Storage Services

The HDF5 Storage Service

class pypet.storageservice.HDF5StorageService(filename=None, file_title='Experiment', display_time=30)

Storage Service to handle the storage of a trajectory/parameters/results into hdf5 files.

Normally you do not interact with the storage service directly but via the trajectory, see pypet.trajectory.Trajectory.f_store() and pypet.trajectory.Trajectory.f_load().

The service is not thread safe. For multiprocessing the service needs to be wrapped either by the LockWrapper or with a combination of QueueStorageServiceSender and QueueStorageServiceWriter.

The storage service supports two operations store and load.

Requests for these two are always passed as msg, what_to_store_or_load, *args, **kwargs

For example:

>>> HDF5StorageService.load(pypetconstants.LEAF, myresult, load_only=['spikestimes','nspikes'])

For a list of supported items see store() and load().

ADD_ROW = 'ADD'

Adds a row to an overview table

REMOVE_ROW = 'REMOVE'

Removes a row from an overview table

MODIFY_ROW = 'MODIFY'

Changes a row of an overview table

COLL_TYPE = 'COLL_TYPE'

Type of a container stored to hdf5, like list,tuple,dict,etc

Must be stored in order to allow perfect reconstructions.

COLL_LIST = 'COLL_LIST'

Container was a list

COLL_TUPLE = 'COLL_TUPLE'

Container was a tuple

COLL_NDARRAY = 'COLL_NDARRAY'

Container was a numpy array

COLL_MATRIX = 'COLL_MATRIX'

Container was a numpy matrix

COLL_DICT = 'COLL_DICT'

Container was a dictionary

COLL_SCALAR = 'COLL_SCALAR'

No container, but the thing to store was a scalar

SCALAR_TYPE = 'SCALAR_TYPE'

Type of scalars stored into a container

NAME_TABLE_MAPPING = {'_overview_explored_parameters': 'explored_parameters', '_overview_parameters': 'parameters', '_overview_derived_parameters_trajectory': 'derived_parameters_trajectory', '_overview_config': 'config', '_overview_results_runs': 'results_runs', '_overview_results_trajectory': 'results_trajectory', '_overview_derived_parameters_runs_summary': 'derived_parameters_runs_summary', '_overview_derived_parameters_runs': 'derived_parameters_runs', '_overview_results_runs_summary': 'results_runs_summary'}

Mapping of trajectory config names to the tables

PR_ATTR_NAME_MAPPING = {'_derived_parameters_per_run': 'derived_parameters_per_run', '_purge_duplicate_comments': 'purge_duplicate_comments', '_overview_explored_parameters_runs': 'explored_parameters_runs', '_results_per_run': 'results_per_run'}

Mapping of Attribute names for hdf5_settings table

ATTR_LIST = ['complevel', 'complib', 'shuffle', 'fletcher32', 'pandas_format', 'pandas_append', 'encoding']

List of HDF5StorageService Attributes that have to be stored into the hdf5_settings table

STORAGE_TYPE = 'SRVC_STORE'

Flag, how data was stored

ARRAY = 'ARRAY'

Stored as array

CARRAY = 'CARRAY'

Stored as carray

EARRAY = 'EARRAY'

Stored as earray

Not supported yet, maybe in near future.

VLARRAY = 'VLARRAY'

Stored as vlarray

Not supported yet, maybe in near future.

DICT = 'DICT'

Stored as dict.

In fact, stored as pytable, but the dictionary wil be reconstructed.

TABLE = 'TABLE'

Stored as pytable

FRAME = 'FRAME'

Stored as pandas DataFrame

SERIES = 'SERIES'

Store data as pandas Series

PANEL = 'PANEL'

Store data as pandas Panel(4D)

SPLIT_TABLE = 'SPLIT_TABLE'

If a table was split due to too many columns

DATATYPE_TABLE = 'DATATYPE_TABLE'

If a table contains the data types instead of the attrs

TYPE_FLAG_MAPPING = {<class 'DataFrame'>: 'FRAME', <Mock object at 0x7f43ce24e050>: 'ARRAY', <Mock object at 0x7f43ce24e210>: 'ARRAY', <Mock object at 0x7f43ce24e190>: 'ARRAY', <Mock object at 0x7f43ce24e090>: 'ARRAY', <Mock object at 0x7f43ce24e0d0>: 'ARRAY', <type 'list'>: 'ARRAY', <class 'Panel4D'>: 'PANEL', <Mock object at 0x7f43ce24ad10>: 'ARRAY', <Mock object at 0x7f43ce24e150>: 'ARRAY', <Mock object at 0x7f43ce24ad90>: 'ARRAY', <Mock object at 0x7f43cdfbf190>: 'CARRAY', <Mock object at 0x7f43ce24e1d0>: 'ARRAY', <Mock object at 0x7f43cdfbf1d0>: 'CARRAY', <Mock object at 0x7f43ce24e250>: 'ARRAY', <Mock object at 0x7f43ce24ae10>: 'ARRAY', <type 'str'>: 'ARRAY', <type 'tuple'>: 'ARRAY', <Mock object at 0x7f43ce24ae50>: 'ARRAY', <Mock object at 0x7f43ce24e290>: 'ARRAY', <Mock object at 0x7f43ce24e110>: 'ARRAY', <class 'pypet.parameter.ObjectTable'>: 'TABLE', <Mock object at 0x7f43ce24aed0>: 'ARRAY', <Mock object at 0x7f43ce24add0>: 'ARRAY', <type 'dict'>: 'DICT', <Mock object at 0x7f43ce24af10>: 'ARRAY', <Mock object at 0x7f43ce24af50>: 'ARRAY', <Mock object at 0x7f43ce24ae90>: 'ARRAY', <Mock object at 0x7f43ce24af90>: 'ARRAY', <class 'Panel'>: 'PANEL', <type 'unicode'>: 'ARRAY', <Mock object at 0x7f43ce24afd0>: 'ARRAY', <class 'Series'>: 'SERIES'}

Mapping from object type to storage flag

FORMATTED_COLUMN_PREFIX = 'SRVC_COLUMN_%s_'

Stores data type of a specific pytables column for perfect reconstruction

DATA_PREFIX = 'SRVC_DATA_'

Stores data type of a pytables carray or array for perfect reconstruction

ANNOTATION_PREFIX = 'SRVC_AN_'

Prefix to store annotations as node attributes

ANNOTATED = 'SRVC_ANNOTATED'

Whether an item was annotated

INIT_PREFIX = 'SRVC_INIT_'

Hdf5 attribute prefix to store class name of parameter or result

CLASS_NAME = 'SRVC_INIT_CLASS_NAME'

Name of a parameter or result class, is converted to a constructor

COMMENT = 'SRVC_INIT_COMMENT'

Comment of parameter or result

LENGTH = 'SRVC_INIT_LENGTH'

Length of a parameter if it is explored

LEAF = 'SRVC_LEAF'

Whether an hdf5 node is a leaf node

encoding

How unicode strings are encoded

display_time

Time interval in seconds, when to display the storage or loading of nodes

complib

Compression library used

complevel

Compression level used

fletcher32

Whether fletcher 32 should be used

shuffle

Whether shuffle filtering should be used

pandas_append

If pandas should create storage in append mode

pandas_format

Format of pandas data. Applicable formats are ‘table’ (or ‘t’) and ‘fixed’ (or ‘f’)

filename

The name and path of the underlying hdf5 file.

load(msg, stuff_to_load, *args, **kwargs)

Loads a particular item from disk.

The storage service always accepts these parameters:

Parameters:

• trajectory_name – Name of current trajectory and name of top node in hdf5 file.
• 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.
• 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:

• 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:

  pypet.pypetconstants.LOAD_NOTHING: (0)

  Nothing is loaded

  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.

  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.

  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.

• pypet.pypetconstants.LEAF (‘LEAF’)

  Loads a parameter or result.

  param stuff_to_load:  

  The item to be loaded

  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.

• pypet.pypetconstants.TREE (‘TREE’)

  Loads a whole subtree

  param stuff_to_load:  The parent node (!) not the one where loading starts! param child_name:  Name of child node that should be loaded param recursive:  Whether to load recursively the subtree below child param load_data:  How to load stuff, accepted values as above for loading the trajectory param trajectory:  The trajectory object

• pypet.pypetconstants.LIST (‘LIST’)

  Analogous to storing lists

Raises:

NoSuchServiceError if message or data is not understood

DataNotInStorageError if data to be loaded cannot be found on disk

store(msg, stuff_to_store, *args, **kwargs)

Stores a particular item to disk.

The storage service always accepts these parameters:

Parameters:

• trajectory_name – Name or current trajectory and name of top node in hdf5 file
• filename – Name of the hdf5 file
• 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:

• 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

• 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.

• 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.

• 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.

• pypet.pypetconstants.SINGLE_RUN (‘SINGLE_RUN’)

  param stuff_to_store:  The single run to be stored param store_data:  If all data below run_XXXXXXXX should be stored param store_final:  If final meta info should be stored

• pypet.pypetconstants.LEAF

  Stores a parameter or result.

  Modification of results is not supported (yet). Everything stored to disk is set in stone!

  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
  • ObjectTable

  The keys from the ‘_store’ dictionaries determine how the data will be named in the hdf5 file.

  param store_flags:  

  Flags describing how to store data.

  ARRAY (‘ARRAY’)

  Store stuff as array

  CARRAY (‘CARRAY’)

  Store stuff as carray

  TABLE (‘TABLE’)

  Store stuff as pytable

  DICT (‘DICT’)

  Store stuff as pytable but reconstructs it later as dictionary on loading

  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 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.

• pypet.pypetconstants.DELETE (‘DELETE’)

  Removes an item from disk. Empty group nodes, results and non-explored parameters can be removed.

  param stuff_to_store:  The item to be removed. param remove_empty_groups:  Whether to also remove groups that become empty due to removal. default is False. param delete_only:  Potential list of parts of a leaf node that should be deleted. param remove_from_item:  If delete_only is used, whether deleted nodes should also be erased from the leaf nodes themseleves.

• pypet.pypetconstants.GROUP (‘GROUP’)

  param stuff_to_store:  The group to store

• pypet.pypetconstants.TREE

  Stores a single node or a full subtree

  param stuff_to_store:  Node to store param recursive:  Whether to store recursively the whole sub-tree

• pypet.pypetconstants.LIST

  Stores several items at once

  param stuff_to_store:  Iterable whose items are to be stored. Iterable must contain tuples, for example [(msg1,item1,arg1,kwargs1),(msg2,item2,arg2,kwargs2),...]

Raises:NoSuchServiceError if message or data is not understood

item

alias of str

The Multiprocessing Wrappers

class pypet.storageservice.LockWrapper(storage_service, lock)

For multiprocessing in WRAP_MODE_LOCK mode, augments a storage service with a lock.

The lock is acquired before storage or loading and released afterwards.

load(*args, **kwargs)

Acquires a lock before loading and releases it afterwards.

store(*args, **kwargs)

Acquires a lock before storage and releases it afterwards.

class pypet.storageservice.QueueStorageServiceSender

For multiprocessing with WRAP_MODE_QUEUE, replaces the original storage service.

All storage requests are send over a queue to the process running the QueueStorageServiceWriter.

Does not support loading of data!

queue

The queue to put data on

send_done()

Signals the writer that it can stop listening to the queue

store(*args, **kwargs)

Puts data to store on queue.

class pypet.storageservice.QueueStorageServiceWriter(storage_service, queue)

Wrapper class that listens to the queue and stores queue items via the storage service.

run()

Starts listening to the queue.

Empty Storage Service for Debugging

class pypet.storageservice.LazyStorageService

This lazy guy does nothing! Only for debugging purposes.

Ignores all storage and loading requests and simply executes pass instead.

load(*args, **kwargs)

Nope, I won’t care, dude!

store(*args, **kwargs)

Do whatever you want, I won’t store anything!