nrv.backend

Description

backend: code for IO, machine and classes handling

the backend sub-package is dedicated to handling the magic behind all NRV computations. The code developed here is not meant to be used directly by the end-user, but internally to develop new functionality or maintain other sub-packages.

Classes

nrv_config(*args, **kwargs)	A unique class to handle all the configuration
NRV_Encoder(*[, skipkeys, ensure_ascii, ...])	Json encoding class, specific for NRV2 axon prevent from type error due to np.arrays solution taken as this from askpython.com
bcolors()
NRV_class()	Instanciate a basic NRV class NRV Class are empty shells, defined as abstract classes of which every class in NRV should inherite.
NRV_results([context])	Results class for NRV
sim_results([context])
NRV_simulable([t_sim, dt, record_V_mem, ...])	Generic class for all NRV simulable classes (nerve, fascicle, myelinated).
NRV_singleton	Should be used as metaclass to define singleton classes
nrv_parameters(*args, **kwargs)	A class for NRV parameters used to gather parameters

Functions

is_empty_iterable(x)	check if the object x is an empty iterable
set_idxs(_i, _n)	convert an object _i into an 1D array of index
get_query(*args, **kwgs)	Convert a list of kwargs into a str compatible with pandas.DataFrame.query
df_to(df, otype, *args, **kwgs)	convert a pandas.DataFrame to another type.
is_iterable(some_stuff)	this function chels wether or not a variable contains an iterrable
rmv_ext(fname)	return filename without extension
generate_new_fname(fname)	Prevent overwriting existing files.
create_folder(foldername[, access_rights])	create a folder with controled access rights.
check_json_fname(fname)	Add ".json" extension is missing at the end of the file name and check if it exists.
json_dump(results, filename)	save stuff as a json file
json_load(filename)	Load stuff from a json file
load_dxf_file(filename)	UNDER DEV
set_attributes(my_object, attributes_dict)
check_function_kwargs(func, kwargs)	check that the keys of a dictionnary are arguments of a function and return an updated dictionnary with only valide keys
function_to_str(func)
str_to_function(lines)
init_reporter()
set_log_level(level[, clear_log_file])
rise_error(*args[, out])	Rises and error to the log and to the prompt for the master process (process ID 0) in case of parallel computing.
rise_warning(*args[, abort])	Rises a warning to the log and to the prompt for the master process (process ID 0) in case of parallel computing.
pass_info(*args, **kwargs)	Pass an info to the log and to the prompt for the master process (process ID 0) in case of parallel computing.
pass_debug_info(*args, **kwargs)	Pass an info to the log and to the prompt for the master process (process ID 0) in case of parallel computing.
progression_popup(current, max_iter[, ...])	Displays the progression on prompt, for single process only, nothing saved to the log
prompt_debug(*args)	outputs for debug, simple redirection to icecream without the installation (see icecream help).
clear_prompt_line([n])	Clear lines of the prompt, to overwrite stuffs.
is_NRV_class(x)	Check if the object x is a NRV_class.
is_NRV_class_list(x)	check if the object x is a list containing only NRV_class.
is_NRV_class_dict(x)	check if the object x is a dictionary containing only NRV_class.
is_NRV_dict(x)	Check if the object x is a dictionary of saved NRV_class.
is_NRV_dict_list(x)	Check if the object x is a list of dictionary of saved NRV_class.
is_NRV_dict_dict(x)	Check if the object x is a dictionary containing dictionaries of saved NRV_class.
is_NRV_object_dict(x)	Check if the object x is_NRV_dict, is_NRV_dict_list or is_NRV_dict_dict.
load_any(data, **kwargs)	loads any type of NRV object from a json file or a dictionary generated with NRV_class.save
generate_results(obj, **kwargs)	generate the proper results object depending of the obj simulated
is_NRV_simulable(x)	Check if the object x is a NRV_simulable.
simulable(x)	Check if the object x is a NRV_simulable.
ncore_autoset([nmod, optim, gmsh, ...])	Automatically set the number of cores for nmod, optim and gmsh based on the number of available cores and a threshold.