live in the same space,the '
'f_set of parameters `%s` is only '
'found in the current trajectory '
'and `%s` only in the other trajectory.' %
(str(diff1), str(diff2)))
# Check if corresponding parameters in both trajectories are of the same type
for key, other_param in allotherparams.items():
my_param = self.f_get(key)
if pypetconstants.RUN_NAME in key:
pass
else:
if not my_param._values_of_same_type(my_param.f_get(), other_param.f_get()):
raise TypeError('Cannot merge trajectories, values of parameters `%s` are not '
'of the same type. Types are %s (current) and %s (other).' %
(key, str(type(my_param.f_get())),
str(type(other_param.f_get()))))
@not_in_run
[docs] def f_backup(self, **kwargs):
"""Backs up the trajectory with the given storage service.
Arguments of ``kwargs`` are directly passed to the storage service,
for the HDF5StorageService you can provide the following argument:
:param backup_filename:
Name of file where to store the backup.
In case you use the standard HDF5 storage service and `backup_filename=None`,
the file will be chosen automatically.
The backup file will be in the same folder as your hdf5 file and
named 'backup_XXXXX.hdf5' where 'XXXXX' is the name of your current trajectory.
"""
self._storage_service.store(pypetconstants.BACKUP, self, trajectory_name=self.v_name,
**kwargs)
@not_in_run
@kwargs_api_change('backup_filename', 'backup')
@kwargs_api_change('move_nodes', 'move_data')
[docs] def f_merge(self, other_trajectory, trial_parameter=None, remove_duplicates=False,
ignore_trajectory_derived_parameters=False,
ignore_trajectory_results=False,
backup=True,
move_data=False,
delete_other_trajectory=False,
keep_info=True,
keep_other_trajectory_info=True,
merge_config=True):
"""Merges another trajectory into the current trajectory.
Both trajectories must live in the same space. This means both need to have the same
parameters with similar types of values.
Note that links are also merged. There are exceptions: Links found under
a generic run group called `run_ALL` or links linking to a node under such a
group are NOT merged and simply skipped, because there is no straightforward
way to resolve the link.
:param other_trajectory: Other trajectory instance to merge into the current one.
:param trial_parameter:
If you have a particular parameter that specifies only the trial
number, i.e. an integer parameter running form 0 to T1 and
0 to T2, the parameter is modified such that after merging it will
cover the range 0 to T1+T2+1. T1 is the number of individual trials in the current
trajectory and T2 number of trials in the other trajectory.
:param remove_duplicates:
Whether you want to remove duplicate parameter points.
Requires N1 * N2 (quadratic complexity in single runs).
A ValueError is raised if no runs would be merged.
:param ignore_trajectory_derived_parameters:
Whether you want to ignore or merge derived parameters
kept under `.derived_parameters.trajectory`
:param ignore_trajectory_results:
As above but with results. If you have trajectory results
with the same name in both trajectories, the result
in the current trajectory is kept and the other one is
not merged into the current trajectory.
:param backup:
If ``True``, backs up both trajectories into files chosen automatically
by the storage services. If you want to customize your backup use
the `f_backup` function instead.
:param move_data:
Tells the storage service to move data from one trajectory to the other
instead of copying it.
If you use the HDF5 storage service and both trajectories are
stored in the same file, merging is performed fast directly within
the file. You can choose if you want to copy nodes ('move_nodes=False`)
from the other trajectory to the current one, or if you want to move them.
Accordingly, the stored data is no longer accessible in the other trajectory.
:param delete_other_trajectory:
If you want to delete the other trajectory after merging.
:param keep_info:
If `True`, information about the merge is added to the trajectory `config` tree under
`config.merge`.
:param merge_config:
Whether or not to merge all config parameters under `.config.git`,
`.config.environment`, and `.config.merge` of the other trajectory
into the current one.
:param keep_other_trajectory_info:
Whether to keep information like length, name, etc. of the other trajectory
in case you want to keep all the information. Setting of `keep_other_trajectory_info`
is irrelevant in case `keep_info=False`.
If you cannot directly merge trajectories within one HDF5 file, a slow merging process
is used. Results are loaded, stored, and emptied again one after the other. Might take
some time!
Annotations of parameters and derived parameters under `.derived_parameters.trajectory`
are NOT merged. If you wish to extract the annotations of these parameters you have to
do that manually before merging. Note that annotations of results and derived parameters
of single runs are copied, so you don't have to worry about these.
"""
# Check if trajectories can be merged
self._check_if_both_have_same_parameters(other_trajectory,
ignore_trajectory_derived_parameters)
# BACKUP if merge is possible
if backup:
other_trajectory.f_backup()
self.f_backup()
# Add meta information about the merge to the current trajectory
self._logger.info('Adding merge information')
timestamp = time.time()
formatted_time = datetime.datetime.fromtimestamp(timestamp).strftime('%Y_%m_%d_%Hh%Mm%Ss')
hexsha = hashlib.sha1(compat.tobytes(self.v_name +
str(self.v_timestamp) +
other_trajectory.v_name +
str(other_trajectory.v_timestamp) +
VERSION)).hexdigest()
short_hexsha = hexsha[0:7]
if keep_info:
merge_name = 'merge_%s_%s' % (short_hexsha, formatted_time)
config_name = 'merge.%s.timestamp' % merge_name
self.f_add_config(config_name, timestamp,
comment='Timestamp of merge')
config_name = 'merge.%s.hexsha' % merge_name
self.f_add_config(config_name, hexsha,
comment='SHA-1 identifier of the merge')
config_name = 'merge.%s.remove_duplicates' % merge_name
self.f_add_config(config_name, remove_duplicates,
comment='Option to remove duplicate entries')
config_name = 'merge.%s.ignore_trajectory_derived_parameters' % merge_name
self.f_add_config(config_name, ignore_trajectory_derived_parameters,
comment='Whether or not to ignore trajectory derived'
' parameters')
config_name = 'merge.%s.ignore_trajectory_results' % merge_name
self.f_add_config(config_name, ignore_trajectory_results,
comment='Whether or not to ignore trajectory results')
config_name = 'merge.%s.length_before_merge' % merge_name
self.f_add_config(config_name, len(self),
comment='Length of trajectory before merge')
self.config.merge.v_comment = 'Settings and information of the different merges'
if self.v_version != VERSION:
config_name = 'merge.%s.version' % merge_name
self.f_add_config(config_name, self.v_version,
comment='Pypet version if it differs from the version'
' of the trajectory')
if trial_parameter is not None:
config_name = 'merge.%s.trial_parameter' % merge_name
self.f_add_config(config_name, len(other_trajectory),
comment='Name of trial parameter')
if keep_other_trajectory_info:
if other_trajectory.v_version != self.v_version:
config_name = 'merge.%s.other_trajectory.version' % merge_name
self.f_add_config(config_name, other_trajectory.v_version,
comment='The version of pypet you used to manage the other'
' trajectory. Only added if other trajectory\'s'
' version differs from current trajectory version.')
config_name = 'merge.%s.other_trajectory.name' % merge_name
self.f_add_config(config_name, other_trajectory.v_name,
comment='Name of other trajectory merged into the current one')
config_name = 'merge.%s.other_trajectory.timestamp' % merge_name
self.f_add_config(config_name, other_trajectory.v_timestamp,
comment='Timestamp of creation of other trajectory '
'merged into the current one')
config_name = 'merge.%s.other_trajectory.length' % merge_name
self.f_add_config(config_name, len(other_trajectory),
comment='Length of other trajectory')
if other_trajectory.v_comment:
config_name = 'merge.%s.other_trajectory.comment' % merge_name
self.f_add_config(config_name, other_trajectory.v_comment,
comment='Comment of other trajectory')
# Merge parameters and keep track which runs where used and which parameters need
# to be updated
self._logger.info('Merging the parameters')
used_runs, changed_parameters = self._merge_parameters(
other_trajectory,
remove_duplicates,
trial_parameter,
ignore_trajectory_derived_parameters)
if keep_info:
config_name = 'merge.%s.merged_runs' % merge_name
self.f_add_config(config_name, int(np.sum(used_runs)),
comment='Number of merged runs')
if np.all(used_runs == 0):
raise ValueError('Your merge discards all runs of the other trajectory, maybe you '
'try to merge a trajectory with itself?')
# Dictionary containing the mappings between run names in the other trajectory
# and their new names in the current trajectory
rename_dict = {}
# Keep track of all trajectory results that should be merged and put
# information into `rename_dict`
if not ignore_trajectory_results and 'results' in other_trajectory:
self._logger.info('Merging trajectory results skeletons')
self._merge_trajectory_results(other_trajectory, rename_dict)
# Single runs are rather easy to merge, we simply keep track of the changed namings
# of the results and derived parameters in `rename_dict`
# and let the storage service do the job in the next step
self._logger.info('Merging single run skeletons')
run_name_dict = self._merge_single_runs(other_trajectory, used_runs, rename_dict)
# Now merge derived parameters which can be resolved run based
if not ignore_trajectory_derived_parameters:
self._merge_derived_parameters_run_based(other_trajectory,
run_name_dict,
rename_dict)
# The storage service needs to prepare the file for merging.
# This includes updating meta information and already storing the merged parameters
self._logger.info('Start copying results and single run derived parameters')
self._logger.info('Updating Trajectory information and changed parameters in storage')
self._storage_service.store(pypetconstants.PREPARE_MERGE, self,
trajectory_name=self.v_name,
changed_parameters=changed_parameters)
try:
# Merge the single run derived parameters and all results
# within the same hdf5 file based on `renamed_dict`
self._storage_service.store(pypetconstants.MERGE, None, trajectory_name=self.v_name,
other_trajectory_name=other_trajectory.v_name,
rename_dict=rename_dict, move_nodes=move_data,
delete_trajectory=delete_other_trajectory)
except pex.NoSuchServiceError:
# If the storage service does not support merge we end up here
self._logger.warning('My storage service does not support merging of trajectories, '
'I will use the f_load mechanism of the other trajectory and '
'store the results slowly item by item. '
'Note that thereby the other '
'trajectory will be altered (in RAM).')
self._merge_slowly(other_trajectory, rename_dict)
except ValueError as e:
# If both trajectories are stored in separate files we end up here
self._logger.warning(str(e))
self._logger.warning('Could not perfom fast merging. '
'I will use the `f_load` method of the other trajectory and '
'store the results slowly item by item. '
'Note that thereby the other '
'trajectory will be altered (in RAM).')
self._merge_slowly(other_trajectory, rename_dict)
# We will merge the git commits and other config data
if merge_config:
self._merge_config(other_trajectory)
# Finally merging links
self._merge_links(other_trajectory, run_name_dict)
# Write out the merged data to disk
self._logger.info('Writing merged data to disk')
self.f_store(store_data=pypetconstants.STORE_DATA)
self._logger.info('Finished Merging!')
def _merge_derived_parameters_run_based(self,
other_trajectory,
run_name_dict,
rename_dict):
""" Merges derived parameters that have the `run_ALL` in a name.
Creates a new parameter with the name of the first new run and links to this
parameter to avoid copying in all other runs.
"""
other_derived_parameters = \
other_trajectory._get_traj_dpars_or_results(other_trajectory, 'derived_parameters')
_, new_first_run_name = next(iter(compat.iteritems(run_name_dict)))
# get first runname
for param_name in other_derived_parameters:
if not pypetconstants.RUN_NAME_DUMMY in param_name:
continue
if param_name in self:
my_param = self.f_get(param_name, fast_access=False)
param = other_derived_parameters[param_name]
if (my_param._equal_values(my_param.f_get(), param.f_get()) and
not (my_param.f_has_range() or param.f_has_range())):
continue
first_new_param_name = param_name.replace(pypetconstants.RUN_NAME_DUMMY,
new_first_run_name)
rename_dict[param_name] = first_new_param_name
comment = param.v_comment
param_type = param.f_get_class_name()
param_type = self._create_class(param_type)
first_param = self.f_add_leaf(param_type,
first_new_param_name,
comment=comment)
for run_name in run_name_dict.values():
if run_name == new_first_run_name:
continue
next_name = param_name.replace(pypetconstants.RUN_NAME_DUMMY, run_name)
split_name = next_name.split('.')
link_name = split_name.pop()
location_name = '.'.join(split_name)
if not self.f_contains(location_name, shortcuts=False):
the_group = self.f_add_group(location_name)
else:
the_group = self.f_get(location_name)
the_group.f_add_link(link_name, first_param)
def _merge_links(self, other_trajectory, run_name_dict):
""" Merges all links"""
linked_items = other_trajectory._linked_by
if len(linked_items) > 0:
self._logger.info('Merging potential links!')
for old_linked_name in other_trajectory._linked_by:
if pypetconstants.RUN_NAME_DUMMY in old_linked_name:
self._logger.warning('Ignoring all links linking to `%s` because '
'I don`t know how to resolve links under a `%s` node.' %
(old_linked_name, pypetconstants.RUN_NAME_DUMMY))
continue
old_link_dict = other_trajectory._linked_by[old_linked_name]
old_linked_item = other_trajectory.f_get(old_linked_name)
if old_linked_item.v_run_branch in run_name_dict:
pos_list = []
for idx, split in enumerate(old_linked_name.split('.')):
if split.startswith(pypetconstants.RUN_NAME):
pos_list.append(idx)
new_runname = run_name_dict[old_linked_item.v_run_branch]
new_linked_full_name = self._rename_key(old_linked_name,
pos_list, new_runname)
else:
new_linked_full_name = old_linked_name
for linking_node, link_set in compat.itervalues(old_link_dict):
linking_full_name = linking_node.v_full_name
if pypetconstants.RUN_NAME_DUMMY in linking_full_name:
self._logger.warning('Ignoring links under `%s` because '
'I don`t know how to resolve links '
'under a `%s` node.' %
(linking_full_name, pypetconstants.RUN_NAME_DUMMY))
if linking_node.v_run_branch in run_name_dict:
pos_list = []
for idx, split in enumerate(linking_full_name.split('.')):
if split.startswith(pypetconstants.RUN_NAME):
pos_list.append(idx)
new_runname = run_name_dict[linking_node.v_run_branch]
new_linking_full_name = self._rename_key(linking_full_name,
pos_list, new_runname)
else:
new_linking_full_name = linking_full_name
for link in link_set:
if link == pypetconstants.RUN_NAME_DUMMY:
self._logger.warning('Ignoring link `%s` under `%s` because '
'I don`t know how to resolve '
'links named as `%s`.' %
(link,
linking_full_name,
pypetconstants.RUN_NAME_DUMMY))
continue
try:
new_linked_item = self.f_get(new_linked_full_name,
shortcuts=False)
if self.f_contains(new_linking_full_name):
new_linking_item = self.f_get(new_linking_full_name,
shortcuts=False)
else:
new_linking_item = self.f_add_group(new_linking_full_name)
if link in run_name_dict:
link = run_name_dict[link]
if not link in new_linking_item._links:
new_linking_item.f_add_link(link, new_linked_item)
else:
self._logger.debug('Link `%s` exists already under `%s`.' %
(link, new_linked_item.v_full_name))
except (AttributeError, ValueError) as e:
self._logger.error('Could not copy link `%s` under `%s` linking '
'to `%s` due to `%s`' %
(link, linking_full_name, old_linked_name,
str(e)))
def _merge_config(self, other_trajectory):
"""Merges meta data about previous merges, git commits, and environment settings
of the other trajectory into the current one.
"""
self._logger.info('Merging config!')
# Merge git commit meta data
if 'config.git' in other_trajectory:
self._logger.info('Merging git commits!')
git_node = other_trajectory.f_get('config.git')
param_list = []
for param in git_node.f_iter_leaves(with_links=False):
if not self.f_contains(param.v_full_name, shortcuts=False):
param_list.append(self.f_add_config(param))
if param_list:
self.f_store_items(param_list)
self._logger.info('Merging git commits successful!')
# Merge environment meta data
if 'config.environment' in other_trajectory:
self._logger.info('Merging environment config!')
env_node = other_trajectory.f_get('config.environment')
param_list = []
for param in env_node.f_iter_leaves(with_links=False):
if not self.f_contains(param.v_full_name, shortcuts=False):
param_list.append(self.f_add_config(param))
if param_list:
self.f_store_items(param_list)
self._logger.info('Merging config successful!')
# Merge meta data of previous merges
if 'config.merge' in other_trajectory:
self._logger.info('Merging merge config!')
merge_node = other_trajectory.f_get('config.merge')
param_list = []
for param in merge_node.f_iter_leaves(with_links=False):
if not self.f_contains(param.v_full_name, shortcuts=False):
param_list.append(self.f_add_config(param))
if param_list:
self.f_store_items(param_list)
self._logger.info('Merging config successful!')
def _merge_slowly(self, other_trajectory, rename_dict):
"""Merges trajectories by loading iteratively items of the other trajectory and
store it into the current trajectory.
:param rename_dict:
Dictionary containing mappings from the old result names in the `other_trajectory`
to the new names in the current trajectory.
"""
for other_key in rename_dict:
new_key = rename_dict[other_key]
other_instance = other_trajectory.f_get(other_key)
if other_instance.f_is_empty():
# To suppress warnings if nothing needs to be loaded
with self._nn_interface._disable_logger:
other_trajectory.f_load_items(other_instance)
# The empty instances for the new data have already been created before
my_instance = self.f_get(new_key)
if not my_instance.f_is_empty():
raise RuntimeError('You want to slowly merge results, but your target result '
'`%s` is not _empty, this should not happen.' %
my_instance.v_full_name)
load_dict = other_instance._store()
my_instance._load(load_dict)
my_instance.f_set_annotations(**other_instance.v_annotations.f_to_dict(copy=False))
self.f_store_item(my_instance)
# We do not want to blow up the RAM Memory
if other_instance.v_is_parameter:
other_instance.f_unlock()
my_instance.f_unlock()
other_instance.f_empty()
my_instance.f_empty()
def _merge_trajectory_results(self, other_trajectory, rename_dict):
"""Checks which results under `.results.trajectory` should be merged.
If a result is marked for merge an empty instance is created in the current trajectory.
Emits a warning if a result in the other trajectory already exists in the current one.
Stores all group nodes with annotations that are not part of the current trajectory
to disk. Results are only created empty and not stored in order to try to
conduct faster merging and copying of results in the hdf5 file.
:param rename_dict:
Dictionary that is filled with the names of results in the `other_trajectory`
as keys and the corresponding new names in the current trajectory as values.
Note for results kept under trajectory run branch there is actually no need to
change the names. So we will simply keep the original name.
"""
other_result_nodes = self._get_traj_dpars_or_results_node_iterator(other_trajectory,
'results')
to_store_groups_with_annotations = []
for node in other_result_nodes:
full_name = node.v_full_name
if node.v_is_leaf:
# If the node is a result, check if the result already exists,
# if not mark it for merge later on
if full_name in self:
self._logger.warning(
'You already have a trajectory result called `%s` in your '
'trajectory. I will not copy it.' % full_name)
else:
rename_dict[full_name] = full_name
comment = node.v_comment
result_type = node.f_get_class_name()
result_type = self._create_class(result_type)
self.f_add_result(result_type, full_name, comment=comment)
else:
# If the group is annotated and unknown to the current trajectory we
# want to copy the annotations into the current trajectory
if not full_name in self:
if not node.v_annotations.f_is_empty():
new_group = self.f_add_result_group(full_name)
annotationdict = node.v_annotations.f_to_dict()
new_group.f_set_annotations(**annotationdict)
to_store_groups_with_annotations.append(new_group)
# If we have annotated groups, store them
if len(to_store_groups_with_annotations) > 0:
self.f_store_items(to_store_groups_with_annotations)
def _merge_single_runs(self, other_trajectory, used_runs, rename_dict):
"""Checks which single run results and derived parameters should be marked for merge.
If a result or derived parameter is marked for merge
an empty instance is created in the current trajectory.
Updates the `run_information` of the current trajectory.
Stores all group nodes with annotations that are not part of the current trajectory
to disk. Results are only created empty and not stored in order to try to
conduct faster merging and copying of results in the hdf5 file.
:param used_runs:
Binary vector of length `len(other_trajectory)`,
`1.0` marks a run that is supposed to be merged, runs flagged with `0.0` are skipped.
:param rename_dict:
Dictionary that is filled with the names of groups, results and derived parameters
in the `other_trajectory` as keys and the corresponding new names
in the current trajectory as values. We DO need to rename results and derived
parameters here because the names of the single runs are changed.
For example, the result `.results.run_0000001.myresult` might be reassigned the
new full name `.results.run_00000012.myresult` when merged into the current trajectory.
:return:
Dictionary containing the old run names as keys and new run names as values
"""
count = len(self) # Variable to count the increasing new run indices and create
# new run names
run_names = other_trajectory.f_get_run_names()
run_name_dict = OrderedDict()
to_store_groups_with_annotations = []
for run_name in run_names:
# Iterate through all used runs and store annotated groups and mark results and
# derived parameters for merging
idx = other_trajectory.f_get_run_information(run_name)['idx']
if used_runs[idx]:
iter_list = []
for parent_group in compat.itervalues(other_trajectory._run_parent_groups):
if parent_group.f_contains(run_name, with_links=False):
node = parent_group.f_get(run_name)
if node.v_is_leaf:
nodes_iterator = iter([node])
else:
nodes_iterator = node.f_iter_nodes(recursive=True, with_links=False)
iter_list.append(nodes_iterator)
nodes_iterator = itools.chain(*iter_list)
# Update the run information dict of the current trajectory
other_info_dict = other_trajectory.f_get_run_information(run_name)
time_ = other_info_dict['time']
timestamp = other_info_dict['timestamp']
completed = other_info_dict['completed']
short_environment_hexsha = other_info_dict['short_environment_hexsha']
finish_timestamp = other_info_dict['finish_timestamp']
runtime = other_info_dict['runtime']
new_runname = pypetconstants.FORMATTED_RUN_NAME % count
run_name_dict[run_name] = new_runname
info_dict = dict(
idx=count,
time=time_,
timestamp=timestamp,
completed=completed,
short_environment_hexsha=short_environment_hexsha,
finish_timestamp=finish_timestamp,
runtime=runtime)
self._add_run_info(**info_dict)
count += 1
for node in nodes_iterator:
full_name = node.v_full_name
pos_list = []
for idx, split in enumerate(full_name.split('.')):
if split.startswith(pypetconstants.RUN_NAME):
pos_list.append(idx)
new_full_name = self._rename_key(full_name, pos_list, new_runname)
if node.v_is_leaf:
# Create new empty result/derived param instance
# for every result/ derived param
# in the other trajectory
# that is going to be merged into the current one
rename_dict[full_name] = new_full_name
comment = node.v_comment
leaf_type = node.f_get_class_name()
leaf_type = self._create_class(leaf_type)
if full_name.startswith('results.'):
self.f_add_result(leaf_type, new_full_name, comment=comment)
else:
self.f_add_derived_parameter(leaf_type, new_full_name,
comment=comment)
else:
if not node.v_annotations.f_is_empty() or node.v_comment != '':
# Store all group nodes that are annotated
if full_name.startswith('results.'):
new_group = self.f_add_result_group(new_full_name)
else:
new_group = self.f_add_derived_parameter_group(new_full_name)
if not node.v_annotations.f_is_empty():
annotationdict = node.v_annotations.f_to_dict()
new_group.f_set_annotations(**annotationdict)
if node.v_comment != '':
new_group.v_comment = node.v_comment
to_store_groups_with_annotations.append(new_group)
# If we have annotated groups, store them
if len(to_store_groups_with_annotations) > 0:
self.f_store_items(to_store_groups_with_annotations)
return run_name_dict
@staticmethod
def _rename_key(key, pos_list, new_name):
"""Rename positions of a key string with colon separation.
Example usage:
>>> Trajectory._rename_key('pos0.pos1.pos2', pos_list=[1], new_name='homer')
'po0.homer.pos2'
"""
split_key = key.split('.')
for pos in pos_list:
split_key[pos] = new_name
renamed_key = '.'.join(split_key)
return renamed_key
@staticmethod
def _get_traj_dpars_or_results(traj, where):
"""Extracts all parameters or results that are not below any group called run_XXXXXXXX"""
result_dict = {}
if where not in ['results', 'derived_parameters']:
raise RuntimeError('You shall not pass!')
if traj.f_contains(where):
group = traj[where]
for leaf in group.f_iter_leaves(with_links=False):
if (leaf.v_run_branch == 'trajectory' or
leaf.v_run_branch == pypetconstants.RUN_NAME_DUMMY):
result_dict[leaf.v_full_name] = leaf
return result_dict
@staticmethod
def _get_traj_dpars_or_results_node_iterator(traj, where):
"""Returns an iterator over all nodes not hanging below `run_XXXXXXXX`"""
if where not in ['results', 'derived_parameters']:
raise RuntimeError('You shall not pass!')
if traj.f_contains(where):
group = traj[where]
predicate = lambda x: (x.v_run_branch == 'trajectory' or
x.v_run_branch == pypetconstants.RUN_NAME_DUMMY)
return filter(predicate, group.f_iter_nodes(recursive=True))
else:
return iter([])
def _merge_parameters(self, other_trajectory, remove_duplicates=False,
trial_parameter_name=None,
ignore_trajectory_derived_parameters=False):
"""Merges parameters from the other trajectory into the current one.
The explored parameters in the current trajectory are directly enlarged (in RAM),
no storage service is needed here. Later on in `f_merge` the storage service
will be requested to store the enlarge parameters to disk.
Note explored parameters are always enlarged. Unexplored parameters might become
new explored parameters if they differ in their default values
in the current and the other trajectory, respectively.
:return: A tuple with two elements:
1.
Binary vector of length `len(other_trajectory)`,
`1.0` marks a run that is supposed to be merged, runs flagged with `0.0`
are skipped.
2.
List of names of parameters that were altered.
"""
if trial_parameter_name:
if remove_duplicates:
self._logger.warning('You have given a trial parameter and you want to '
'remove_items duplicates. There cannot be any duplicates '
'when adding trials, I will not look for duplicates.')
remove_duplicates = False
# Dictionary containing full parameter names as keys
# and pairs of parameters from both trajectories as values.
# Parameters kept in this dictionary are marked for merging and will be enlarged
# with ranges and values of corresponding parameters in the other trajectory
params_to_change = {}
if trial_parameter_name:
# We want to merge a trial parameter
# First make some sanity checks
my_trial_parameter = self.f_get(trial_parameter_name)
other_trial_parameter = other_trajectory.f_get(trial_parameter_name)
if not isinstance(my_trial_parameter, BaseParameter):
raise TypeError('Your trial_parameter `%s` does not evaluate to a real parameter'
' in the trajectory' % trial_parameter_name)
# Extract the ranges of both trial parameters
if my_trial_parameter.f_has_range():
my_trial_list = my_trial_parameter.f_get_range()
else:
# If we only have a single trial, we need to make a range of length 1
# This is probably a very exceptional case
my_trial_list = [my_trial_parameter.f_get()]
if other_trial_parameter.f_has_range():
other_trial_list = other_trial_parameter.f_get_range()
else:
other_trial_list = [other_trial_parameter.f_get()]
# Make sanity checks if both ranges contain all numbers from 0 to T1
# for the current trajectory and 0 to T2 for the other trajectory
mytrialset = set(my_trial_list)
mymaxtrial_T1 = max(mytrialset) # maximum trial index in current trajectory aka T1
if mytrialset != set(range(mymaxtrial_T1 + 1)):
raise TypeError('In order to specify a trial parameter, this parameter must '
'contain integers from 0 to %d, but it in fact it '
'contains `%s`.' % (mymaxtrial_T1, str(mytrialset)))
othertrialset = set(other_trial_list)
othermaxtrial_T2 = max(othertrialset) # maximum trial index in other trajectory aka T2
if othertrialset != set(range(othermaxtrial_T2 + 1)):
raise TypeError('In order to specify a trial parameter, this parameter must '
'contain integers from 0 to %d, but it infact it contains `%s` '
'in the other trajectory.' %
(othermaxtrial_T2, str(othertrialset)))
# If the trial parameter's name was just given in parts we update it here
# to the full name
trial_parameter_name = my_trial_parameter.v_full_name
# If we had the very exceptional case, that our trial parameter was not explored,
# aka we only had 1 trial, we have to add it to the explored parameters
if not trial_parameter_name in self._explored_parameters:
self._explored_parameters[trial_parameter_name] = my_trial_parameter
# We need to mark the trial parameter for merging
params_to_change[trial_parameter_name] = (my_trial_parameter, other_trial_parameter)
# Dictionary containing all parameters of the other trajectory, we will iterate through it
# to spot parameters that need to be enlarge or become new explored parameters
params_to_merge = other_trajectory._parameters.copy()
if not ignore_trajectory_derived_parameters:
trajectory_derived_parameters = \
self._get_traj_dpars_or_results(self, 'derived_parameters')
params_to_merge.update(trajectory_derived_parameters)
# Iterate through all parameters of the other trajectory
# and check which differ from the parameters of the current trajectory
for key in params_to_merge:
other_param = params_to_merge[key]
if pypetconstants.RUN_NAME in key:
continue
my_param = self.f_get(key)
if not my_param._values_of_same_type(my_param.f_get(), other_param.f_get()):
raise TypeError('The parameters with name `%s` are not of the same type, cannot '
'merge trajectory.' % key)
# We have taken care about the trial parameter before, it is already
# marked for merging
if my_param.v_full_name == trial_parameter_name:
continue
# If a parameter was explored in one of the trajectories or two unexplored
# parameters differ, we need to mark them for merge
if (my_param.f_has_range() or
other_param.f_has_range() or
not my_param._equal_values(my_param.f_get(), other_param.f_get())):
# If two unexplored parameters differ, that means they differ in every run,
# accordingly we do not need to check for duplicate runs anymore
params_to_change[key] = (my_param, other_param)
if not my_param.f_has_range() and not other_param.f_has_range():
remove_duplicates = False
# Check if we use all runs or remove duplicates:
use_runs = np.ones(len(other_trajectory))
if remove_duplicates:
# We need to compare all parameter combinations in the current trajectory
# to all parameter combinations in the other trajectory to spot duplicate points.
# Quadratic Complexity!
for irun in compat.xrange(len(other_trajectory)):
for jrun in compat.xrange(len(self)):
change = True
# Check all marked parameters
for my_param, other_param in compat.itervalues(params_to_change):
if other_param.f_has_range():
other_param._set_parameter_access(irun)
if my_param.f_has_range():
my_param._set_parameter_access(jrun)
val1 = my_param.f_get()
val2 = other_param.f_get()
# If only one parameter differs, the parameter space point differs
# and we can skip the rest of the parameters
if not my_param._equal_values(val1, val2):
change = False
break
# If we found one parameter space point in the current trajectory
# that matches the ith point in the other, we do not need the ith
# point. We can also skip comparing to the rest of the points in the
# current trajectory
if change:
use_runs[irun] = 0.0
break
# Restore changed default values
for my_param, other_param in compat.itervalues(params_to_change):
other_param._restore_default()
my_param._restore_default()
# Merge parameters into the current trajectory
adding_length = int(sum(use_runs))
if adding_length == 0:
return use_runs, []
for my_param, other_param in compat.itervalues(params_to_change):
fullname = my_param.v_full_name
# We need new ranges to enlarge all parameters marked for merging
if fullname == trial_parameter_name:
# The trial parameter now has to cover the range 0 to T1+T2+1
other_range = [x + mymaxtrial_T1 + 1 for x in other_trial_list]
else:
# In case we do not use all runs we need to filter the ranges of the
# parameters of the other trajectory
if other_param.f_has_range():
other_range = (x for run, x in zip(use_runs, other_param.f_get_range())
if run)
else:
other_range = (other_param.f_get() for _ in compat.xrange(adding_length))
# If a parameter in the current trajectory was marked for merging but was not
# explored before, we need to explore it first, simply by creating the range of
# the current trajectory's length containing only it's default value
if not my_param.f_has_range():
my_param.f_unlock()
my_param._explore((my_param.f_get() for _ in compat.xrange(len(self))))
# After determining the new range extension `other_range`,
# expand the parameters
my_param.f_unlock()
my_param._expand(other_range)
if not fullname in self._explored_parameters:
self._explored_parameters[fullname] = my_param
return use_runs, compat.listkeys(params_to_change)
@not_in_run
[docs] def f_migrate(self, new_name=None, in_store=False,
new_storage_service=None, **kwargs):
"""Can be called to rename and relocate the trajectory.
:param new_name: New name of the trajectory, None if you do not want to change the name.
:param in_store:
Set this to True if the trajectory has been stored with the new name at the new
file before and you just want to "switch back" to the location.
If you migrate to a store used before and you do not set `in_store=True`,
the storage service will throw a RuntimeError in case you store the Trajectory
because it will assume that you try to store a new trajectory that accidentally has
the very same name as another trajectory. If set to `True` and trajectory is not found
in the file, the trajectory is simply stored to the file.
:param new_storage_service:
New service where you want to migrate to. Leave none if you want to keep the olde one.
:param kwargs:
Additional keyword arguments passed to the service.
For instance, to change the file of the trajectory use ``filename='my_new_file.hdf5``.
"""
if new_name is not None:
self._name = new_name
unused_kwargs = set(kwargs.keys())
if new_storage_service is not None or len(kwargs) > 0:
self._storage_service, unused_kwargs = storage_factory(
storage_service=new_storage_service,
trajectory=self, **kwargs)
if len(unused_kwargs) > 0:
raise ValueError('The following keyword arguments were not used: `%s`' %
str(unused_kwargs))
self._stored = in_store
@kwargs_api_change('new_filename')
@kwargs_api_change('new_name')
[docs] def f_store(self, only_init=False, store_data=pypetconstants.STORE_DATA,
store_full_in_run=False):
""" Stores the trajectory to disk and recursively all data in the tree.
:param only_init
If you just want to initialise the store. If yes, only meta information about
the trajectory is stored and none of the groups/leaves within the trajectory.
:param store_data:
Only considered if ``only_init=False``. Choose of the following:
* :const:`pypet.pypetconstants.STORE_NOTHING`: (0)
Nothing is store.
* :const:`pypet.pypetconstants.STORE_DATA_SKIPPING`: (1)
Speedy version of normal ``STORE_DATA`` will entirely skip groups
(but not their children) and leaves if they have been stored before.
No new data is added in this case.
* :const:`pypet.pypetconstants.STORE_DATA`: (2)
Stores every group and leave node. If they contain data that is not yet stored
to disk it is added.
* :const:`pypet.pypetconstants.OVERWRITE_DATA`: (3)
Stores all groups and leave nodes and will delete all data on disk
and overwrite it with the current data in RAM.
**NOT RECOMMENDED**! Overwriting data on disk fragments the HDF5 file and
yields badly compressed large files. Better stick to the concept
write once and read many!
If you use the HDF5 Storage Service usually (STORE_DATA (2)) only novel data
is stored to disk.
If you have results that have been stored to disk before only new data items are added and
already present data is NOT overwritten.
Overwriting (OVERWRITE_DATA (3)) existing data with the HDF5 storage service
is not recommended due to fragmentation of the HDF5 file. Better stick to the concept
write once, but read often.
If you want to store individual parameters or results, you might want to
take a look at :func:`~pypet.Trajectory.f_store_items`.
To store whole subtrees of your trajectory check out
:func:`~pypet.naturalnaming.NNGroupNode.f_store_child`.
Note both functions require that your trajectory was stored to disk with `f_store`
at least once before.
**ATTENTION**: Calling `f_store` during a single run the behavior is different.
To avoid re-storing the full trajectory in every single run, which is redundant,
only sub-trees of the trajectory are really stored.
The storage serivce looks for new data that is added below groups called `run_XXXXXXXXXX`
and stores it where `XXXXXXXXX` is the index of this run. The `only_init` parameter is
ignored in this case. You can avoid this behavior by using the argument from below.
:param store_full_in_run:
Only useful during single runs. If ``store_full_in_run=True`` the full tree
is traversed instead of just items below groups called `run_XXXXXXXXXX`.
Accordingly, storing is analogous to storing a trajectory not during a single run.
"""
if self._is_run:
self._storage_service.store(pypetconstants.SINGLE_RUN, self,
trajectory_name=self.v_name,
store_final=False,
store_data=store_data,
store_full_in_run=store_full_in_run)
else:
if self._stored and self._expansion_not_stored:
self._store_expansion()
self._expansion_not_stored = False
self._storage_service.store(pypetconstants.TRAJECTORY, self,
trajectory_name=self.v_name,
only_init=only_init,
store_data=store_data)
self._stored = True # We do this in case the storage service forgot to tell the
# Trajectory that it was actually stored
@not_in_run
[docs] def f_is_empty(self):
""" Whether no results nor parameters have been added yet to the trajectory
(ignores config)."""
return (len(self._parameters) == 0 and
len(self._derived_parameters) == 0 and
len(self._results) == 0 and
len(self._other_leaves) == 0)
@not_in_run
[docs] def f_restore_default(self):
""" Restores the default value in all explored parameters and sets the
v_idx property back to -1 and v_crun to None."""
self._idx = -1
self._crun = None
for param in compat.itervalues(self._explored_parameters):
param._restore_default()
def _set_explored_parameters_to_idx(self, idx):
""" Notifies the explored parameters what current point in the parameter space
they should represent.
"""
for param in compat.itervalues(self._explored_parameters):
param._set_parameter_access(idx)
def _make_single_run(self, idx):
""" Modifies the trajectory for single runs executed by the environment """
self._is_run = False # to be able to use f_set_crun
self.f_set_crun(idx)
self._is_run = True
self._set_start_time()
return self
[docs] def f_get_run_names(self, sort=True):
""" Returns a list of run names.
ONLY useful for a single run during multiprocessing if ``v_full_copy` was set to ``True``.
Otherwise only the current run is available.
:param sort:
Whether to get them sorted, will only require O(N) [and not O(N*log N)] since we
use (sort of) bucket sort.
"""
if sort:
return [self.f_idx_to_run(idx) for idx in compat.xrange(len(self))]
else:
return compat.listkeys(self._run_information)
[docs] def f_find_idx(self, name_list, predicate):
""" Finds a single run index given a particular condition on parameters.
ONLY useful for a single run if ``v_full_copy` was set to ``True``.
Otherwise a TypeError is thrown.
:param name_list:
A list of parameter names the predicate applies to, if you have only a single
parameter name you can omit the list brackets.
:param predicate:
A lambda predicate for filtering that evaluates to either ``True`` or ``False``
:return: A generator yielding the matching single run indices
Example:
>>> predicate = lambda param1, param2: param1==4 and param2 in [1.0, 2.0]
>>> iterator = traj.f_find_idx(['groupA.param1', 'groupA.param2'], predicate)
>>> [x for x in iterator]
[0, 2, 17, 36]
"""
if self._is_run and not self.v_full_copy:
raise TypeError('You cannot use this function during a multiprocessing signle run and '
'not having ``v_full_copy=True``.')
if isinstance(name_list, compat.base_type):
name_list = [name_list]
# First create a list of iterators, each over the range of the matched parameters
iter_list = []
for name in name_list:
param = self.f_get(name)
if not param.v_is_parameter:
raise TypeError('`%s` is not a parameter it is a %s, find idx is not applicable' %
(name, str(type(param))))
if param.f_has_range():
iter_list.append(iter(param.f_get_range()))
else:
iter_list.append(itools.repeat(param.f_get(), len(self)))
# Create a logical iterator returning `True` or `False`
# whether the user's predicate matches the parameter data
logic_iter = map(predicate, *iter_list)
# Now the run indices are the the indices where `logic_iter` evaluates to `True`
for idx, item in enumerate(logic_iter):
if item:
yield idx
[docs] def f_idx_to_run(self, name_or_idx):
"""Converts an integer idx to the corresponding single run name and vice versa.
Note during a single run ONLY useful if ``v_full_copy`` was set to True.
:param name_or_idx: Name of a single run or an integer index
:return: The corresponding idx or name of the single run
Example usage:
>>> traj.f_idx_to_run(4)
'run_00000004'
>>> traj.f_idx_to_run('run_00000000')
0
"""
return self._single_run_ids[name_or_idx]
def _set_start_time(self):
""" Sets the start timestamp and formatted time to the current time. """
init_time = time.time()
formatted_time = datetime.datetime.fromtimestamp(init_time).strftime('%Y_%m_%d_%Hh%Mm%Ss')
self._timestamp_run = init_time
self._time_run = formatted_time
def _set_finish_time(self):
""" Sets the finish time and computes the runtime in human readable format """
self._finish_timestamp_run = time.time()
findatetime = datetime.datetime.fromtimestamp(self._finish_timestamp_run)
startdatetime = datetime.datetime.fromtimestamp(self._timestamp_run)
self._runtime_run = str(findatetime - startdatetime)
def _construct_instance(self, constructor, full_name, *args, **kwargs):
""" Creates a new node. Checks if the new node needs to know the trajectory.
:param constructor: The constructor to use
:param full_name: Full name of node
:param args: Arguments passed to constructor
:param kwargs: Keyword arguments passed to the constructor
:return:
"""
if getattr(constructor, 'KNOWS_TRAJECTORY', False):
return constructor(full_name, self, *args, **kwargs)
else:
return constructor(full_name, *args, **kwargs)
def _create_class(self, class_name):
"""Dynamically creates a class.
It is tried if the class can be created by the already given imports.
If not the list of the dynamically loaded classes is used.
"""
try:
new_class = eval(class_name)
if not inspect.isclass(new_class):
raise TypeError('Not a class!')
return new_class
except (NameError, TypeError):
for dynamic_class in self._dynamic_imports:
# Dynamic classes can be provided directly as a Class instance,
# for example as `MyCustomParameter`,
# or as a string describing where to import the class from,
# for instance as `'mypackage.mymodule.MyCustomParameter'`.
if inspect.isclass(dynamic_class):
if class_name == dynamic_class.__name__:
return dynamic_class
else:
# The class name is always the last in an import string,
# e.g. `'mypackage.mymodule.MyCustomParameter'`
class_name_to_test = dynamic_class.split('.')[-1]
if class_name == class_name_to_test:
new_class = self._load_class(dynamic_class)
return new_class
raise ImportError('Could not create the class named `%s`.' % class_name)
@staticmethod
def _load_class(full_class_string):
"""Loads a class from a string naming the module and class name.
For example:
>>> Trajectory._load_class(full_class_string = 'pypet.brian.parameter.BrianParameter')
<BrianParameter>
"""
class_data = full_class_string.split(".")
module_path = ".".join(class_data[:-1])
class_str = class_data[-1]
module = importlib.import_module(module_path)
# We retrieve the Class from the module
return getattr(module, class_str)
@property
def v_shortcuts(self):
"""Whether shortcuts are allowed if accessing data via natural naming or
squared bracket indexing."""
return self._shortcuts
@v_shortcuts.setter
[docs] def v_shortcuts(self, shortcuts):
self._shortcuts = bool(shortcuts)
@property
@deprecated('No longer supported, please don`t use it anymore.')
def v_backwards_search(self):
return False
@property
def v_max_depth(self):
"""The maximum depth the tree should be searched if shortcuts are allowed.
Set to `None` if there should be no depth limit.
"""
return self._max_depth
@v_max_depth.setter
[docs] def v_max_depth(self, max_depth):
self._max_depth = max_depth
@property
def v_iter_recursive(self):
"""Whether using `__iter__` should iterate only immediate children or
recursively all nodes."""
return self._iter_recursive
@v_iter_recursive.setter
[docs] def v_iter_recursive(self, iter_recursive):
self._iter_recursive = bool(iter_recursive)
@property
def v_auto_load(self):
"""Whether the trajectory should attempt to load data on the fly.
"""
return self._auto_load
@v_auto_load.setter
[docs] def v_auto_load(self, auto_load):
self._auto_load = bool(auto_load)
@property
[docs] def v_timestamp(self):
"""Float timestamp of creation time"""
return self._timestamp
@property
[docs] def v_time(self):
"""Formatted time string of the time the trajectory or run was created."""
return self._time
@property
def v_standard_parameter(self):
""" The standard parameter used for parameter creation"""
return self._standard_parameter
@v_standard_parameter.setter
[docs] def v_standard_parameter(self, parameter):
"""Sets the standard parameter"""
self._standard_parameter = parameter
@property
def v_standard_result(self):
"""The standard result class used for result creation """
return self._standard_result
@v_standard_result.setter
[docs] def v_standard_result(self, result):
"""Sets standard result"""
self._standard_result = result
@property
def v_standard_leaf(self):
"""The standard constructor used if you add a generic leaf.
The constructor is only used if you do not add items under the usual four subtrees
(`parameters`, `derived_parameters`, `config`, `results`).
"""
return self._standard_leaf
@v_standard_leaf.setter
[docs] def v_standard_leaf(self, leaf):
"""Sets standard result"""
self._standard_leaf = leaf
@property
def v_fast_access(self):
"""Whether parameter instances (False) or their values (True) are returned via natural
naming.
Works also for results if they contain a single item with the name of the result.
Default is True.
"""
return self._fast_access
@v_fast_access.setter
[docs] def v_fast_access(self, value):
"""Sets fast access"""
self._fast_access = bool(value)
# @property
# def v_fast_adding(self):
# """If you can add items to the trajectory simply by calling ``setattr``
#
# For example, ``traj.par.a = 4`` would add a parameter named `a` with the value 4.
#
# """
# return self._fast_adding
#
# @v_fast_adding.setter
# def v_fast_adding(self, value):
# """Sets fast adding"""
# self._fast_adding = bool(value)
@property
[docs] def v_environment_hexsha(self):
"""If the trajectory is used with an environment this returns
the SHA-1 code of the environment.
"""
return self._environment_hexsha
@property
[docs] def v_environment_name(self):
"""If the trajectory is used with an environment this returns
the name of the environment.
"""
return self._environment_name
@staticmethod
def _return_item_dictionary(param_dict, fast_access, copy):
"""Returns a dictionary containing either all parameters, all explored parameters,
all config, all derived parameters, or all results.
:param param_dict: The dictionary which is about to be returned
:param fast_access: Whether to use fast access
:param copy: If the original dict should be returned or a shallow copy
:return: The dictionary
:raises: ValueError if `copy=False` and fast_access=True`
"""
if not copy and fast_access:
raise ValueError('You cannot access the original dictionary and use fast access at the'
' same time!')
if not fast_access:
if copy:
return param_dict.copy()
else:
return param_dict
else:
resdict = {}
for key in param_dict:
param = param_dict[key]
val = param.f_get()
resdict[key] = val
return resdict
@property
@deprecated(msg='Please use `v_name`.')
[docs] def v_trajectory_name(self):
"""Name of the (parent) trajectory"""
return self.v_name
@property
@deprecated(msg='Please use `v_time`.')
[docs] def v_trajectory_time(self):
"""Time (parent) trajectory was created"""
return self.v_time
@property
@deprecated(msg='Please use `v_timestamp`.')
[docs] def v_trajectory_timestamp(self):
"""Float timestamp when (parent) trajectory was created"""
return self.v_timestamp
def _finalize_run(self):
"""Called by the environment after storing to perform some rollback operations.
All results and derived parameters created in the current run are removed.
Important for single processing to not blow up the parent trajectory with the results
of all runs.
"""
self._run_information[self.v_crun]['completed'] = 1
_keep_linked_lambda = lambda node: _keep_linked(self, node)
for group_name in self._run_parent_groups:
group = self._run_parent_groups[group_name]
if group.f_contains(self.v_crun):
group.f_remove_child(self.v_crun, recursive=True, predicate=_keep_linked_lambda)
[docs] def f_to_dict(self, fast_access=False, short_names=False, copy=True,
with_links=True):
"""Returns a dictionary with pairings of (full) names as keys and instances/values.
:param fast_access:
If True, parameter values are returned instead of the instances.
Works also for results if they contain a single item with the name of the result.
:param short_names:
If true, keys are not full names but only the names. Raises a ValueError
if the names are not unique.
:param copy:
If `fast_access=False` and `short_names=False` you can access the original
data dictionary if you set `copy=False`. If you do that, please do not
modify anything! Raises ValueError if `copy=False` and `fast_access=True`
or `short_names=True`.
:param with_links:
If links should be ignored
:return: dictionary
:raises: ValueError
"""
return self._nn_interface._to_dict(self, fast_access=fast_access,
short_names=short_names,
copy=copy, with_links=with_links)
[docs] def f_get_config(self, fast_access=False, copy=True):
"""Returns a dictionary containing the full config names as keys and the config parameters
or the config parameter data items as values.
:param fast_access:
Determines whether the parameter objects or their values are returned
in the dictionary.
:param copy:
Whether the original dictionary or a shallow copy is returned.
If you want the real dictionary please do not modify it at all!
Not Copying and fast access do not work at the same time! Raises ValueError
if fast access is true and copy false.
:return: Dictionary containing the config data
:raises: ValueError
"""
return self._return_item_dictionary(self._config, fast_access, copy)
[docs] def f_get_parameters(self, fast_access=False, copy=True):
""" Returns a dictionary containing the full parameter names as keys and the parameters
or the parameter data items as values.
:param fast_access:
Determines whether the parameter objects or their values are returned
in the dictionary.
:param copy:
Whether the original dictionary or a shallow copy is returned.
If you want the real dictionary please do not modify it at all!
Not Copying and fast access do not work at the same time! Raises ValueError
if fast access is true and copy false.
:return: Dictionary containing the parameters.
:raises: ValueError
"""
return self._return_item_dictionary(self._parameters, fast_access, copy)
[docs] def f_get_explored_parameters(self, fast_access=False, copy=True):
""" Returns a dictionary containing the full parameter names as keys and the parameters
or the parameter data items as values.
:param fast_access:
Determines whether the parameter objects or their values are returned
in the dictionary.
:param copy:
Whether the original dictionary or a shallow copy is returned.
If you want the real dictionary please do not modify it at all!
Not Copying and fast access do not work at the same time! Raises ValueError
if fast access is true and copy false.
:return: Dictionary containing the parameters.
:raises: ValueError
"""
return self._return_item_dictionary(self._explored_parameters, fast_access, copy)
[docs] def f_get_derived_parameters(self, fast_access=False, copy=True):
""" Returns a dictionary containing the full parameter names as keys and the parameters
or the parameter data items as values.
:param fast_access:
Determines whether the parameter objects or their values are returned
in the dictionary.
:param copy:
Whether the original dictionary or a shallow copy is returned.
If you want the real dictionary please do not modify it at all!
Not Copying and fast access do not work at the same time! Raises ValueError
if fast access is true and copy false.
:return: Dictionary containing the parameters.
:raises: ValueError
"""
return self._return_item_dictionary(self._derived_parameters, fast_access, copy)
[docs] def f_get_results(self, fast_access=False, copy=True):
""" Returns a dictionary containing the full result names as keys and the corresponding
result objects or result data items as values.
:param fast_access:
Determines whether the result objects or their values are returned
in the dictionary. Works only for results if they contain a single item with
the name of the result.
:param copy:
Whether the original dictionary or a shallow copy is returned.
If you want the real dictionary please do not modify it at all!
Not Copying and fast access do not work at the same time! Raises ValueError
if fast access is true and copy false.
:return: Dictionary containing the results.
:raises: ValueError
"""
return self._return_item_dictionary(self._results, fast_access, copy)
def _store_final(self, do_store_data=False):
"""Signals the storage service that single run is completed
:param do_store_data: If all data should be stored as in `f_store`.
"""
if do_store_data:
store_data = pypetconstants.STORE_DATA
else:
store_data = pypetconstants.STORE_NOTHING
self._storage_service.store(pypetconstants.SINGLE_RUN, self,
trajectory_name=self.v_name,
store_final=True,
store_data=store_data,
store_full_in_run=False)
[docs] def f_store_item(self, item, *args, **kwargs):
"""Stores a single item, see also :func:`~pypet.trajectory.Trajectory.f_store_items`."""
self.f_store_items([item], *args, **kwargs)
[docs] def f_store_items(self, iterator, *args, **kwargs):
"""Stores individual items to disk.
This function is useful if you calculated very large results (or large derived parameters)
during runtime and you want to write these to disk immediately and empty them afterwards
to free some memory.
Instead of storing individual parameters or results you can also store whole subtrees with
:func:`~pypet.naturalnaming.NNGroupNode.f_store_child`.
You can pass the following arguments to `f_store_items`:
:param iterator:
An iterable containing the parameters or results to store, either their
names or the instances. You can also pass group instances or names here
to store the annotations of the groups.
:param non_empties:
Optional keyword argument (boolean),
if `True` will only store the subset of provided items that are not empty.
Empty parameters or results found in `iterator` are simply ignored.
:param args: Additional arguments passed to the storage service
:param kwargs:
If you use the standard hdf5 storage service, you can pass the following additional
keyword argument:
:param overwrite:
List names of parts of your item that should
be erased and overwritten by the new data in your leaf.
You can also set `overwrite=True`
to overwrite all parts.
For instance:
>>> traj.f_add_result('mygroup.myresult', partA=42, partB=44, partC=46)
>>> traj.f_store()
>>> traj.mygroup.myresult.partA = 333
>>> traj.mygroup.myresult.partB = 'I am going to change to a string'
>>> traj.f_store_item('mygroup.myresult', overwrite=['partA', 'partB'])
Will store `'mygroup.myresult'` to disk again and overwrite the parts
`'partA'` and `'partB'` with the new values `333` and
`'I am going to change to a string'`.
The data stored as `partC` is not changed.
Be aware that you need to specify the names of parts as they were stored
to HDF5. Depending on how your leaf construction works, this may differ
from the names the data might have in your leaf in the trajectory container.
Note that massive overwriting will fragment and blow up your HDF5 file.
Try to avoid changing data on disk whenever you can.
:raises:
TypeError:
If the (parent) trajectory has never been stored to disk. In this case
use :func:`pypet.trajectory.f_store` first.
ValueError: If no item could be found to be stored.
Note if you use the standard hdf5 storage service, there are no additional arguments
or keyword arguments to pass!
"""
if not self._stored:
raise TypeError('Cannot store stuff for a trajectory that has never been '
'stored to disk. Please call traj.f_store(only_init=True) first.')
fetched_items = self._nn_interface._fetch_items(STORE, iterator, args, kwargs)
if fetched_items:
self._storage_service.store(pypetconstants.LIST, fetched_items,
trajectory_name=self.v_name)
else:
raise ValueError('Your storage was not successful, could not find a single item '
'to store.')
[docs] def f_load_item(self, item, *args, **kwargs):
"""Loads a single item, see also :func:`~pypet.trajectory.Trajectory.f_load_items`"""
self.f_load_items([item], *args, **kwargs)
[docs] def f_load_items(self, iterator, *args, **kwargs):
"""Loads parameters and results specified in `iterator`.
You can directly list the Parameter objects or just their names.
If names are given the `~pypet.naturalnaming.NNGroupNode.f_get` method is applied to find the
parameters or results in the trajectory. Accordingly, the parameters and results
you want to load must already exist in your trajectory (in RAM), probably they
are just empty skeletons waiting desperately to handle data.
If they do not exist in RAM yet, but have been stored to disk before,
you can call :func:`~pypet.trajectory.Trajectory.f_update_skeleton` in order to
bring your trajectory tree skeleton up to date. In case of a single run you can
use the :func:`~pypet.naturalnaming.NNGroupNode.f_load_child` method to recursively
load a subtree without any data.
Then you can load the data of individual results or parameters one by one.
If want to load the whole trajectory at once or ALL results and parameters that are
still empty take a look at :func:`~pypet.trajectory.Trajectory.f_load`.
As mentioned before, to load subtrees of your trajectory you might want to check out
:func:`~pypet.naturalnaming.NNGroupNode.f_load_child`.
To load a list of parameters or results with `f_load_items` you can pass
the following arguments:
:param iterator: A list with parameters or results to be loaded.
:param only_empties:
Optional keyword argument (boolean),
if `True` only empty parameters or results are passed
to the storage service to get loaded. Non-empty parameters or results found in
`iterator` are simply ignored.
:param args: Additional arguments directly passed to the storage service
:param kwargs:
Additional keyword arguments directly passed to the storage service
(except the kwarg `only_empties`)
If you use the standard hdf5 storage service, you can pass the following additional
keyword arguments:
:param load_only:
If you load a result, you can partially load it and ignore the rest of data items.
Just specify the name of the data you want to load. You can also provide a list,
for example `load_only='spikes'`, `load_only=['spikes','membrane_potential']`.
Be aware that you need to specify the names of parts as they were stored
to HDF5. Depending on how your leaf construction works, this may differ
from the names the data might have in your leaf in the trajectory container.
A warning is issued if data specified in `load_only` cannot be found in the
instances specified in `iterator`.
:param load_except:
Analogous to the above, but everything is loaded except names or parts
specified in `load_except`.
You cannot use `load_only` and `load_except` at the same time. If you do
a ValueError is thrown.
A warning is issued if names listed in `load_except` are not part of the
items to load.
"""
if not self._stored:
raise TypeError(
'Cannot load stuff from disk for a trajectory that has never been stored.')
fetched_items = self._nn_interface._fetch_items(LOAD, iterator, args, kwargs)
if fetched_items:
self._storage_service.load(pypetconstants.LIST, fetched_items,
trajectory_name=self.v_name)
else:
self._logger.warning('Your loading was not successful, could not find a single item '
'to load.')
@kwargs_api_change('remove_empty_groups')
[docs] def f_remove_item(self, item, recursive=False):
"""Removes a single item, see :func:`~pypet.trajectory.Trajectory.f_remove_items`"""
self.f_remove_items([item], recursive=recursive)
@kwargs_api_change('remove_empty_groups')
[docs] def f_remove_items(self, iterator, recursive=False):
"""Removes parameters, results or groups from the trajectory.
This function ONLY removes items from your current trajectory and does not delete
data stored to disk. If you want to delete data from disk, take a look at
:func:`~pypet.trajectory.Trajectory.f_delete_items`.
This will also remove all links if items are linked.
:param iterator:
A sequence of items you want to remove. Either the instances themselves
or strings with the names of the items.
:param recursive:
In case you want to remove group nodes, if the children should be removed, too.
"""
# Will format the request in a form that is understood by the storage service
# aka (msg, item, args, kwargs)
fetched_items = self._nn_interface._fetch_items(REMOVE, iterator, (), {})
if fetched_items:
for _, item, dummy1, dummy2 in fetched_items:
self._nn_interface._remove_node_or_leaf(item, recursive=recursive)
else:
self._logger.warning('Your removal was not successful, could not find a single '
'item to remove.')
[docs] def f_delete_link(self, link, remove_from_trajectory=False):
"""Deletes a single link see :func:`~pypet.trajectory.Trajectory.f_delete_links`"""
self.f_delete_links((link,), remove_from_trajectory)
[docs] def f_delete_links(self, iterator_of_links, remove_from_trajectory=False):
"""Deletes several links from the hard disk.
Links can be passed as a string ``'groupA.groupB.linkA'``
or as a tuple containing the node from which the link should be removed and the
name of the link ``(groupWithLink, 'linkA')``.
"""
to_delete_links = []
group_link_pairs = []
for elem in iterator_of_links:
if isinstance(elem, compat.base_type):
split_names = elem.split('.')
parent_name = '.'.join(split_names[:-1])
link = split_names[-1]
parent_node = self.f_get(parent_name) if parent_name != '' else self
link_name = parent_node.v_full_name + '.' + link if parent_name != '' else link
to_delete_links.append((pypetconstants.DELETE_LINK, link_name))
group_link_pairs.append((parent_node, link))
else:
link_name = elem[0].v_full_name + '.' + elem[1]
to_delete_links.append((pypetconstants.DELETE_LINK, link_name))
group_link_pairs.append(elem)
try:
self._storage_service.store(pypetconstants.LIST, to_delete_links,
trajectory_name=self.v_name)
except:
self._logger.error('Could not remove `%s` from the trajectory. Maybe the'
' item(s) was/were never stored to disk.' % str(to_delete_links))
raise
if remove_from_trajectory:
for group, link in group_link_pairs:
group.f_remove_link(link)
[docs] def f_remove(self, recursive=True, predicate=None):
"""Recursively removes all children of the trajectory
:param recursive:
Only here for consistency with signature of parent method. Cannot be set
to `False` because the trajectory root node cannot be removed.
:param predicate:
Predicate which can evaluate for each node to ``True`` in order to remove the node or
``False`` if the node should be kept. Leave ``None`` if you want to remove all nodes.
"""
if not recursive:
raise ValueError('Nice try ;-)')
for child in compat.listkeys(self._children):
self.f_remove_child(child, recursive=True, predicate=predicate)
@kwargs_api_change('remove_empty_groups')
[docs] def f_delete_item(self, item, *args, **kwargs):
"""Deletes a single item, see :func:`~pypet.trajectory.Trajectory.f_delete_items`"""
self.f_delete_items([item], *args, **kwargs)
@kwargs_api_change('remove_empty_groups')
[docs] def f_delete_items(self, iterator, *args, **kwargs):
"""Deletes items from storage on disk.
Per default the item is NOT removed from the trajectory.
Links are NOT deleted on the hard disk, please delete links manually before deleting
data!
:param iterator:
A sequence of items you want to remove. Either the instances themselves
or strings with the names of the items.
:param remove_from_trajectory:
If items should also be removed from trajectory. Default is `False`.
:param args: Additional arguments passed to the storage service
:param kwargs: Additional keyword arguments passed to the storage service
If you use the standard hdf5 storage service, you can pass the following additional
keyword argument:
:param delete_only:
You can partially delete leaf nodes. Specify a list of parts of the result node
that should be deleted like `delete_only=['mystuff','otherstuff']`.
This wil only delete the hdf5 sub parts `mystuff` and `otherstuff` from disk.
BE CAREFUL,
erasing data partly happens at your own risk. Depending on how complex the
loading process of your result node is, you might not be able to reconstruct
any data due to partially deleting some of it.
Be aware that you need to specify the names of parts as they were stored
to HDF5. Depending on how your leaf construction works, this may differ
from the names the data might have in your leaf in the trajectory container.
If the hdf5 nodes you specified in `delete_only` cannot be found a warning
is issued.
Note that massive deletion will fragment your HDF5 file.
Try to avoid changing data on disk whenever you can.
If you want to erase a full node, simply ignore this argument or set to `None`.
:param remove_from_item:
If data that you want to delete from storage should also be removed from
the items in `iterator` if they contain these. Default is `False`.
:param recursive:
If you want to delete a group node and it has children you need to
set `recursive` to `True. Default is `False`.
"""
remove_from_trajectory = kwargs.pop('remove_from_trajectory', False)
recursive = kwargs.get('recursive', False)
# Will format the request in a form that is understood by the storage service
# aka (msg, item, args, kwargs)
fetched_items = self._nn_interface._fetch_items(REMOVE, iterator, args, kwargs)
if fetched_items:
try:
self._storage_service.store(pypetconstants.LIST, fetched_items,
trajectory_name=self.v_name)
except:
self._logger.error('Could not remove `%s` from the trajectory. Maybe the'
' item(s) was/were never stored to disk.' % str(fetched_items))
raise
for _, item, dummy1, dummy2 in fetched_items:
if remove_from_trajectory:
self._nn_interface._remove_node_or_leaf(item, recursive=recursive)
else:
item._stored = False
else:
self._logger.warning('Your removal was not successful, could not find a single '
'item to remove.')
@not_in_run
@copydoc(ConfigGroup.f_add_config)
[docs] def f_add_config(self, *args, **kwargs):
return super(Trajectory, self).f_add_config(*args, **kwargs)
@not_in_run
@copydoc(ConfigGroup.f_add_config_group)
[docs] def f_add_config_group(self, *args, **kwargs):
return super(Trajectory, self).f_add_config_group(*args, **kwargs)
@not_in_run
@copydoc(ParameterGroup.f_add_parameter)
[docs] def f_add_parameter(self, *args, **kwargs):
return super(Trajectory, self).f_add_parameter(*args, **kwargs)
@not_in_run
@copydoc(ParameterGroup.f_add_parameter_group)
[docs] def f_add_parameter_group(self, *args, **kwargs):
return super(Trajectory, self).f_add_parameter_group(*args, **kwargs)
def __dir__(self):
"""Adds all children to auto-completion
In case of a single run it spares all non-available functions
"""
result = dir(type(self))
if self._is_run:
result = [x for x in result if not x.startswith('f_') or
not getattr(getattr(self, x), '_not_in_run', False)]
result.extend(compat.listkeys(self.__dict__))
if not is_debug():
result.extend(self._children.keys())
return result