_id stringlengths 2 7 | title stringlengths 1 88 | partition stringclasses 3
values | text stringlengths 31 13.1k | language stringclasses 1
value | meta_information dict |
|---|---|---|---|---|---|
q274100 | LockerServer._lock | test | def _lock(self, name, client_id, request_id):
"""Hanldes locking of locks
If a lock is already locked sends a WAIT command,
else LOCKs it and sends GO.
Complains if a given client re-locks a lock without releasing it before.
"""
if name in self._locks:
other_client_id, other_request_id = self._locks[name]
if other_client_id == client_id:
response = (self.LOCK_ERROR + self.DELIMITER +
'Re-request of lock `%s` (old request id | python | {
"resource": ""
} |
q274101 | ReliableClient.send_done | test | def send_done(self):
"""Notifies the Server to shutdown"""
self.start(test_connection=False) | python | {
"resource": ""
} |
q274102 | ReliableClient.finalize | test | def finalize(self):
"""Closes socket and terminates context
NO-OP if already closed.
"""
if self._context is not None:
if self._socket is not None:
| python | {
"resource": ""
} |
q274103 | ReliableClient.start | test | def start(self, test_connection=True):
"""Starts connection to server if not existent.
NO-OP if connection is already established.
Makes ping-pong test as well if desired.
"""
if self._context is None:
self._logger.debug('Starting Client') | python | {
"resource": ""
} |
q274104 | ReliableClient._req_rep_retry | test | def _req_rep_retry(self, request):
"""Returns response and number of retries"""
retries_left = self.RETRIES
while retries_left:
self._logger.log(1, 'Sending REQ `%s`', request)
self._send_request(request)
socks = dict(self._poll.poll(self.TIMEOUT))
if socks.get(self._socket) == zmq.POLLIN:
response = self._receive_response()
self._logger.log(1, 'Received REP `%s`', response)
return response, self.RETRIES - retries_left
else:
| python | {
"resource": ""
} |
q274105 | LockerClient.acquire | test | def acquire(self):
"""Acquires lock and returns `True`
Blocks until lock is available.
"""
self.start(test_connection=False)
while True:
str_response, retries = self._req_rep_retry(LockerServer.LOCK)
response = str_response.split(LockerServer.DELIMITER)
if response[0] == LockerServer.GO:
return True
elif response[0] == LockerServer.LOCK_ERROR and retries > 0:
# Message was sent but Server response was lost and we tried again
| python | {
"resource": ""
} |
q274106 | QueuingServerMessageListener.listen | test | def listen(self):
""" Handles listening requests from the client.
There are 4 types of requests:
1- Check space in the queue
2- Tests the socket
3- If there is a space, it sends data
4- after data is sent, puts it to queue for storing
"""
count = 0
self._start()
while True:
result = self._socket.recv_pyobj()
if isinstance(result, tuple):
request, data = result
else:
request = result
data = None
if request == self.SPACE:
if self.queue.qsize() + count < self.queue_maxsize:
self._socket.send_string(self.SPACE_AVAILABLE)
count += 1
else:
self._socket.send_string(self.SPACE_NOT_AVAILABLE)
elif request == self.PING:
| python | {
"resource": ""
} |
q274107 | QueuingClient.put | test | def put(self, data, block=True):
""" If there is space it sends data to server
If no space in the queue
It returns the request in every 10 millisecond
until there will be space in the queue.
"""
self.start(test_connection=False)
while True:
response = self._req_rep(QueuingServerMessageListener.SPACE)
| python | {
"resource": ""
} |
q274108 | ForkDetector._detect_fork | test | def _detect_fork(self):
"""Detects if lock client was forked.
Forking is detected by comparing the PID of the current
process with the stored PID.
"""
if self._pid is None:
self._pid = os.getpid()
| python | {
"resource": ""
} |
q274109 | StorageServiceDataHandler._handle_data | test | def _handle_data(self, msg, args, kwargs):
"""Handles data and returns `True` or `False` if everything is done."""
stop = False
try:
if msg == 'DONE':
stop = True
elif msg == 'STORE':
if 'msg' in kwargs:
store_msg = kwargs.pop('msg')
else:
store_msg = args[0]
args = args[1:]
if 'stuff_to_store' in kwargs:
stuff_to_store = kwargs.pop('stuff_to_store')
else:
stuff_to_store = args[0]
args = args[1:]
trajectory_name = kwargs['trajectory_name']
if self._trajectory_name != trajectory_name:
if self._storage_service.is_open:
self._close_file()
self._trajectory_name = trajectory_name
self._open_file()
self._storage_service.store(store_msg, stuff_to_store, *args, **kwargs) | python | {
"resource": ""
} |
q274110 | StorageServiceDataHandler.run | test | def run(self):
"""Starts listening to the queue."""
try:
while True:
msg, args, kwargs = self._receive_data()
stop = self._handle_data(msg, args, kwargs)
if stop:
| python | {
"resource": ""
} |
q274111 | QueueStorageServiceWriter._receive_data | test | def _receive_data(self):
"""Gets data from queue"""
result = self.queue.get(block=True)
if | python | {
"resource": ""
} |
q274112 | PipeStorageServiceWriter._receive_data | test | def _receive_data(self):
"""Gets data from pipe"""
while True:
while len(self._buffer) < self.max_size and self.conn.poll():
data = self._read_chunks()
if | python | {
"resource": ""
} |
q274113 | LockWrapper.store | test | def store(self, *args, **kwargs):
"""Acquires a lock before storage and releases it afterwards."""
try:
self.acquire_lock()
return self._storage_service.store(*args, **kwargs)
| python | {
"resource": ""
} |
q274114 | ReferenceWrapper.store | test | def store(self, msg, stuff_to_store, *args, **kwargs):
"""Simply keeps a reference to the stored data """
trajectory_name = kwargs['trajectory_name']
if trajectory_name not in self.references:
| python | {
"resource": ""
} |
q274115 | ReferenceStore.store_references | test | def store_references(self, references):
"""Stores references to disk and may collect garbage."""
for trajectory_name in | python | {
"resource": ""
} |
q274116 | parse_config | test | def parse_config(init_func):
"""Decorator wrapping the environment to use a config file"""
@functools.wraps(init_func)
def new_func(env, *args, **kwargs):
config_interpreter = ConfigInterpreter(kwargs)
# Pass the config data to the kwargs
new_kwargs = config_interpreter.interpret()
init_func(env, | python | {
"resource": ""
} |
q274117 | ConfigInterpreter._collect_section | test | def _collect_section(self, section):
"""Collects all settings within a section"""
kwargs = {}
try:
if self.parser.has_section(section):
options = self.parser.options(section)
for option in options:
| python | {
"resource": ""
} |
q274118 | ConfigInterpreter._collect_config | test | def _collect_config(self):
"""Collects all info from three sections"""
kwargs = {}
sections = | python | {
"resource": ""
} |
q274119 | ConfigInterpreter.interpret | test | def interpret(self):
"""Copies parsed arguments into the kwargs passed to the environment"""
if self.config_file:
new_kwargs = self._collect_config()
for key in new_kwargs:
# | python | {
"resource": ""
} |
q274120 | ConfigInterpreter.add_parameters | test | def add_parameters(self, traj):
"""Adds parameters and config from the `.ini` file to the trajectory"""
if self.config_file:
parameters = self._collect_section('parameters')
for name in parameters:
value = parameters[name]
if not isinstance(value, tuple):
value = (value,)
traj.f_add_parameter(name, *value)
| python | {
"resource": ""
} |
q274121 | convert_rule | test | def convert_rule(rule_number):
""" Converts a rule given as an integer into a binary list representation.
It reads from left to right (contrary to the Wikipedia article given below),
i.e. the 2**0 is found on the left hand side and 2**7 on the right.
For example:
``convert_rule(30)`` returns [0, 1, 1, 1, 1, 0, 0, 0]
The resulting binary list can be interpreted as
the following transition table:
neighborhood new cell state
000 0
001 1
010 1
011 1
100 1
| python | {
"resource": ""
} |
q274122 | make_initial_state | test | def make_initial_state(name, ncells, seed=42):
""" Creates an initial state for the automaton.
:param name:
Either ``'single'`` for a single live cell in the middle of the cell ring,
or ``'random'`` for uniformly distributed random pattern of zeros and ones.
:param ncells: Number of cells in the automaton
:param seed: Random number seed for the ``#random'`` | python | {
"resource": ""
} |
q274123 | plot_pattern | test | def plot_pattern(pattern, rule_number, filename):
""" Plots an automaton ``pattern`` and stores the image under a given ``filename``.
For axes labels the ``rule_number`` is also required.
"""
plt.figure()
| python | {
"resource": ""
} |
q274124 | cellular_automaton_1D | test | def cellular_automaton_1D(initial_state, rule_number, steps):
""" Simulates a 1 dimensional cellular automaton.
:param initial_state:
The initial state of *dead* and *alive* cells as a 1D numpy array.
It's length determines the size of the simulation.
:param rule_number:
The update rule as an integer from 0 to 255.
:param steps:
Number of cell iterations
:return:
A 2D numpy array (steps x len(initial_state)) containing zeros and ones representing
the automaton development over time.
"""
ncells = len(initial_state)
# Create an array for the full pattern
pattern = np.zeros((steps, ncells))
# Pass | python | {
"resource": ""
} |
q274125 | main | test | def main():
""" Main simulation function """
rules_to_test = [10, 30, 90, 110, 184] # rules we want to explore:
steps = 250 # cell iterations
ncells = 400 # number of cells
seed = 100042 # RNG seed
initial_states = ['single', 'random'] # Initial states we want to explore
# create a folder for the plots and the data
folder = os.path.join(os.getcwd(), 'experiments', 'ca_patterns_original')
if not os.path.isdir(folder):
os.makedirs(folder)
filename = os.path.join(folder, 'all_patterns.p')
print('Computing all patterns')
all_patterns = [] # list containing the simulation results
for idx, rule_number in enumerate(rules_to_test):
# iterate over all rules
for initial_name in initial_states:
# iterate over the initial states
# make the initial state
initial_state = make_initial_state(initial_name, ncells, seed=seed)
# simulate the automaton
pattern = cellular_automaton_1D(initial_state, rule_number, steps)
# keep the resulting pattern
all_patterns.append((rule_number, initial_name, pattern))
# Print a progressbar, because I am always impatient
| python | {
"resource": ""
} |
q274126 | NodeProcessingTimer.signal_update | test | def signal_update(self):
"""Signals the process timer.
If more time than the display time has passed a message is emitted.
"""
if not self.active:
return
self._updates += 1
current_time = time.time()
dt = current_time - self._last_time
if dt > self._display_time:
dfullt = current_time - self._start_time
seconds = int(dfullt) % 60
minutes = int(dfullt) / 60
if minutes == 0:
formatted_time = '%ds' % | python | {
"resource": ""
} |
q274127 | HDF5StorageService._overview_group | test | def _overview_group(self):
"""Direct link to the overview group"""
if self._overview_group_ is | python | {
"resource": ""
} |
q274128 | HDF5StorageService.load | test | def load(self, msg, stuff_to_load, *args, **kwargs):
"""Loads a particular item from disk.
The storage service always accepts these parameters:
:param trajectory_name: Name of current trajectory and name of top node in hdf5 file.
:param trajectory_index:
If no `trajectory_name` is provided, you can specify an integer index.
The trajectory at the index position in the hdf5 file is considered to loaded.
Negative indices are also possible for reverse indexing.
:param filename: Name of the hdf5 file
The following messages (first argument msg) are understood and the following arguments
can be provided in combination with the message:
* :const:`pypet.pypetconstants.TRAJECTORY` ('TRAJECTORY')
Loads a trajectory.
:param stuff_to_load: The trajectory
:param as_new: Whether to load trajectory as new
:param load_parameters: How to load parameters and config
:param load_derived_parameters: How to load derived parameters
:param load_results: How to load results
:param force: Force load in case there is a pypet version mismatch
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 including annotations are loaded, i.e. the items are empty.
Non-empty items in RAM are left untouched.
:const:`pypet.pypetconstants.LOAD_DATA`: (2)
The whole data is loaded.
Only empty or in RAM non-existing instance are filled with the
data found on disk.
:const:`pypet.pypetconstants.OVERWRITE_DATA`: (3)
The whole data is loaded.
If items that are to be loaded are already in RAM and not empty,
they are emptied and new data is loaded from disk.
* :const:`pypet.pypetconstants.LEAF` ('LEAF')
Loads a parameter or result.
:param stuff_to_load: The item to be loaded
:param load_data: How to load data
:param load_only:
If you load a result, you can partially load it and ignore the
rest of the data. 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']`.
Issues a warning if items cannot be found.
:param load_except:
If you load a result you can partially load in and specify items
that should NOT be loaded here. You cannot use `load_except` and
`load_only` at the same time.
* :const:`pypet.pyetconstants.GROUP`
Loads a group a node (comment and annotations)
:param recursive:
Recursively loads everything below
:param load_data:
How to load stuff if ``recursive=True``
accepted values as above for loading the trajectory
:param max_depth:
Maximum depth in case of recursion. `None` for no limit.
* :const:`pypet.pypetconstants.TREE` ('TREE')
Loads a whole subtree
| python | {
"resource": ""
} |
q274129 | HDF5StorageService.store | test | def store(self, msg, stuff_to_store, *args, **kwargs):
""" Stores a particular item to disk.
The storage service always accepts these parameters:
:param trajectory_name: Name or current trajectory and name of top node in hdf5 file
:param filename: Name of the hdf5 file
:param file_title: If file needs to be created, assigns a title to the file.
The following messages (first argument msg) are understood and the following arguments
can be provided in combination with the message:
* :const:`pypet.pypetconstants.PREPARE_MERGE` ('PREPARE_MERGE'):
Called to prepare a trajectory for merging, see also 'MERGE' below.
Will also be called if merging cannot happen within the same hdf5 file.
Stores already enlarged parameters and updates meta information.
:param stuff_to_store: Trajectory that is about to be extended by another one
:param changed_parameters:
List containing all parameters that were enlarged due to merging
:param old_length:
Old length of trajectory before merge
* :const:`pypet.pypetconstants.MERGE` ('MERGE')
Note that before merging within HDF5 file, the storage service will be called
with msg='PREPARE_MERGE' before, see above.
Raises a ValueError if the two trajectories are not stored within the very
same hdf5 file. Then the current trajectory needs to perform the merge slowly
item by item.
Merges two trajectories, parameters are:
:param stuff_to_store: The trajectory data is merged into
:param other_trajectory_name: Name of the other trajectory
:param rename_dict:
Dictionary containing the old result and derived parameter names in the
other trajectory and their new names in the current trajectory.
:param move_nodes:
Whether to move the nodes from the other to the current trajectory
:param delete_trajectory:
Whether to delete the other trajectory after merging.
* :const:`pypet.pypetconstants.BACKUP` ('BACKUP')
:param stuff_to_store: Trajectory to be backed up
:param backup_filename:
Name of file where to store the backup. If None 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.
* :const:`pypet.pypetconstants.TRAJECTORY` ('TRAJECTORY')
Stores the whole trajectory
:param stuff_to_store: The trajectory to be stored
: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 nodes/leaves within the trajectory.
:param store_data:
How to store data, the following settings are understood:
:const:`pypet.pypetconstants.STORE_NOTHING`: (0)
Nothing is stored
:const:`pypet.pypetconstants.STORE_DATA_SKIPPING`: (1)
Data of not already stored nodes is stored
:const:`pypet.pypetconstants.STORE_DATA`: (2)
Data of all nodes is stored. However, existing data on disk is left
untouched.
:const:`pypet.pypetconstants.OVERWRITE_DATA`: (3)
Data of all nodes is stored and data on disk is overwritten.
May lead to fragmentation of the HDF5 file. The user is adviced
to recompress the file manually later on.
* :const:`pypet.pypetconstants.SINGLE_RUN` ('SINGLE_RUN')
:param stuff_to_store: The trajectory
:param store_data: How to store data see above
:param store_final: If final meta info should be stored
* :const:`pypet.pypetconstants.LEAF`
Stores a parameter or result
Note that everything that is supported by the storage service and that is
stored to disk will be perfectly recovered.
For instance, you store a tuple of numpy 32 bit integers, you will get a tuple
of numpy 32 bit integers after loading independent of the platform!
:param stuff_to_sore: Result or parameter to store
In order to determine what to store, the function '_store' of the parameter or
result is called. This function returns a dictionary with name keys and data to
store as values. In order to determine how to store the data, the storage flags
are considered, see below.
The function '_store' has to return a dictionary containing values only from
the following objects:
* python natives (int, long, str, bool, float, complex),
*
numpy natives, arrays and matrices of type np.int8-64, np.uint8-64,
np.float32-64, np.complex, np.str
*
python lists and tuples of the previous types
(python natives + numpy natives and arrays)
Lists and tuples are not allowed to be nested and must be
homogeneous, i.e. only contain data of one particular type.
Only integers, or only floats, etc.
*
python dictionaries of the previous types (not nested!), data can be
heterogeneous, keys must be strings. For example, one key-value-pair
of string and int and one key-value pair of string and float, and so
on.
* pandas DataFrames_
* :class:`~pypet.parameter.ObjectTable`
.. _DataFrames: http://pandas.pydata.org/pandas-docs/dev/dsintro.html#dataframe
The keys from the '_store' dictionaries determine how the data will be named
in the hdf5 file.
:param store_data:
How to store the data, see above for a descitpion.
:param store_flags: Flags describing how to store data.
:const:`~pypet.HDF5StorageService.ARRAY` ('ARRAY')
Store stuff as array
:const:`~pypet.HDF5StorageService.CARRAY` ('CARRAY')
Store stuff as carray
:const:`~pypet.HDF5StorageService.TABLE` ('TABLE')
Store stuff as pytable
:const:`~pypet.HDF5StorageService.DICT` ('DICT')
Store stuff as pytable but reconstructs it later as dictionary
on loading
:const:`~pypet.HDF%StorageService.FRAME` ('FRAME')
Store stuff as pandas data frame
Storage flags can also be provided by the parameters and results themselves
if they implement a function '_store_flags' that returns a dictionary
with the names of the data to store as keys and the flags as values.
If no storage flags are provided, they are automatically inferred from the
data. See :const:`pypet.HDF5StorageService.TYPE_FLAG_MAPPING` for the mapping
from type to flag.
:param overwrite:
Can be used if parts of a leaf should be replaced. Either a list of
HDF5 names or `True` if this should account for all.
* :const:`pypet.pypetconstants.DELETE` ('DELETE')
| python | {
"resource": ""
} |
q274130 | HDF5StorageService._srvc_load_several_items | test | def _srvc_load_several_items(self, iterable, *args, **kwargs):
"""Loads several items from an iterable
Iterables are supposed to be of a format like `[(msg, item, args, kwarg),...]`
If `args` and `kwargs` are not part of a tuple, they are taken from the
current `args` and `kwargs` provided to this function.
"""
for input_tuple in iterable:
msg = input_tuple[0]
item = input_tuple[1]
if len(input_tuple) > 2: | python | {
"resource": ""
} |
q274131 | HDF5StorageService._srvc_check_hdf_properties | test | def _srvc_check_hdf_properties(self, traj):
"""Reads out the properties for storing new data into the hdf5file
:param traj:
The trajectory
"""
for attr_name in HDF5StorageService.ATTR_LIST:
try:
config = traj.f_get('config.hdf5.' + attr_name).f_get()
setattr(self, attr_name, config)
except AttributeError:
self._logger.debug('Could not find `%s` in traj config, '
'using (default) value `%s`.' %
(attr_name, str(getattr(self, attr_name))))
for attr_name, table_name in HDF5StorageService.NAME_TABLE_MAPPING.items():
try:
if table_name in ('parameters', 'config'):
table_name += '_overview'
config = traj.f_get('config.hdf5.overview.' + table_name).f_get()
setattr(self, attr_name, config)
except AttributeError:
self._logger.debug('Could not find `%s` in traj config, '
'using (default) value `%s`.' %
(table_name, str(getattr(self, attr_name))))
| python | {
"resource": ""
} |
q274132 | HDF5StorageService._srvc_store_several_items | test | def _srvc_store_several_items(self, iterable, *args, **kwargs):
"""Stores several items from an iterable
Iterables are supposed to be of a format like `[(msg, item, args, kwarg),...]`
If `args` and `kwargs` are not part of a tuple, they are taken from the
current `args` and `kwargs` provided to this function.
"""
for input_tuple in iterable:
msg = input_tuple[0]
item = input_tuple[1]
if len(input_tuple) > | python | {
"resource": ""
} |
q274133 | HDF5StorageService._srvc_closing_routine | test | def _srvc_closing_routine(self, closing):
"""Routine to close an hdf5 file
The file is closed only when `closing=True`. `closing=True` means that
the file was opened in the current highest recursion level. This prevents re-opening
and closing of the file if `store` or `load` are called recursively.
"""
if (not self._keep_open and
closing and
self.is_open):
f_fd = self._hdf5file.fileno()
self._hdf5file.flush()
try:
os.fsync(f_fd)
try:
self._hdf5store.flush(fsync=True)
except TypeError:
f_fd = self._hdf5store._handle.fileno()
self._hdf5store.flush()
os.fsync(f_fd)
except OSError as exc:
# This seems to be the only way to avoid an OSError under Windows
errmsg = ('Encountered OSError while flushing file.'
| python | {
"resource": ""
} |
q274134 | HDF5StorageService._srvc_extract_file_information | test | def _srvc_extract_file_information(self, kwargs):
"""Extracts file information from kwargs.
Note that `kwargs` is not passed as `**kwargs` in order to also
`pop` the elements on the level of the function calling `_srvc_extract_file_information`.
"""
if 'filename' in kwargs:
self._filename = kwargs.pop('filename')
if 'file_title' in kwargs:
self._file_title = kwargs.pop('file_title')
| python | {
"resource": ""
} |
q274135 | HDF5StorageService._trj_backup_trajectory | test | def _trj_backup_trajectory(self, traj, backup_filename=None):
"""Backs up a trajectory.
:param traj: Trajectory that should be backed up
:param backup_filename:
Path and filename of backup file. If None is specified the storage service
defaults to `path_to_trajectory_hdf5_file/backup_trajectory_name.hdf`.
"""
self._logger.info('Storing backup of %s.' % traj.v_name)
mypath, _ = os.path.split(self._filename)
if backup_filename is None:
backup_filename = os.path.join('%s' % mypath, 'backup_%s.hdf5' % traj.v_name)
backup_hdf5file = pt.open_file(filename=backup_filename,
| python | {
"resource": ""
} |
q274136 | HDF5StorageService._trj_read_out_row | test | def _trj_read_out_row(colnames, row):
"""Reads out a row and returns a dictionary containing the row content.
:param colnames: List of column names
:param row: A pytables table row
:return: A dictionary with colnames as keys and content as values
| python | {
"resource": ""
} |
q274137 | HDF5StorageService._trj_prepare_merge | test | def _trj_prepare_merge(self, traj, changed_parameters, old_length):
"""Prepares a trajectory for merging.
This function will already store extended parameters.
:param traj: Target of merge
:param changed_parameters: List of extended parameters (i.e. their names).
"""
if not traj._stored:
traj.f_store()
# Update meta information
infotable = getattr(self._overview_group, 'info')
insert_dict = self._all_extract_insert_dict(traj, infotable.colnames)
self._all_add_or_modify_row(traj.v_name, insert_dict, infotable, index=0,
flags=(HDF5StorageService.MODIFY_ROW,))
# Store extended parameters
for param_name in changed_parameters:
param = traj.f_get(param_name)
try:
self._all_delete_parameter_or_result_or_group(param)
except pt.NoSuchNodeError:
pass # We are fine and the node did not exist in the first place
# Increase the run table by the number of new runs
run_table = getattr(self._overview_group, 'runs')
actual_rows = run_table.nrows
self._trj_fill_run_table(traj, actual_rows, len(traj))
# Extract parameter summary and if | python | {
"resource": ""
} |
q274138 | HDF5StorageService._trj_load_meta_data | test | def _trj_load_meta_data(self, traj, load_data, as_new, with_run_information, force):
"""Loads meta information about the trajectory
Checks if the version number does not differ from current pypet version
Loads, comment, timestamp, name, version from disk in case trajectory is not loaded
as new. Updates the run information as well.
"""
metatable = self._overview_group.info
metarow = metatable[0]
try:
version = metarow['version'].decode('utf-8')
except (IndexError, ValueError) as ke:
self._logger.error('Could not check version due to: %s' % str(ke))
version = '`COULD NOT BE LOADED`'
try:
python = metarow['python'].decode('utf-8')
except (IndexError, ValueError) as ke:
self._logger.error('Could not check version due to: %s' % str(ke))
python = '`COULD NOT BE LOADED`'
self._trj_check_version(version, python, force)
# Load the skeleton information
self._grp_load_group(traj, load_data=load_data,
with_links=False, recursive=False, _traj=traj,
_as_new=as_new, _hdf5_group=self._trajectory_group)
if as_new:
length = int(metarow['length'])
for irun in range(length):
traj._add_run_info(irun)
else:
traj._comment = metarow['comment'].decode('utf-8')
traj._timestamp = float(metarow['timestamp'])
traj._trajectory_timestamp = traj._timestamp
traj._time = metarow['time'].decode('utf-8')
traj._trajectory_time = traj._time
traj._name = metarow['name'].decode('utf-8')
traj._trajectory_name = traj._name
traj._version = version
traj._python = python
single_run_table = self._overview_group.runs
if with_run_information:
for row in single_run_table.iterrows():
name = row['name'].decode('utf-8')
idx = int(row['idx'])
timestamp = float(row['timestamp'])
time_ = row['time'].decode('utf-8')
completed = int(row['completed'])
| python | {
"resource": ""
} |
q274139 | HDF5StorageService._tree_load_sub_branch | test | def _tree_load_sub_branch(self, traj_node, branch_name,
load_data=pypetconstants.LOAD_DATA,
with_links=True, recursive=False,
max_depth=None, _trajectory=None,
_as_new=False, _hdf5_group=None):
"""Loads data starting from a node along a branch and starts recursively loading
all data at end of branch.
:param traj_node: The node from where loading starts
:param branch_name:
A branch along which loading progresses. Colon Notation is used:
'group1.group2.group3' loads 'group1', then 'group2', then 'group3' and then finally
recursively all children and children's children below 'group3'
:param load_data:
How to load the data
:param with_links:
If links should be loaded
:param recursive:
If loading recursively
:param max_depth:
The maximum depth to load the tree
:param _trajectory:
The trajectory
:param _as_new:
If trajectory is loaded as new
:param _hdf5_group:
HDF5 node in the file corresponding to `traj_node`.
"""
if load_data == pypetconstants.LOAD_NOTHING:
return
if max_depth is None:
max_depth = float('inf')
if _trajectory is None:
_trajectory = traj_node.v_root
if _hdf5_group is None:
hdf5_group_name = traj_node.v_full_name.replace('.', '/')
# Get child node to load
if hdf5_group_name == '':
_hdf5_group = self._trajectory_group
else:
try:
_hdf5_group = self._hdf5file.get_node(where=self._trajectory_group,
name=hdf5_group_name)
except pt.NoSuchNodeError:
self._logger.error('Cannot find `%s` the hdf5 node `%s` does not exist!'
| python | {
"resource": ""
} |
q274140 | HDF5StorageService._trj_check_version | test | def _trj_check_version(self, version, python, force):
"""Checks for version mismatch
Raises a VersionMismatchError if version of loaded trajectory and current pypet version
do not match. In case of `force=True` error is not raised only a warning is emitted.
"""
curr_python = pypetconstants.python_version_string
if (version != VERSION or curr_python != python) and not force:
raise pex.VersionMismatchError('Current pypet version is %s used under python %s '
' but your trajectory'
' was created with version %s and python %s.'
' Use >>force=True<< to perform your load regardless'
| python | {
"resource": ""
} |
q274141 | HDF5StorageService._trj_fill_run_table | test | def _trj_fill_run_table(self, traj, start, stop):
"""Fills the `run` overview table with information.
Will also update new information.
"""
def _make_row(info_dict):
row = (info_dict['idx'],
info_dict['name'],
info_dict['time'],
info_dict['timestamp'],
info_dict['finish_timestamp'],
info_dict['runtime'],
info_dict['parameter_summary'],
info_dict['short_environment_hexsha'],
info_dict['completed'])
| python | {
"resource": ""
} |
q274142 | HDF5StorageService._trj_load_exploration | test | def _trj_load_exploration(self, traj):
"""Recalls names of all explored parameters"""
if hasattr(self._overview_group, 'explorations'):
explorations_table = self._overview_group._f_get_child( 'explorations')
for row in explorations_table.iterrows():
param_name = row['explorations'].decode('utf-8')
if param_name not in traj._explored_parameters:
traj._explored_parameters[param_name] = None
else:
# This is for backwards compatibility
for what in ('parameters', 'derived_parameters'):
if hasattr(self._trajectory_group, what):
| python | {
"resource": ""
} |
q274143 | HDF5StorageService._trj_store_explorations | test | def _trj_store_explorations(self, traj):
"""Stores a all explored parameter names for internal recall"""
nexplored = len(traj._explored_parameters)
if nexplored > 0:
if hasattr(self._overview_group, 'explorations'):
explorations_table = self._overview_group._f_get_child('explorations')
if len(explorations_table) != nexplored:
self._hdf5file.remove_node(where=self._overview_group,
name='explorations')
if not hasattr(self._overview_group, 'explorations'):
explored_list = list(traj._explored_parameters.keys())
if explored_list:
string_col = self._all_get_table_col('explorations',
explored_list,
| python | {
"resource": ""
} |
q274144 | HDF5StorageService._srvc_make_overview_tables | test | def _srvc_make_overview_tables(self, tables_to_make, traj=None):
"""Creates the overview tables in overview group"""
for table_name in tables_to_make:
# Prepare the tables desciptions, depending on which overview table we create
# we need different columns
paramdescriptiondict = {}
expectedrows = 0
# Every overview table has a name and location column
paramdescriptiondict['location'] = pt.StringCol(
pypetconstants.HDF5_STRCOL_MAX_LOCATION_LENGTH,
pos=0)
paramdescriptiondict['name'] = pt.StringCol(pypetconstants.HDF5_STRCOL_MAX_NAME_LENGTH,
pos=1)
paramdescriptiondict['comment'] = pt.StringCol(
pypetconstants.HDF5_STRCOL_MAX_COMMENT_LENGTH)
paramdescriptiondict['value'] = pt.StringCol(
pypetconstants.HDF5_STRCOL_MAX_VALUE_LENGTH, pos=2)
if table_name == 'config_overview':
if traj is not None:
expectedrows = len(traj._config)
if table_name == 'parameters_overview':
if traj is not None:
expectedrows = len(traj._parameters)
if table_name == 'explored_parameters_overview':
paramdescriptiondict['range'] = pt.StringCol(
pypetconstants.HDF5_STRCOL_MAX_RANGE_LENGTH)
paramdescriptiondict['length'] = pt.IntCol()
if traj is not None:
expectedrows = len(traj._explored_parameters)
if table_name.endswith('summary'):
paramdescriptiondict['hexdigest'] = pt.StringCol(64, pos=10)
# Check if the user provided an estimate of the amount of results per run
# This can help to speed up storing
if table_name == 'derived_parameters_overview':
expectedrows = self._derived_parameters_per_run
if traj is not None:
expectedrows *= | python | {
"resource": ""
} |
q274145 | HDF5StorageService._trj_store_trajectory | test | def _trj_store_trajectory(self, traj, only_init=False, store_data=pypetconstants.STORE_DATA,
max_depth=None):
""" Stores a trajectory to an hdf5 file
Stores all groups, parameters and results
"""
if not only_init:
self._logger.info('Start storing Trajectory `%s`.' % self._trajectory_name)
else:
self._logger.info('Initialising storage or updating meta data of Trajectory `%s`.' %
self._trajectory_name)
store_data = pypetconstants.STORE_NOTHING
# In case we accidentally chose a trajectory name that already exist
# We do not want to mess up the stored trajectory but raise an Error
if not traj._stored and self._trajectory_group is not None:
raise RuntimeError('You want to store a completely new trajectory with name'
' `%s` but this trajectory is already found in file `%s`.'
'Did you try to accidentally overwrite existing data? If '
'you DO want to override existing data, use `overwrite_file=True`.'
'Note that this deletes the whole HDF5 file not just the particular '
'trajectroy therein! ' %
(traj.v_name, self._filename))
# Extract HDF5 settings from the trajectory
self._srvc_check_hdf_properties(traj)
# Store the trajectory for the first time if necessary:
if self._trajectory_group is None:
self._trajectory_group = self._hdf5file.create_group(where='/',
name=self._trajectory_name,
title=self._trajectory_name,
filters=self._all_get_filters())
# Store meta information
self._trj_store_meta_data(traj)
# # Store recursively the config subtree
# self._tree_store_recursively(pypetconstants.LEAF,traj.config,self._trajectory_group)
if store_data in (pypetconstants.STORE_DATA_SKIPPING,
| python | {
"resource": ""
} |
q274146 | HDF5StorageService._tree_store_sub_branch | test | def _tree_store_sub_branch(self, traj_node, branch_name,
store_data=pypetconstants.STORE_DATA,
with_links=True,
recursive=False,
max_depth=None,
hdf5_group=None):
"""Stores data starting from a node along a branch and starts recursively loading
all data at end of branch.
:param traj_node: The node where storing starts
:param branch_name:
A branch along which storing progresses. Colon Notation is used:
'group1.group2.group3' loads 'group1', then 'group2', then 'group3', and then finally
recursively all children and children's children below 'group3'.
:param store_data: How data should be stored
:param with_links: If links should be stored
:param recursive:
If the rest of the tree should be recursively stored
:param max_depth:
Maximum depth to store
:param hdf5_group:
HDF5 node in the file corresponding to `traj_node`
"""
if store_data == pypetconstants.STORE_NOTHING:
return
if max_depth is None:
max_depth = float('inf')
if hdf5_group is None:
# Get parent hdf5 node
location = traj_node.v_full_name
hdf5_location = location.replace('.', '/')
try:
if location == '':
hdf5_group = self._trajectory_group
else:
hdf5_group = self._hdf5file.get_node( where=self._trajectory_group,
name=hdf5_location)
except pt.NoSuchNodeError:
self._logger.debug('Cannot store `%s` the parental hdf5 node with path `%s` does '
'not exist on disk.' %
(traj_node.v_name, hdf5_location))
if traj_node.v_is_leaf:
self._logger.error('Cannot store `%s` the parental hdf5 '
'node with path `%s` does '
'not exist on disk! The child '
'you want to store is a leaf node,'
'that cannot be stored without '
'the parental node existing on '
'disk.' % (traj_node.v_name, hdf5_location))
raise
else:
self._logger.debug('I will try to store the path from trajectory root to '
'the child now.')
self._tree_store_sub_branch(traj_node._nn_interface._root_instance,
| python | {
"resource": ""
} |
q274147 | HDF5StorageService._tree_create_leaf | test | def _tree_create_leaf(self, name, trajectory, hdf5_group):
""" Creates a new pypet leaf instance.
Returns the leaf and if it is an explored parameter the length of the range.
"""
| python | {
"resource": ""
} |
q274148 | HDF5StorageService._tree_load_nodes_dfs | test | def _tree_load_nodes_dfs(self, parent_traj_node, load_data, with_links, recursive,
max_depth, current_depth, trajectory, as_new, hdf5_group):
"""Loads a node from hdf5 file and if desired recursively everything below
:param parent_traj_node: The parent node whose child should be loaded
:param load_data: How to load the data
:param with_links: If links should be loaded
:param recursive: Whether loading recursively below hdf5_group
:param max_depth: Maximum depth
:param current_depth: Current depth
:param trajectory: The trajectory object
:param as_new: If trajectory is loaded as new
:param hdf5_group: The hdf5 group containing the child to be loaded
"""
if max_depth is None:
max_depth = float('inf')
loading_list = [(parent_traj_node, current_depth, hdf5_group)]
while loading_list:
parent_traj_node, current_depth, hdf5_group = loading_list.pop()
if isinstance(hdf5_group, pt.link.SoftLink):
if with_links:
# We end up here when auto-loading a soft link
self._tree_load_link(parent_traj_node, load_data=load_data, traj=trajectory,
as_new=as_new, hdf5_soft_link=hdf5_group)
continue
name = hdf5_group._v_name
is_leaf = self._all_get_from_attrs(hdf5_group, HDF5StorageService.LEAF)
in_trajectory = name in parent_traj_node._children
if is_leaf:
# In case we have a leaf node, we need to check if we have to create a new
# parameter or result
| python | {
"resource": ""
} |
q274149 | HDF5StorageService._tree_store_nodes_dfs | test | def _tree_store_nodes_dfs(self, parent_traj_node, name, store_data, with_links, recursive,
max_depth, current_depth,
parent_hdf5_group):
"""Stores a node to hdf5 and if desired stores recursively everything below it.
:param parent_traj_node: The parental node
:param name: Name of node to be stored
:param store_data: How to store data
:param with_links: If links should be stored
:param recursive: Whether to store recursively the subtree
:param max_depth: Maximum recursion depth in tree
:param current_depth: Current depth
:param parent_hdf5_group: Parent hdf5 group
"""
if max_depth is None:
max_depth = float('inf')
store_list = [(parent_traj_node, name, current_depth, parent_hdf5_group)]
while store_list:
parent_traj_node, name, current_depth, parent_hdf5_group = store_list.pop()
# Check if we create a link
if name in parent_traj_node._links:
if with_links:
self._tree_store_link(parent_traj_node, name, parent_hdf5_group)
continue
traj_node = parent_traj_node._children[name]
# If the node does not exist in the hdf5 file create it
if not hasattr(parent_hdf5_group, name):
newly_created = True
new_hdf5_group = self._hdf5file.create_group(where=parent_hdf5_group,
name=name, filters=self._all_get_filters())
else:
newly_created = False
new_hdf5_group = getattr(parent_hdf5_group, name)
if traj_node.v_is_leaf:
| python | {
"resource": ""
} |
q274150 | HDF5StorageService._all_store_param_or_result_table_entry | test | def _all_store_param_or_result_table_entry(self, instance, table, flags,
additional_info=None):
"""Stores a single row into an overview table
:param instance: A parameter or result instance
:param table: Table where row will be inserted
:param flags:
Flags how to insert into the table. Potential Flags are
`ADD_ROW`, `REMOVE_ROW`, `MODIFY_ROW`
:param additional_info:
Dictionary containing information that cannot be extracted from
`instance`, but needs to be inserted, too.
"""
# assert isinstance(table, pt.Table)
location = instance.v_location
name = instance.v_name
fullname = instance.v_full_name
if (flags == (HDF5StorageService.ADD_ROW,) and table.nrows < 2
and 'location' in table.colnames):
# We add the modify row option here because you cannot delete the very first
# row of the table, so there is the rare condition, that the row might already
# exist.
# We also need to check if 'location' is in the columns in order to avoid
# confusion with the smaller explored parameter overviews
flags = (HDF5StorageService.ADD_ROW, HDF5StorageService.MODIFY_ROW)
if flags == (HDF5StorageService.ADD_ROW,):
# If we are sure we only want to | python | {
"resource": ""
} |
q274151 | HDF5StorageService._all_get_or_create_table | test | def _all_get_or_create_table(self, where, tablename, description, expectedrows=None):
"""Creates a new table, or if the table already exists, returns it."""
where_node = self._hdf5file.get_node(where)
if not tablename in where_node:
if not expectedrows is None:
table = self._hdf5file.create_table(where=where_node, name=tablename,
description=description, title=tablename,
expectedrows=expectedrows,
filters=self._all_get_filters())
else:
| python | {
"resource": ""
} |
q274152 | HDF5StorageService._all_get_node_by_name | test | def _all_get_node_by_name(self, name):
"""Returns an HDF5 node by the path specified in `name`"""
path_name = name.replace('.', '/')
| python | {
"resource": ""
} |
q274153 | HDF5StorageService._all_set_attributes_to_recall_natives | test | def _all_set_attributes_to_recall_natives(data, ptitem, prefix):
"""Stores original data type to hdf5 node attributes for preserving the data type.
:param data:
Data to be stored
:param ptitem:
HDF5 node to store data types as attributes. Can also be just a PTItemMock.
:param prefix:
String prefix to label and name data in HDF5 attributes
"""
# If `data` is a container, remember the container type
if type(data) is tuple:
HDF5StorageService._all_set_attr(ptitem, prefix + HDF5StorageService.COLL_TYPE,
HDF5StorageService.COLL_TUPLE)
elif type(data) is list:
HDF5StorageService._all_set_attr(ptitem, prefix + HDF5StorageService.COLL_TYPE,
HDF5StorageService.COLL_LIST)
elif type(data) is np.ndarray:
HDF5StorageService._all_set_attr(ptitem, prefix + HDF5StorageService.COLL_TYPE,
HDF5StorageService.COLL_NDARRAY)
elif type(data) is np.matrix:
HDF5StorageService._all_set_attr(ptitem, prefix + HDF5StorageService.COLL_TYPE,
HDF5StorageService.COLL_MATRIX)
elif type(data) in pypetconstants.PARAMETER_SUPPORTED_DATA:
HDF5StorageService._all_set_attr(ptitem, prefix + HDF5StorageService.COLL_TYPE,
HDF5StorageService.COLL_SCALAR)
strtype = type(data).__name__
if not strtype in pypetconstants.PARAMETERTYPEDICT:
raise TypeError('I do not know how to handle `%s` its type is `%s`.' %
(str(data), repr(type(data))))
HDF5StorageService._all_set_attr(ptitem, prefix + HDF5StorageService.SCALAR_TYPE, strtype)
elif type(data) is dict:
if len(data) > 0:
HDF5StorageService._all_set_attr(ptitem, prefix + HDF5StorageService.COLL_TYPE,
HDF5StorageService.COLL_DICT)
else:
HDF5StorageService._all_set_attr(ptitem, prefix + HDF5StorageService.COLL_TYPE,
| python | {
"resource": ""
} |
q274154 | HDF5StorageService._all_recall_native_type | test | def _all_recall_native_type(self, data, ptitem, prefix):
"""Checks if loaded data has the type it was stored in. If not converts it.
:param data: Data item to be checked and converted
:param ptitem: HDf5 Node or Leaf from where data was loaded
:param prefix: Prefix for recalling the data type from the hdf5 node attributes
:return:
Tuple, first item is the (converted) `data` item, second boolean whether
item was converted or not.
"""
typestr = self._all_get_from_attrs(ptitem, prefix + HDF5StorageService.SCALAR_TYPE)
colltype = self._all_get_from_attrs(ptitem, prefix + HDF5StorageService.COLL_TYPE)
type_changed = False
# Check what the original data type was from the hdf5 node attributes
if colltype == HDF5StorageService.COLL_SCALAR:
# Here data item was a scalar
if isinstance(data, np.ndarray):
# If we recall a numpy scalar, pytables loads a 1d array :-/
# So we have to change it to a real scalar value
data = np.array([data])[0]
type_changed = True
if not typestr is None:
# Check if current type and stored type match
# if not convert the data
if typestr != type(data).__name__:
if typestr == str.__name__:
data = data.decode(self._encoding)
else:
try:
data = pypetconstants.PARAMETERTYPEDICT[typestr](data)
except KeyError:
# For compatibility with | python | {
"resource": ""
} |
q274155 | HDF5StorageService._all_add_or_modify_row | test | def _all_add_or_modify_row(self, item_name, insert_dict, table, index=None, condition=None,
condvars=None,
flags=(ADD_ROW, MODIFY_ROW,)):
"""Adds or changes a row in a pytable.
:param item_name: Name of item, the row is about, only important for throwing errors.
:param insert_dict:
Dictionary of data that is about to be inserted into the pytables row.
:param table:
The table to insert or modify a row in
:param index:
Index of row to be modified. Instead of an index a search condition can be
used as well, see below.
:param condition:
Condition to search for in the table
:param condvars:
Variables for the search condition
:param flags:
Flags whether to add, modify, or remove a row in the table
"""
if len(flags) == 0:
# No flags means no-op
return
# You can only specify either an index or a condition not both
if index is not None and condition is not None:
raise ValueError('Please give either a condition or an index or none!')
elif condition is not None:
row_iterator = table.where(condition, condvars=condvars)
elif index is not None:
row_iterator = table.iterrows(index, index + 1)
else:
row_iterator = None
try:
row = next(row_iterator)
except TypeError:
row = None
except StopIteration:
row = None
# multiple_entries = []
if ((HDF5StorageService.MODIFY_ROW in flags or HDF5StorageService.ADD_ROW in flags) and
HDF5StorageService.REMOVE_ROW in flags):
# You cannot remove and modify or add at the same time
raise ValueError('You cannot add or modify and remove a row at the same time.')
if row is None and HDF5StorageService.ADD_ROW in flags:
# Here we add a new row
row = table.row
self._all_insert_into_row(row, insert_dict)
| python | {
"resource": ""
} |
q274156 | HDF5StorageService._all_insert_into_row | test | def _all_insert_into_row(self, row, insert_dict):
"""Copies data from `insert_dict` into a pytables `row`."""
for key, val in insert_dict.items():
try:
row[key] = val
| python | {
"resource": ""
} |
q274157 | HDF5StorageService._all_extract_insert_dict | test | def _all_extract_insert_dict(self, item, colnames, additional_info=None):
"""Extracts information from a given item to be stored into a pytable row.
Items can be a variety of things here, trajectories, single runs, group node,
parameters, results.
:param item: Item from which data should be extracted
:param colnames: Names of the columns in the pytable
:param additional_info: (dict)
Additional information that should be stored into the pytable row that cannot be
read out from `item`.
:return: Dictionary containing the data to be inserted into a row
"""
insert_dict = {}
if 'length' in colnames:
insert_dict['length'] = len(item)
if 'comment' in colnames:
comment = self._all_cut_string(item.v_comment.encode('utf-8'),
pypetconstants.HDF5_STRCOL_MAX_COMMENT_LENGTH,
self._logger)
insert_dict['comment'] = comment
if 'location' in colnames:
insert_dict['location'] = item.v_location.encode('utf-8')
if 'name' in colnames:
name = item._name if (not item.v_is_root or not item.v_is_run) else item._crun
insert_dict['name'] = name.encode('utf-8')
if 'class_name' in colnames:
insert_dict['class_name'] = item.f_get_class_name().encode('utf-8')
if 'value' in colnames:
insert_dict['value'] = self._all_cut_string(
item.f_val_to_str().encode('utf-8'),
pypetconstants.HDF5_STRCOL_MAX_VALUE_LENGTH,
self._logger)
if 'hexdigest' in colnames:
insert_dict['hexdigest'] = additional_info['hexdigest']
if 'idx' in colnames:
insert_dict['idx'] = item.v_idx
if 'time' in colnames:
time_ = item._time
insert_dict['time'] = time_.encode('utf-8')
if 'timestamp' in colnames:
timestamp = item._timestamp
insert_dict['timestamp'] = timestamp
if 'range' in colnames:
third_length = pypetconstants.HDF5_STRCOL_MAX_RANGE_LENGTH // 3 + 10
item_range = itools.islice(item.f_get_range(copy=False), 0, third_length)
| python | {
"resource": ""
} |
q274158 | HDF5StorageService._all_cut_string | test | def _all_cut_string(string, max_length, logger):
"""Cuts string data to the maximum length allowed in a pytables column
if string is too long.
:param string: String to be cut
:param max_length: Maximum allowed string length
:param logger: Logger where messages about truncating should be written
:return: String, cut if too long
"""
| python | {
"resource": ""
} |
q274159 | HDF5StorageService._all_create_or_get_group | test | def _all_create_or_get_group(self, name, parent_hdf5_group=None):
"""Creates or returns a group"""
if not name in parent_hdf5_group:
new_hdf5_group = self._hdf5file.create_group(where=parent_hdf5_group,
name=name,
title=name,
| python | {
"resource": ""
} |
q274160 | HDF5StorageService._all_create_or_get_groups | test | def _all_create_or_get_groups(self, key, start_hdf5_group=None):
"""Creates new or follows existing group nodes along a given colon separated `key`.
:param key:
Colon separated path along hdf5 file, e.g. `parameters.mobiles.cars`.
:param start_hdf5_group:
HDF5 group from where to start, leave `None` for the trajectory group.
:return:
Final group node, e.g. group node with name `cars`.
"""
if start_hdf5_group is None:
newhdf5_group = self._trajectory_group
| python | {
"resource": ""
} |
q274161 | HDF5StorageService._ann_store_annotations | test | def _ann_store_annotations(self, item_with_annotations, node, overwrite=False):
"""Stores annotations into an hdf5 file."""
# If we overwrite delete all annotations first
if overwrite is True or overwrite == 'v_annotations':
annotated = self._all_get_from_attrs(node, HDF5StorageService.ANNOTATED)
if annotated:
current_attrs = node._v_attrs
for attr_name in current_attrs._v_attrnames:
if attr_name.startswith(HDF5StorageService.ANNOTATION_PREFIX):
delattr(current_attrs, attr_name)
delattr(current_attrs, HDF5StorageService.ANNOTATED)
self._hdf5file.flush()
# Only store annotations if the item has some
if not item_with_annotations.v_annotations.f_is_empty():
anno_dict = item_with_annotations.v_annotations._dict
current_attrs = node._v_attrs
changed = False
for field_name in anno_dict:
val = anno_dict[field_name]
| python | {
"resource": ""
} |
q274162 | HDF5StorageService._ann_load_annotations | test | def _ann_load_annotations(self, item_with_annotations, node):
"""Loads annotations from disk."""
annotated = self._all_get_from_attrs(node, HDF5StorageService.ANNOTATED)
if annotated:
annotations = item_with_annotations.v_annotations
# You can only load into non-empty annotations, to prevent overwriting data in RAM
if not annotations.f_is_empty():
raise TypeError('Loading into non-empty annotations!')
current_attrs = node._v_attrs
for attr_name in current_attrs._v_attrnames:
| python | {
"resource": ""
} |
q274163 | HDF5StorageService._grp_store_group | test | def _grp_store_group(self, traj_group, store_data=pypetconstants.STORE_DATA,
with_links=True, recursive=False, max_depth=None,
_hdf5_group=None, _newly_created=False):
"""Stores a group node.
For group nodes only annotations and comments need to be stored.
"""
if store_data == pypetconstants.STORE_NOTHING:
return
elif store_data == pypetconstants.STORE_DATA_SKIPPING and traj_group._stored:
self._logger.debug('Already found `%s` on disk I will not store it!' %
traj_group.v_full_name)
elif not recursive:
if _hdf5_group is None:
_hdf5_group, _newly_created = self._all_create_or_get_groups(traj_group.v_full_name)
overwrite = store_data == pypetconstants.OVERWRITE_DATA
if (traj_group.v_comment != '' and
(HDF5StorageService.COMMENT not in _hdf5_group._v_attrs or overwrite)):
setattr(_hdf5_group._v_attrs, HDF5StorageService.COMMENT, traj_group.v_comment)
if ((_newly_created or overwrite) and
type(traj_group) not in (nn.NNGroupNode, nn.ConfigGroup, nn.ParameterGroup,
nn.DerivedParameterGroup, nn.ResultGroup)):
# We only store the name of the class if it is not one of the standard groups,
| python | {
"resource": ""
} |
q274164 | HDF5StorageService._grp_load_group | test | def _grp_load_group(self, traj_group, load_data=pypetconstants.LOAD_DATA, with_links=True,
recursive=False, max_depth=None,
_traj=None, _as_new=False, _hdf5_group=None):
"""Loads a group node and potentially everything recursively below"""
if _hdf5_group is None:
_hdf5_group = self._all_get_node_by_name(traj_group.v_full_name)
_traj = traj_group.v_root
if recursive:
parent_traj_node = traj_group.f_get_parent()
self._tree_load_nodes_dfs(parent_traj_node, load_data=load_data, with_links=with_links,
recursive=recursive, max_depth=max_depth,
current_depth=0,
trajectory=_traj, as_new=_as_new,
| python | {
"resource": ""
} |
q274165 | HDF5StorageService._all_load_skeleton | test | def _all_load_skeleton(self, traj_node, hdf5_group):
"""Reloads skeleton data of a tree node"""
if traj_node.v_annotations.f_is_empty():
self._ann_load_annotations(traj_node, hdf5_group)
if traj_node.v_comment == '':
| python | {
"resource": ""
} |
q274166 | HDF5StorageService._prm_extract_missing_flags | test | def _prm_extract_missing_flags(data_dict, flags_dict):
"""Extracts storage flags for data in `data_dict`
if they were not specified in `flags_dict`.
See :const:`~pypet.storageservice.HDF5StorageService.TYPE_FLAG_MAPPING`
for how to store different types of data per default.
"""
for key, data in data_dict.items():
if not key in flags_dict:
dtype = type(data)
if (dtype is np.ndarray or dtype is dict) and len(data) == 0:
# Empty containers are stored as an Array
# No need to ask for tuple or list, because they are always
# stored as arrays.
flags_dict[key] = HDF5StorageService.ARRAY
| python | {
"resource": ""
} |
q274167 | HDF5StorageService._prm_meta_add_summary | test | def _prm_meta_add_summary(self, instance):
"""Adds data to the summary tables and returns if `instance`s comment has to be stored.
Also moves comments upwards in the hierarchy if purge_duplicate_comments is true
and a lower index run has completed. Only necessary for *multiprocessing*.
:return: Tuple
* String specifying the subtree
* Boolean whether to store the comment to `instance`s hdf5 node
"""
if instance.v_comment == '':
return False
where = instance.v_branch
definitely_store_comment = True
# Get the hexdigest of the comment to see if such a comment has been stored before
bytes_comment = instance.v_comment.encode('utf-8')
hexdigest = hashlib.sha1(bytes_comment).hexdigest()
hexdigest = hexdigest.encode('utf-8')
# Get the overview table
table_name = where + '_summary'
# Check if the overview | python | {
"resource": ""
} |
q274168 | HDF5StorageService._prm_add_meta_info | test | def _prm_add_meta_info(self, instance, group, overwrite=False):
"""Adds information to overview tables and meta information to
the `instance`s hdf5 `group`.
:param instance: Instance to store meta info about
:param group: HDF5 group of instance
:param overwrite: If data should be explicitly overwritten
"""
if overwrite:
flags = ()
else:
flags = (HDF5StorageService.ADD_ROW,)
definitely_store_comment = True
try:
# Check if we need to store the comment. Maybe update the overview tables
# accordingly if the current run index is lower than the one in the table.
definitely_store_comment = self._prm_meta_add_summary(instance)
try:
# Update the summary overview table
table_name = instance.v_branch + '_overview'
table = getattr(self._overview_group, table_name)
if len(table) < pypetconstants.HDF5_MAX_OVERVIEW_TABLE_LENGTH:
self._all_store_param_or_result_table_entry(instance, table,
flags=flags)
except pt.NoSuchNodeError:
pass
except Exception as exc:
self._logger.error('Could not store information table due to `%s`.' % repr(exc))
if ((not self._purge_duplicate_comments or definitely_store_comment) and | python | {
"resource": ""
} |
q274169 | HDF5StorageService._prm_store_from_dict | test | def _prm_store_from_dict(self, fullname, store_dict, hdf5_group, store_flags, kwargs):
"""Stores a `store_dict`"""
for key, data_to_store in store_dict.items():
# self._logger.log(1, 'SUB-Storing %s [%s]', key, str(store_dict[key]))
original_hdf5_group = None
flag = store_flags[key]
if '.' in key:
original_hdf5_group = hdf5_group
split_key = key.split('.')
key = split_key.pop()
for inner_key in split_key:
hdf5_group, newly_created = self._all_create_or_get_group(inner_key,
hdf5_group)
if newly_created:
setattr(hdf5_group._v_attrs, HDF5StorageService.STORAGE_TYPE,
HDF5StorageService.NESTED_GROUP)
else:
store_type = self._all_get_from_attrs(hdf5_group, HDF5StorageService.STORAGE_TYPE)
if store_type != HDF5StorageService.NESTED_GROUP:
raise ValueError('You want to nested results but `%s` is already '
'of type `%s`!' % (hdf5_group._v_name, store_type))
# Iterate through the data and store according to the storage flags
if key in hdf5_group:
# We won't change any data that is found on disk
self._logger.debug(
'Found %s already in hdf5 node of %s, so I will ignore it.' %
(key, fullname))
continue
if flag == HDF5StorageService.TABLE:
# self._logger.log(1, 'SUB-Storing %s TABLE', key)
self._prm_write_into_pytable(key, data_to_store, hdf5_group, fullname,
**kwargs)
elif flag == HDF5StorageService.DICT:
# self._logger.log(1, 'SUB-Storing %s DICT', key)
self._prm_write_dict_as_table(key, data_to_store, hdf5_group, fullname,
| python | {
"resource": ""
} |
q274170 | HDF5StorageService._prm_store_parameter_or_result | test | def _prm_store_parameter_or_result(self,
instance,
store_data=pypetconstants.STORE_DATA,
store_flags=None,
overwrite=None,
with_links=False,
recursive=False,
_hdf5_group=None,
_newly_created=False,
**kwargs):
"""Stores a parameter or result to hdf5.
:param instance:
The instance to be stored
:param store_data:
How to store data
:param store_flags:
Dictionary containing how to store individual data, usually empty.
:param overwrite:
Instructions how to overwrite data
:param with_links:
Placeholder because leaves have no links
:param recursive:
Placeholder, because leaves have no children
:param _hdf5_group:
The hdf5 group for storing the parameter or result
:param _newly_created:
If should be created in a new form
"""
if store_data == pypetconstants.STORE_NOTHING:
return
elif store_data == pypetconstants.STORE_DATA_SKIPPING and instance._stored:
self._logger.debug('Already found `%s` on disk I will not store it!' %
instance.v_full_name)
return
elif store_data == pypetconstants.OVERWRITE_DATA:
if not overwrite:
overwrite = True
fullname = instance.v_full_name
self._logger.debug('Storing `%s`.' % fullname)
if _hdf5_group is None:
# If no group is provided we might need to create one
_hdf5_group, _newly_created = self._all_create_or_get_groups(fullname)
# kwargs_flags = {} # Dictionary to change settings
# old_kwargs = {}
store_dict = {}
# If the user did not supply storage flags, we need to set it to the empty dictionary
if store_flags is None:
store_flags = {}
try:
# Get the data to store from the instance
if not instance.f_is_empty():
store_dict = instance._store()
try:
# Ask the instance for storage flags
instance_flags = instance._store_flags().copy() # copy to avoid modifying the
# original data
except AttributeError:
# If it does not provide any, set it to the empty dictionary
instance_flags = {}
# User specified flags have priority over the flags from the instance
instance_flags.update(store_flags)
store_flags = instance_flags
# If we still have data in `store_dict` about which we do not know how to store
# it, pick default storage flags
self._prm_extract_missing_flags(store_dict, store_flags)
if overwrite:
if isinstance(overwrite, str):
overwrite = [overwrite]
if overwrite is True:
to_delete = [key for key in store_dict.keys() if key in _hdf5_group]
self._all_delete_parameter_or_result_or_group(instance,
delete_only=to_delete,
_hdf5_group=_hdf5_group)
elif isinstance(overwrite, (list, tuple)):
overwrite_set = set(overwrite)
key_set = set(store_dict.keys())
stuff_not_to_be_overwritten = overwrite_set - key_set
if overwrite!='v_annotations' and len(stuff_not_to_be_overwritten) | python | {
"resource": ""
} |
q274171 | HDF5StorageService._prm_write_shared_array | test | def _prm_write_shared_array(self, key, data, hdf5_group, full_name, flag, **kwargs):
"""Creates and array that can be used with an HDF5 array object"""
if flag == HDF5StorageService.ARRAY:
self._prm_write_into_array(key, data, hdf5_group, full_name, **kwargs)
elif flag in (HDF5StorageService.CARRAY,
HDF5StorageService.EARRAY,
HDF5StorageService.VLARRAY):
self._prm_write_into_other_array(key, data, hdf5_group, full_name,
| python | {
"resource": ""
} |
q274172 | HDF5StorageService._prm_write_shared_table | test | def _prm_write_shared_table(self, key, hdf5_group, fullname, **kwargs):
"""Creates a new empty table"""
first_row = None
description = None
if 'first_row' in kwargs:
first_row = kwargs.pop('first_row')
if not 'description' in kwargs:
description = {}
for colname in first_row:
data = first_row[colname]
column = self._all_get_table_col(key, [data], fullname)
| python | {
"resource": ""
} |
q274173 | HDF5StorageService._prm_write_dict_as_table | test | def _prm_write_dict_as_table(self, key, data_to_store, group, fullname, **kwargs):
"""Stores a python dictionary as pytable
:param key:
Name of data item to store
:param data_to_store:
Dictionary to store
:param group:
Group node where to store data in hdf5 file
:param fullname:
Full name of the `data_to_store`s original container, only needed for throwing errors.
"""
if key in group:
raise ValueError(
'Dictionary `%s` already exists in `%s`. Appending is not supported (yet).')
| python | {
"resource": ""
} |
q274174 | HDF5StorageService._prm_write_pandas_data | test | def _prm_write_pandas_data(self, key, data, group, fullname, flag, **kwargs):
"""Stores a pandas DataFrame into hdf5.
:param key:
Name of data item to store
:param data:
Pandas Data to Store
:param group:
Group node where to store data in hdf5 file
:param fullname:
Full name of the `data_to_store`s original container, only needed for throwing errors.
:param flag:
If it is a series, frame or panel
"""
try:
if 'filters' not in kwargs:
filters = self._all_get_filters(kwargs)
kwargs['filters'] = filters
if 'format' not in kwargs:
kwargs['format'] = self.pandas_format
if 'encoding' not in kwargs:
kwargs['encoding'] = self.encoding
overwrite = kwargs.pop('overwrite', False)
if key in group and not (overwrite or kwargs.get('append', False)):
raise ValueError(
'DataFrame `%s` already exists in `%s`. '
'To append pass ``append=`True```.' % | python | {
"resource": ""
} |
q274175 | HDF5StorageService._prm_write_into_other_array | test | def _prm_write_into_other_array(self, key, data, group, fullname,
flag, **kwargs):
"""Stores data as carray, earray or vlarray depending on `flag`.
:param key:
Name of data item to store
:param data:
Data to store
:param group:
Group node where to store data in hdf5 file
:param fullname:
Full name of the `data_to_store`s original container, only needed for throwing errors.
:param recall:
If container type and data type for perfect recall should be stored
:param flag:
How to store:
CARRAY, EARRAY, VLARRAY
"""
try:
if flag == HDF5StorageService.CARRAY:
factory = self._hdf5file.create_carray
elif flag == HDF5StorageService.EARRAY:
factory = self._hdf5file.create_earray
elif flag == HDF5StorageService.VLARRAY:
factory = self._hdf5file.create_vlarray
else:
raise RuntimeError('You shall not pass!')
if key in group:
raise ValueError(
'CArray `%s` already exists in `%s`. Appending is not supported (yet).')
if 'filters' in kwargs:
filters = kwargs.pop('filters')
else:
filters = self._all_get_filters(kwargs)
try:
other_array = factory(where=group, name=key, obj=data,
filters=filters, **kwargs)
except (ValueError, TypeError) as exc:
try:
| python | {
"resource": ""
} |
q274176 | HDF5StorageService._prm_write_into_array | test | def _prm_write_into_array(self, key, data, group, fullname, **kwargs):
"""Stores data as array.
:param key:
Name of data item to store
:param data:
Data to store
:param group:
Group node where to store data in hdf5 file
:param fullname:
Full name of the `data_to_store`s original container, only needed for throwing errors.
:param recall:
If container type and data type for perfect recall should be stored
"""
try:
if key in group:
raise ValueError(
'Array `%s` already exists in `%s`. Appending is not supported (yet).')
try:
array = self._hdf5file.create_array(where=group,
name=key, obj=data, **kwargs)
except (TypeError, ValueError) as exc:
try:
if type(data) is dict and len(data) == 0:
# We cannot store an empty dictionary,
# but we can use an empty tuple as a dummy.
conv_data = ()
elif isinstance(data, str):
conv_data = data.encode(self._encoding)
elif isinstance(data, int):
conv_data = np.int64(data)
else:
conv_data = []
for string in data:
| python | {
"resource": ""
} |
q274177 | HDF5StorageService._lnk_delete_link | test | def _lnk_delete_link(self, link_name):
"""Removes a link from disk"""
translated_name = | python | {
"resource": ""
} |
q274178 | HDF5StorageService._all_delete_parameter_or_result_or_group | test | def _all_delete_parameter_or_result_or_group(self, instance,
delete_only=None,
remove_from_item=False,
recursive=False,
_hdf5_group=None):
"""Removes a parameter or result or group from the hdf5 file.
:param instance: Instance to be removed
:param delete_only:
List of elements if you only want to delete parts of a leaf node. Note that this
needs to list the names of the hdf5 subnodes. BE CAREFUL if you erase parts of a leaf.
Erasing partly happens at your own risk, it might be the case that you can
no longer reconstruct the leaf from the leftovers!
:param remove_from_item:
If using `delete_only` and `remove_from_item=True` after deletion the data item is
also removed from the `instance`.
:param recursive:
If a group node has children, you will can delete it if recursive is True.
"""
split_name = instance.v_location.split('.')
if _hdf5_group is None:
where = '/' + self._trajectory_name + '/' + '/'.join(split_name)
node_name = instance.v_name
_hdf5_group = self._hdf5file.get_node(where=where, name=node_name)
if delete_only is None:
if instance.v_is_group and not recursive and len(_hdf5_group._v_children) != 0:
raise TypeError('You cannot remove the group `%s`, it has children, please '
'use `recursive=True` to enforce removal.' %
instance.v_full_name)
_hdf5_group._f_remove(recursive=True)
else:
if not instance.v_is_leaf:
raise ValueError('You can only choose `delete_only` mode for leafs.')
| python | {
"resource": ""
} |
q274179 | HDF5StorageService._prm_write_into_pytable | test | def _prm_write_into_pytable(self, tablename, data, hdf5_group, fullname, **kwargs):
"""Stores data as pytable.
:param tablename:
Name of the data table
:param data:
Data to store
:param hdf5_group:
Group node where to store data in hdf5 file
:param fullname:
Full name of the `data_to_store`s original container, only needed for throwing errors.
"""
datasize = data.shape[0]
try:
# Get a new pytables description from the data and create a new table
description_dict, data_type_dict = self._prm_make_description(data, fullname)
description_dicts = [{}]
if len(description_dict) > ptpa.MAX_COLUMNS:
# For optimization we want to store the original data types into another table
# and split the tables into several ones
new_table_group = self._hdf5file.create_group(where=hdf5_group,
name=tablename,
filters=self._all_get_filters(kwargs.copy()))
count = 0
for innerkey in description_dict:
val = description_dict[innerkey]
if count == ptpa.MAX_COLUMNS:
description_dicts.append({})
count = 0
description_dicts[-1][innerkey] = val
count += 1
setattr(new_table_group._v_attrs, HDF5StorageService.STORAGE_TYPE,
HDF5StorageService.TABLE)
setattr(new_table_group._v_attrs, HDF5StorageService.SPLIT_TABLE, 1)
hdf5_group = new_table_group
else:
description_dicts = [description_dict]
for idx, descr_dict in enumerate(description_dicts):
if idx == 0:
tblname = tablename
else:
tblname = tablename + '_%d' % idx
table = self._hdf5file.create_table(where=hdf5_group, name=tblname,
description=descr_dict,
title=tblname,
expectedrows=datasize,
filters=self._all_get_filters(kwargs.copy()))
row = table.row
for n in range(datasize):
# Fill the columns with data, note if the parameter was extended nstart!=0
for key in descr_dict:
row[key] = data[key][n]
row.append()
# Remember the original types of the data for perfect recall
if idx == 0 and len(description_dict) <= ptpa.MAX_COLUMNS:
# We only have a single table and
# we can store the original data types as attributes
for field_name in data_type_dict:
type_description = data_type_dict[field_name]
| python | {
"resource": ""
} |
q274180 | HDF5StorageService._prm_make_description | test | def _prm_make_description(self, data, fullname):
""" Returns a description dictionary for pytables table creation"""
def _convert_lists_and_tuples(series_of_data):
"""Converts lists and tuples to numpy arrays"""
if isinstance(series_of_data[0],
(list, tuple)): # and not isinstance(series_of_data[0], np.ndarray):
# If the first data item is a list, the rest must be as well, since
# data has to be homogeneous
for idx, item in enumerate(series_of_data):
series_of_data[idx] = np.array(item)
descriptiondict = {} # dictionary containing the description to build a pytables table
original_data_type_dict = {} # dictionary containing the original data types
| python | {
"resource": ""
} |
q274181 | HDF5StorageService._all_get_table_col | test | def _all_get_table_col(self, key, column, fullname):
""" Creates a pytables column instance.
The type of column depends on the type of `column[0]`.
Note that data in `column` must be homogeneous!
"""
val = column[0]
try:
# # We do not want to loose int_
if type(val) is int:
return pt.IntCol()
if isinstance(val, (str, bytes)):
itemsize = int(self._prm_get_longest_stringsize(column))
| python | {
"resource": ""
} |
q274182 | HDF5StorageService._prm_get_longest_stringsize | test | def _prm_get_longest_stringsize(string_list):
""" Returns the longest string size for a string entry across data."""
maxlength = 1
for stringar in string_list:
if isinstance(stringar, np.ndarray):
if stringar.ndim > 0:
for string in stringar.ravel():
maxlength = max(len(string), maxlength)
else:
| python | {
"resource": ""
} |
q274183 | HDF5StorageService._prm_load_into_dict | test | def _prm_load_into_dict(self, full_name, load_dict, hdf5_group, instance,
load_only, load_except, load_flags, _prefix = ''):
"""Loads into dictionary"""
for node in hdf5_group:
load_type = self._all_get_from_attrs(node, HDF5StorageService.STORAGE_TYPE)
if _prefix:
load_name = '%s.%s' % (_prefix, node._v_name)
else:
load_name = node._v_name
if load_type == HDF5StorageService.NESTED_GROUP:
self._prm_load_into_dict(full_name=full_name,
load_dict=load_dict,
hdf5_group=node,
instance=instance,
load_only=load_only,
load_except=load_except,
load_flags=load_flags,
_prefix=load_name)
continue
if load_only is not None:
if load_name not in load_only:
continue
else:
load_only.remove(load_name)
elif load_except is not None:
if load_name in load_except:
load_except.remove(load_name)
continue
# Recall from the hdf5 node attributes how the data was stored and reload accordingly
if load_name in load_flags:
load_type = load_flags[load_name]
if load_type == HDF5StorageService.DICT:
to_load = self._prm_read_dictionary(node, full_name)
elif load_type == HDF5StorageService.TABLE:
to_load = self._prm_read_table(node, full_name)
elif load_type in (HDF5StorageService.ARRAY, HDF5StorageService.CARRAY,
| python | {
"resource": ""
} |
q274184 | HDF5StorageService._prm_read_dictionary | test | def _prm_read_dictionary(self, leaf, full_name):
"""Loads data that was originally a dictionary when stored
:param leaf:
PyTables table containing the dictionary data
:param full_name:
Full name of the parameter or result whose data is to be loaded
:return:
Data to be loaded
"""
try:
# Load as Pbject Table
temp_table = self._prm_read_table(leaf, full_name)
# Turn the ObjectTable into a dictionary of lists (with length 1).
temp_dict = temp_table.to_dict('list')
| python | {
"resource": ""
} |
q274185 | HDF5StorageService._prm_read_shared_data | test | def _prm_read_shared_data(self, shared_node, instance):
"""Reads shared data and constructs the appropraite class.
:param shared_node:
hdf5 node storing the pandas DataFrame
:param full_name:
Full name of the parameter or result whose data is to be loaded
:return:
Data to load
"""
try:
data_type = self._all_get_from_attrs(shared_node,
HDF5StorageService.SHARED_DATA_TYPE)
| python | {
"resource": ""
} |
q274186 | HDF5StorageService._prm_read_table | test | def _prm_read_table(self, table_or_group, full_name):
"""Reads a non-nested PyTables table column by column and created a new ObjectTable for
the loaded data.
:param table_or_group:
PyTables table to read from or a group containing subtables.
:param full_name:
Full name of the parameter or result whose data is to be loaded
:return:
Data to be loaded
"""
try:
result_table = None
if self._all_get_from_attrs(table_or_group, HDF5StorageService.SPLIT_TABLE):
table_name = table_or_group._v_name
data_type_table_name = table_name + '__' + HDF5StorageService.STORAGE_TYPE
data_type_table = table_or_group._v_children[data_type_table_name]
data_type_dict = {}
for row in data_type_table:
fieldname = row['field_name'].decode('utf-8')
data_type_dict[fieldname] = row['data_type'].decode('utf-8')
for sub_table in table_or_group:
sub_table_name = sub_table._v_name
if sub_table_name == data_type_table_name:
continue
for colname in sub_table.colnames:
# Read Data column by column
col = sub_table.col(colname)
data_list = list(col)
prefix = HDF5StorageService.FORMATTED_COLUMN_PREFIX % colname
for idx, data in enumerate(data_list):
# Recall original type of data
data, type_changed = self._all_recall_native_type(data,
PTItemMock(
data_type_dict),
prefix)
if type_changed:
data_list[idx] = data
else:
break
# Construct or insert into an ObjectTable
if result_table is None:
result_table = ObjectTable(data={colname: data_list})
| python | {
"resource": ""
} |
q274187 | HDF5StorageService._prm_read_array | test | def _prm_read_array(self, array, full_name):
"""Reads data from an array or carray
:param array:
PyTables array or carray to read from
:param full_name:
Full name of the parameter or result whose data is to be loaded
:return:
Data to load
"""
try:
result = self._svrc_read_array(array)
# Recall original data types
result, dummy = self._all_recall_native_type(result, array, | python | {
"resource": ""
} |
q274188 | load_trajectory | test | def load_trajectory(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,
recursive=True,
load_data=None,
max_depth=None,
force=False,
dynamic_imports=None,
new_name='my_trajectory',
add_time=True,
wildcard_functions=None,
with_run_information=True,
storage_service=storage.HDF5StorageService,
**kwargs):
"""Helper function that creates a novel trajectory and loads it from disk.
For the parameters see :func:`~pypet.trajectory.Trajectory.f_load`.
``new_name`` and ``add_time`` are only used in case ``as_new`` is ``True``.
Accordingly, they determine the new name of trajectory.
"""
if name is None and index is None:
raise ValueError('Please specify either a name or an index')
elif name is not None and index is not None:
raise ValueError('Please specify either a name or an index')
| python | {
"resource": ""
} |
q274189 | make_set_name | test | def make_set_name(idx):
"""Creates a run set name based on ``idx``"""
GROUPSIZE = 1000
set_idx = idx // GROUPSIZE
if set_idx >= 0:
| python | {
"resource": ""
} |
q274190 | Trajectory.f_set_properties | test | 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:
| python | {
"resource": ""
} |
q274191 | Trajectory.f_add_to_dynamic_imports | test | 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)):
| python | {
"resource": ""
} |
q274192 | Trajectory.f_set_crun | test | 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
| python | {
"resource": ""
} |
q274193 | Trajectory.f_iter_runs | test | def f_iter_runs(self, start=0, stop=None, step=1, yields='name'):
"""Makes the trajectory iterate over all runs.
:param start: Start index of run
:param stop: Stop index, leave ``None`` for length of trajectory
:param step: Stepsize
:param yields:
What should be yielded: ``'name'`` of run, ``idx`` of run
or ``'self'`` to simply return the trajectory container.
You can also pick ``'copy'`` to get **shallow** copies (ie the tree is copied but
no leave nodes except explored ones.) of your trajectory,
might lead to some of overhead.
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 | python | {
"resource": ""
} |
q274194 | Trajectory.f_shrink | test | 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 self._explored_parameters.values():
param.f_unlock()
try:
| python | {
"resource": ""
} |
q274195 | Trajectory._preset | test | 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'
| python | {
"resource": ""
} |
q274196 | Trajectory.f_preset_parameter | test | 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 | python | {
"resource": ""
} |
q274197 | Trajectory._prepare_experiment | test | def _prepare_experiment(self):
"""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(
| python | {
"resource": ""
} |
q274198 | Trajectory.f_get_from_runs | test | 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:
if len(self._run_parent_groups) > 0:
for run_name in self.f_iter_runs():
# Iterate over all runs
value = None
already_found = False
for run_parent_group in self._run_parent_groups.values():
if run_name not in run_parent_group._children:
continue
try:
value = run_parent_group.f_get(run_name + '.' + name,
fast_access=False,
with_links=with_links,
shortcuts=shortcuts,
max_depth=max_depth,
| python | {
"resource": ""
} |
q274199 | Trajectory._is_completed | test | 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(
| python | {
"resource": ""
} |
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