elektronn2.utils.utils_basic module

elektronn2.utils.utils_basic.get_free_cpu_count()[source]
elektronn2.utils.utils_basic.parallel_accum(func, n_ret, var_args, const_args, proc=-1, debug=False)[source]
class elektronn2.utils.utils_basic.timeit(*args, **kwargs)[source]

Bases: elektronn2.utils.utils_basic.DecoratorBase

class elektronn2.utils.utils_basic.cache(*args, **kwargs)[source]

Bases: elektronn2.utils.utils_basic.DecoratorBase

static hash_args(args)[source]
class elektronn2.utils.utils_basic.CircularBuffer(buffer_len)[source]

Bases: object

append(data)[source]
data
mean()[source]
setvals(val)[source]
class elektronn2.utils.utils_basic.AccumulationArray(right_shape=(), dtype=<type 'numpy.float32'>, n_init=100, data=None, ema_factor=0.95)[source]

Bases: object

add_offset(off)[source]
append(data)[source]
clear()[source]
data
ema
max()[source]
mean()[source]
min()[source]
sum()[source]
class elektronn2.utils.utils_basic.KDT(n_neighbors=5, radius=1.0, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=1, **kwargs)[source]

Bases: sklearn.neighbors.unsupervised.NearestNeighbors

warning_shown = False
class elektronn2.utils.utils_basic.DynamicKDT(points=None, k=1, n_jobs=-1, rebuild_thresh=100, aniso_scale=None)[source]

Bases: object

append(point)[source]
get_knn(query_points, k=None)[source]
get_radius_nn(query_points, radius)[source]
elektronn2.utils.utils_basic.import_variable_from_file(file_path, class_name)[source]
elektronn2.utils.utils_basic.pickleload(file_name)[source]

Loads all object that are saved in the pickle file. Multiple objects are returned as list.

elektronn2.utils.utils_basic.picklesave(data, file_name)[source]

Writes one or many objects to pickle file

data:
single objects to save or iterable of objects to save. For iterable, all objects are written in this order to the file.
file_name: string
path/name of destination file
elektronn2.utils.utils_basic.h5save(data, file_name, keys=None, compress=True)[source]

Writes one or many arrays to h5 file

data:
single array to save or iterable of arrays to save. For iterable all arrays are written to the file.
file_name: string
path/name of destination file
keys: string / list thereof
For single arrays this is a single string which is used as a name for the data set. For multiple arrays each dataset is named by the corresponding key. If keys is None, the dataset names created by enumeration: data%i
compress: Bool
Whether to use lzf compression, defaults to True. Most useful for label arrays.
elektronn2.utils.utils_basic.h5load(file_name, keys=None)[source]

Loads data sets from h5 file

file_name: string
destination file
keys: string / list thereof
Load only data sets specified in keys and return as list in the order of keys For a single key the data is returned directly - not as list If keys is None all datasets that are listed in the keys-attribute of the h5 file are loaded.
elektronn2.utils.utils_basic.pretty_string_ops(n)[source]

Return a humanized string representation of a large number.

elektronn2.utils.utils_basic.pretty_string_time(t)[source]

Custom printing of elapsed time

elektronn2.utils.utils_basic.makeversiondir(path, dir_name=None, cd=False)[source]
class elektronn2.utils.utils_basic.Timer(silent_all=False)[source]

Bases: object

check(name=None, silent=False)[source]
plot(accum=False)[source]
summary(silent=False, print_func=None)[source]
elektronn2.utils.utils_basic.unique_rows(a)[source]
elektronn2.utils.utils_basic.as_list(var)[source]