Neurodamus.utils Sub-Modules

neurodamus.utils.compat

Compatibility Classes to work similar to HOC types, recreating or wrapping them

Classes

List([iterable])

Behavior similar to Hoc List

Vector([type_, array])

Behavior similar to Hoc Vector
class neurodamus.utils.compat.List(iterable=(), /)

Behavior similar to Hoc List

__annotate_func__ = None
__annotations_cache__ = {}
__firstlineno__ = 28
__module__ = 'neurodamus.utils.compat'
__slots__ = ()
__static_attributes__ = ()
count(obj=None)

Return number of occurrences of value.

o(idx)
class neurodamus.utils.compat.Vector(type_='I', array=None)

Behavior similar to Hoc Vector

__add__(other)

Return self+value.

__annotate_func__ = None
__annotations_cache__ = {}
__firstlineno__ = 6
__module__ = 'neurodamus.utils.compat'
static __new__(cls, type_='I', array=None)
__slots__ = ()
__static_attributes__ = ()
size()
property x
neurodamus.utils.compat.hoc_vector(np_array)

neurodamus.utils.logging

Loggeers init & Formatters

Functions

log_stage(msg, *args)

Shortcut to log a messge with the STAGE level

log_verbose(msg, *args)

Shortcut to log a messge with the VERBOSE level

setup_logging(loglevel[, logfile, rank, ...])

Setup neurodamus logging.
neurodamus.utils.logging.log_all(level, msg, *args)

Like logging.log, but always displays. Level is used for style only

neurodamus.utils.logging.log_stage(msg, *args)

Shortcut to log a messge with the STAGE level

neurodamus.utils.logging.log_verbose(msg, *args)

Shortcut to log a messge with the VERBOSE level

neurodamus.utils.logging.setup_logging(loglevel, logfile=None, rank=None, use_color=True)

Setup neurodamus logging. Features tabs and colors output to stdout and pydamus.log

Parameters:

  • loglevel (int) – minimum loglevel for emitting messages

  • logfile – The destination for log messages besides stdout

  • rank – A tag so warnings/errors are correctly identified in case of MPI

neurodamus.utils.multimap

A collection of Pure-Python MultiMaps

Classes

GroupedMultiMap(np_keys, values)

A Multimap which groups values by key in a list.
class neurodamus.utils.multimap.GroupedMultiMap(np_keys, values)

A Multimap which groups values by key in a list.

exists(key)
find(key)
get(k[, d]) → D[k] if k in D, else d.  d defaults to None.
get_items(key)
items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
static sort_together(np_keys, values)
values() → an object providing a view on D's values

neurodamus.utils.progressbar

Classes to represent a task’s progress in the form of a progress bar.

Here is some basic usage with the default options:

>>> from progressbar import ProgressBar
>>> p = ProgressBar()
>>> print p
[>............] 0%
>>> p + 1
>>> print p
[=>...........] 10%
>>> p + 9
>>> print p
[============>] 0%

And here another example with different options:

>>> from progressbar import ProgressBar
>>> custom_options = {
...     'end': 100,
...     'width': 20,
...     'fill': '#',
...     'format': '%(progress)s%% [%(fill)s%(blank)s]'
... }
>>> p = ProgressBar(**custom_options)
>>> print p
0% [....................]
>>> p + 5
>>> print p
5% [#...................]
>>> p + 9
>>> print p
100% [####################]

Classes

Progress(end[, start])

Progress class holds the progress information.

ProgressBar(end[, start, width, fill, ...])

ProgressBar implements a fully visual text-based representation of a progress.
class neurodamus.utils.progressbar.Progress(end, start=0, **kw)

Progress class holds the progress information.

It can be queried for the current progress, and overloads __repr__ for a simple display. It can be managed manually, via ‘+=’ and -= operators, or automatically by consuming iterables.

property completion_ratio
classmethod iter(iterable, end=None, start=0, **kw)

Consumes (a slice of) an iterable.

Parameters:

  • iterable – the iterable to consume and monitor progress

  • end – The end index. Alternatively None will automatically detect size, while False instructs to not compute size -> spinner

  • start – in which position to start iterating

property progress
reset()

Resets the current progress to the start point

property time_taken
class neurodamus.utils.progressbar.ProgressBar(end, start=0, width=60, fill='=', blank='.', stream=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>, clear=None, fmt='[%(fill)s>%(blank)s] %(progress)s', tty_bar=None, name='')

ProgressBar implements a fully visual text-based representation of a progress. and may be any file-object to which send the progress status.

property progress
show_progress()

neurodamus.utils.pyutils

Collection of generic Python utilities.

Functions

bin_search(container, key[, keyf])

Performs binary search in a container, retrieving the index where key should be inserted to keep ordering.

docopt_sanitize(docopt_opts)

Sanitizes docopt parsed key names

Classes

ConfigT([opt_dict])

Base class for configurations.

ConsoleColors()

Helper class for formatting console text.

WeakList([iterable])

dict_filter_map(dic, mapp)

Filters a dict and converts the keys according to a given map
class neurodamus.utils.pyutils.ConfigT(opt_dict=None, **opts)

Base class for configurations.

This class serves as a base for set of configurations. By inheriting and setting several class-level attributes, instances will be able to initialize from kwargs and dictionaries with the same keys, effectively working as validators of fields with default values. Furthermore, for validation of values, the attributes may be Enums.

class RunConfig(ConfigT):
   # NOTE: Enum fields: the FIRST value is the default
   mode = Enum("Mode", "BUILD_SIMULATE BUILD_ONLY")
   model_path = None
REQUIRED = <neurodamus.utils.pyutils.ConfigT._ConfigFlag object>
property all
as_dict(subset=None, excludes=())
get(*args)
class neurodamus.utils.pyutils.ConsoleColors

Helper class for formatting console text.

BLACK = 30
BLUE = 34
BOLD = 256
CYAN = 36
DEFAULT = 39
DIM = 512
GREEN = 32
HIDDEN = 1536
INVERTED = 1280
MAGENTA = 35
NORMAL = 0
RED = 31
UNDERLINED = 768
WHITE = 37
YELLOW = 33
classmethod format_text(text, color, style=None)
classmethod reset()
exception neurodamus.utils.pyutils.CumulativeError(errors=None)
append(func_name, err)
raise_if_any()
class neurodamus.utils.pyutils.StrEnumBase(new_class_name, /, names, *, module=None, qualname=None, type=None, start=1, boundary=None)
classmethod default()
classmethod from_string(s: str)
to_string() → str
class neurodamus.utils.pyutils.WeakList(iterable=(), /)
append(item)

Append object to the end of the list.

neurodamus.utils.pyutils.append_recarray(target_array, record)

Append a np.record to a np.recarray

Performs binary search in a container, retrieving the index where key should be inserted to keep ordering. Accepts a key function to be applied to each element of the container.

Parameters:

  • container – The container to be searched through

  • key – The key to look for

  • keyf – (Optional) the function transforming container elements into comparable keys

Returns: The position where the element is to be inserted to keep ordering.

neurodamus.utils.pyutils.cache_errors(func)

Decorator that catches exceptions and appends (func name, exception) to cumulative_error if provided.

neurodamus.utils.pyutils.dict_filter_map(dic, mapp)

Filters a dict and converts the keys according to a given map

neurodamus.utils.pyutils.docopt_sanitize(docopt_opts)

Sanitizes docopt parsed key names

neurodamus.utils.pyutils.gen_ranges(limit, blocklen, low=0, block_increase_rate=1)

Generates ranges in block intervals for a given length block_increase_rate may be >1 in case we want the block to get increasingly large

neurodamus.utils.pyutils.rmtree(path)

Recursively remove a path

Note: shutils.rmtree wouldn’t complete for directories with many files. See: https://github.com/openbraininstitute/neurodamus/pull/247/files/e9d12100b22bf512fdcd624022d9d999cb50db77#r2079776328 # noqa: E501

Source

api/subpackages/utils.rst