"""The complete AlF specification descriptors and validators
TODO Currently extensions are not optional
TODO Make Subjects an optional requirement with lab, i.e. lab/Subjects/subject/date/num OR
subject/date/num but NOT lab/subject/date/num
"""
import re
import textwrap
from uuid import UUID
from typing import Union
from iblutil.util import flatten
SPEC_DESCRIPTION = {
'lab': 'The name of the lab where the data were collected',
'Subjects': 'An optional directory to indicate that the experiment data are divided by '
'subject',
'subject': 'The subject name, typically an arbitrary label',
'date': 'The date on which the experiment session took place, in ISO format, i.e. yyyy-mm-dd',
'number': 'The sequential session number of the day, optionally zero-padded to be three '
'numbers, e.g. 001, 002, etc.',
'collection': 'An optional folder to group data by modality, device, etc. This is necessary '
'when a session contains multiple measurements of the same type, from example '
'spike times from multiple probes. Label examples include "probe00", '
'"raw_video_data".',
'revision': 'An optional folder to organize data by version. The version label is arbitrary, '
'however the folder must start and end with pound signs, e.g. "#v1.0.0#". '
'Unlike collections, if a specified revision is not found, the previous revision '
'will be returned. The revisions are ordered lexicographically.',
'namespace': 'An option filename prefix for data that are not not expected to be a community '
'standard, for example task specific events. The namespace may also be used to '
'indicate data unique to a given piece of hardware or software, and is '
'identified by underscores, e.g. "_iblrig_", "_phy_".',
'object': 'Every file describing a given object has the same number of rows (i.e. the 1st '
'dimension of an npy file, number of frames in a video file, etc). You can '
'therefore think of the files for an object as together defining a table, with '
'column headings given by the attribute in the file names, and values given by the '
'file contents. Object names should be in Haskell case and pluralized, '
'e.g. "wheelMoves", "sparseNoise", "trials".\nEncoding of relations between objects '
'can be achieved by a simplified relational model. If the attribute name of one '
'file matches the object name of a second, then the first file is guaranteed to '
'contain integers referring to the rows of the second. For example, '
'"spikes.clusters.npy" would contain integer references to the rows of '
'"clusters.brain_location.json" and "clusters.probes.npy"; and '
'"clusters.probes.npy" would contain integer references to "probes.insertion.json". '
'\nBe careful of plurals ("clusters.probe.npy" would not correspond to '
'"probes.insertion.json") and remember we count arrays starting from 0.',
'attribute': 'Together with the object, the attribute represents the type of data in the '
'file, for example "times", "amplitudes", "clusters". The names should be in '
'Haskell case, however the following three attributes may be separated by an '
'underscore, e.g. "stimOn_times".\nThe attribute "times" is reserved for '
'discrete event times and comprises a numerical array containing times of the '
'events in seconds, relative to a universal timescale common to all files.\n'
'The attribute "intervals" should have two columns, indicating the start and end '
'times of each interval relative to the universal timescale.\n'
'Continuous timeseries are represented by the "timestamps" attribute. The file '
'may contain a vector of times in universal seconds if unevenly sampled, or two '
'rows each representing a synchronization point, the first column giving the '
'sample number (counting from 0), and the second column giving the '
'corresponding time in universal seconds. The times corresponding to all '
'samples are then found by linear interpolation. NB: the "timestamps" file is '
'an exception to the rule that all files representing a continuous timeseries '
'object must have one row per sample, as it will often have substantially less.',
'timescale': 'If you want to represent times relative to another (non-universal) timescale, '
'a timescale can be appended after an underscore e.g. '
'"spikes.times_ephysClock.npy", "trials.intervals_nidaq", '
'"wheel.timestamps_bpod.csv".',
'extra': 'File names could have as many optional parts as you like: '
'"object.attribute.x1.x2.[…].xN.extension". The extra name parts play no formal '
'role, but can serve several additional purposes. For example, it could be a UUID or '
'file hash for archiving purposes. If there are multiple files with the same '
'object, attribute, and extensions but different extra parts, these should be '
'treated as files to be concatenated, for example to allow multiple-part tif files '
'as produced by scanimage to be encoded in ALF. The concatenation would happen in '
'hierarchical lexicographical order: i.e. by lexicographic order of x1, '
'then x2, etc.',
'extension': 'ALF can deal with any sort of file, as long as it has a concept of a number of '
'rows (or primary dimension). The type of file is recognized by its extension. \n'
'Preferred choices:\n\n.npy: numpy array file. This is recommended over flat '
'binary since datatype and shape is stored in the file. If you have an array of '
'3 or more dimensions, the first dimension counts as the number of rows.\n\n'
'.tsv: tab-delimited text file. This is recommended over comma-separated files'
'since text fields often have commas in. All rows should have the same number '
'of columns. The first row contains tab-separated names for each column.\n\n'
'.bin: flat binary file. It’s better to use .npy for storing binary data but '
'some recording systems save in flat binary. Rather than convert them, '
'you can ALFize a flat binary file by adding a metadata file, which specifies '
'the number of columns (as the size of the "columns" array) and the binary '
'datatype as a top-level key "dtype", using numpy naming conventions.'
}
"""The following are the specifications and patterns for ALFs"""
SESSION_SPEC = '{lab}/(Subjects/)?{subject}/{date}/{number}'
COLLECTION_SPEC = r'({collection}/)?(#{revision}#/)?'
FILE_SPEC = r'_?{namespace}?_?{object}\.{attribute}_?{timescale}*\.?{extra}*\.{extension}$'
REL_PATH_SPEC = f'{COLLECTION_SPEC}{FILE_SPEC}'
FULL_SPEC = f'{SESSION_SPEC}/{REL_PATH_SPEC}'
_DEFAULT = (
('lab', r'\w+'),
('subject', r'[\w-]+'),
('date', r'\d{4}-\d{2}-\d{2}'),
('number', r'\d{1,3}'),
('collection', r'[\w/]+'),
('revision', r'[\w-]+'), # brackets
# to include underscores: r'(?P<namespace>(?:^_)\w+(?:_))?'
('namespace', '(?<=_)[a-zA-Z0-9]+'), # brackets
('object', r'\w+'),
# to treat _times and _intervals as timescale: (?P<attribute>[a-zA-Z]+)_?
# (?:_[a-z]+_)? allows attribute level namespaces (deprecated)
('attribute', r'(?:_[a-z]+_)?[a-zA-Z0-9]+(?:_times(?=[_.])|_intervals(?=[_.]))?'), # brackets
('timescale', r'(?:_?)\w+'), # brackets
('extra', r'[.\w-]+'), # brackets
('extension', r'\w+')
)
[docs]def path_pattern():
"""Returns a template string representing the where the ALF parts lie in an ALF path
Brackets denote optional parts. This is used for documentation purposes only.
"""
return ''.join(filter(lambda c: c not in '{}?*\\$', FULL_SPEC))
[docs]def describe(part=None, width=99):
"""Print a description of an ALF part
Prints the path pattern along with a description of the given ALF part (or all parts if None).
Parameters
----------
part : str
ALF part to describe. One from `SPEC_DESCRIPTION.keys()`. If None, all parts are
described.
width : int
The max line length.
Returns
-------
None
Examples
-------
>>> describe()
>>> describe('collection')
>>> describe('extension', width=120)
"""
full_spec = path_pattern()
print(full_spec)
if part:
if part not in SPEC_DESCRIPTION.keys():
all_parts = '"' + '", "'.join(SPEC_DESCRIPTION.keys()) + '"'
raise ValueError(f'Unknown ALF part "{part}", should be one of {all_parts}')
parts = [part]
span = re.search(part, full_spec).span()
' ' * len(full_spec)
print(' ' * span[0] + '^' * (span[1] - span[0]) + ' ' * (len(full_spec) - span[1]))
else:
parts = SPEC_DESCRIPTION.keys()
for part in parts:
print('\n' + part.upper())
# Split by max width
lines = flatten(textwrap.wrap(ln, width, replace_whitespace=False)
for ln in SPEC_DESCRIPTION[part].splitlines())
[print(ln) for ln in lines]
def _dromedary(string) -> str:
"""
Convert a string to camel case. Acronyms/initialisms are preserved.
Examples:
_dromedary('Hello world') == 'helloWorld'
_dromedary('motion_energy') == 'motionEnergy'
_dromedary('passive_RFM') == 'passive RFM'
_dromedary('FooBarBaz') == 'fooBarBaz'
:param string: To be converted to camel case
:return: The string in camel case
"""
def _capitalize(x):
return x if x.isupper() else x.capitalize()
if not string: # short circuit on None and ''
return string
first, *other = re.split(r'[_\s]', string)
if len(other) == 0:
# Already camel/Pascal case, ensure first letter lower case
return first[0].lower() + first[1:]
# Convert to camel case, preserving all-uppercase elements
first = first if first.isupper() else first.casefold()
return ''.join([first, *map(_capitalize, other)])
def _named(pattern, name):
"""Wraps a regex pattern in a named capture group"""
return f'(?P<{name}>{pattern})'
[docs]def regex(spec: str = FULL_SPEC, **kwargs) -> re.Pattern:
"""
Construct a regular expression pattern for parsing or validating an ALF
Parameters
----------
spec : str
The spec string to construct the regular expression from
kwargs : dict[str]
Optional patterns to replace the defaults
Returns
-------
A regular expression Pattern object
Examples
--------
# Regex for a filename
pattern = regex(spec=FILE_SPEC)
# Regex for a complete path (including root)
pattern = '.*' + regex(spec=FULL_SPEC)
# Regex pattern for specific object name
pattern = regex(object='trials)
"""
fields = dict(_DEFAULT)
if not fields.keys() >= kwargs.keys():
unknown = next(k for k in kwargs.keys() if k not in fields.keys())
raise KeyError(f'Unknown field "{unknown}"')
fields.update({k: v for k, v in kwargs.items() if v is not None})
spec_str = spec.format(**{k: _named(fields[k], k) for k in re.findall(r'(?<={)\w+', spec)})
return re.compile(spec_str)
[docs]def is_valid(filename):
"""
Returns a True for a given file name if it is an ALF file, otherwise returns False
Parameters
----------
filename : str
The name of the file to evaluate
Returns
-------
True if filename is valid ALF
Examples
--------
>>> is_valid('trials.feedbackType.npy')
True
>>> is_valid('_ns_obj.attr1.2622b17c-9408-4910-99cb-abf16d9225b9.metadata.json')
True
>>> is_valid('spike_train.npy')
False
>>> is_valid('channels._phy_ids.csv') # WARNING: attribute level namespaces are deprecated
True
"""
return regex(FILE_SPEC).match(filename) is not None
[docs]def is_session_path(path_object):
"""
Checks if the syntax corresponds to a session path. Note that there is no physical check
about existence nor contents
Parameters
----------
path_object : str, pathlib.Path
Returns
-------
True if session path a valid ALF session path
"""
session_spec = re.compile(regex(SESSION_SPEC).pattern + '$')
if hasattr(path_object, 'as_posix'):
path_object = path_object.as_posix()
path_object = path_object.strip('/')
return session_spec.search(path_object) is not None
[docs]def is_uuid_string(string: str) -> bool:
"""
Bool test for randomly generated hexadecimal uuid validity
NB: unlike is_uuid, is_uuid_string checks that uuid is correctly hyphen separated
"""
return isinstance(string, str) and is_uuid(string, (3, 4, 5)) and str(UUID(string)) == string
[docs]def is_uuid(uuid: Union[str, int, bytes, UUID], versions=(4,)) -> bool:
"""Bool test for randomly generated hexadecimal uuid validity
Unlike `is_uuid_string`, this function accepts UUID objects
"""
if not isinstance(uuid, (UUID, str, bytes, int)):
return False
elif not isinstance(uuid, UUID):
try:
uuid = UUID(uuid) if isinstance(uuid, str) else UUID(**{type(uuid).__name__: uuid})
except ValueError:
return False
return isinstance(uuid, UUID) and uuid.version in versions
[docs]def to_alf(object, attribute, extension, namespace=None, timescale=None, extra=None):
"""
Given a set of ALF file parts, return a valid ALF file name. Essential periods and
underscores are added by the function.
Parameters
----------
object : str
The ALF object name
attribute : str
The ALF object attribute name
extension : str
The file extension
namespace : str
An optional namespace
timescale : str
An optional timescale
extra : str, tuple
One or more optional extra ALF attributes
Returns
-------
A file name string built from the ALF parts
Examples
--------
>>> to_alf('spikes', 'times', 'ssv')
'spikes.times.ssv'
>>> to_alf('spikes', 'times', 'ssv', namespace='ibl')
'_ibl_spikes.times.ssv'
>>> to_alf('spikes', 'times', 'ssv', namespace='ibl', timescale='ephysClock')
'_ibl_spikes.times_ephysClock.ssv'
>>> to_alf('spikes', 'times', 'npy', namespace='ibl', timescale='ephysClock', extra='raw')
'_ibl_spikes.times_ephysClock.raw.npy'
>>> to_alf('wheel', 'timestamps', 'npy', 'ibl', 'bpod', ('raw', 'v12'))
'_ibl_wheel.timestamps_bpod.raw.v12.npy'
"""
# Validate inputs
if not extension:
raise TypeError('An extension must be provided')
elif extension.startswith('.'):
extension = extension[1:]
if re.search('_(?!times$|intervals)', attribute):
raise ValueError('Object attributes must not contain underscores')
if any(pt is not None and '.' in pt for pt in
(object, attribute, namespace, extension, timescale)):
raise ValueError('ALF parts must not contain a period (`.`)')
if '_' in (namespace or ''):
raise ValueError('Namespace must not contain extra underscores')
if object[0] == '_':
raise ValueError('Objects must not contain underscores; use namespace arg instead')
# Ensure parts are camel case (converts whitespace and snake case)
object, timescale = map(_dromedary, (object, timescale))
# Optional extras may be provided as string or tuple of strings
if not extra:
extra = ()
elif isinstance(extra, str):
extra = extra.split('.')
# Construct ALF file
parts = (('_%s_' % namespace if namespace else '') + object,
attribute + ('_%s' % timescale if timescale else ''),
*extra,
extension)
return '.'.join(parts)