added docs

dev_tool/create_hhnb_dev_client.py

@@ -1,5 +1,10 @@
 #! /usr/bin/python3
 
+"""
+This python script is used to create a local OIDC client for the
+Hodgkin-Huxley Neuron Builder web application in order to perform
+the authentication process with the EBRAINS platform.
+"""
 
 import requests
 import getpass
@@ -9,6 +14,24 @@
 
 
 def get_dev_token(username, password):
+    """
+    Returns the developer token used to create the OIDC client. 
+    This is got by making a request to the EBRAINS platform providing
+    the user's username and password passed as argoument of this function.
+
+    Parameters
+    ----------
+    username : str
+        username
+    password : str
+        password
+
+    Returns
+    -------
+    str
+        the user developer token
+    """
+
     r = requests.post(
         url='https://iam.ebrains.eu/auth/realms/hbp/protocol/openid-connect/token',
         auth=('developer', ''),
@@ -31,6 +54,23 @@ def get_dev_token(username, password):
 
 
 def create_hhnb_dev_client(username, dev_token):
+    """
+    Create an EBRAINS OIDC client to perform the authentication process
+    for the Hodgkin-Huxley Neuron Builder web application. The client
+    parameters are set by default for a local environment.  
+
+    Parameters
+    ----------
+    username : str
+        username 
+    dev_token : str
+        the user developer token
+
+    Returns
+    -------
+    dict
+        the client in a json format 
+    """
 
     client = {
         'clientId': 'hhnb-%s-dev-local-client' % username,

dev_tool/create_metadata_file.py

@@ -1,3 +1,8 @@
+"""
+This script is used to create a a metadata file for a etrace.
+"""
+
+
 import os
 import json
 

dev_tool/storage_cleaner.py

@@ -1,5 +1,11 @@
 #! /usr/bin/python3
 
+"""
+This script is used to automatically clean the storage of the
+Hodgkin-Huxley Neuron Builder backend.
+"""
+
+
 import argparse
 import os
 import datetime

efelg/tools/manage_json.py

@@ -12,6 +12,9 @@
 
 # generate hash md5 code for the filename passed as parameter
 def md5(filename):
+    """
+    Returns the md5 hash for the file.
+    """
     hash_md5 = hashlib.md5()
 
     with open(filename, "rb") as f:
@@ -21,7 +24,9 @@ def md5(filename):
 
 
 def get_traces_abf(filename):
-
+    """
+    Returns a tuple of features extracted from the ".abf" trace and its metadata. 
+    """
     data = neo.io.AxonIO(filename)
     metadata = get_metadata(filename)
 
@@ -73,6 +78,9 @@ def get_traces_abf(filename):
 
 # read metadata file into a json dictionary
 def get_metadata(filename):
+    """
+    Returns the metadata dictionaire by reading the metadata file.
+    """
     filepath, name = os.path.split(filename)
     name_no_ext, extension = os.path.splitext(name)
     metadata_file = os.path.join(filepath, name_no_ext + '_metadata.json')
@@ -85,6 +93,9 @@ def get_metadata(filename):
 
 # perform units conversions
 def perform_conversions_json(data):
+    """
+    Convert some features values.
+    """
     if ("stimulus_unit" in data) and (not data["stimulus_unit"].lower() in ["na", "unknown"]):
         a_pow = 1
         stimlus_unit = data["stimulus_unit"].lower()
@@ -131,6 +142,9 @@ def perform_conversions_json(data):
 
 
 def extract_data(filepath, metadata_dict=None):
+    """
+    Returns the trace data in a json format.
+    """
     if filepath.endswith(".abf"):
         sampling_rate, tonoff, traces, voltage_unit, stimulus_unit, voltage_correction, voltage_correction_unit = get_traces_abf(filepath)
         data = {
@@ -188,13 +202,19 @@ def extract_data(filepath, metadata_dict=None):
 
 
 def create_file_name(data):
+    """
+    Generate a file name for the relative cell.
+    """
     filename_keys = [
         "animal_species", "brain_structure", "cell_soma_location", "cell_type", "etype", "cell_id", "filename"
     ]
     return '____'.join([data[key] for key in filename_keys]) + ".json"
 
 
 def update_file_name(data, metadata_dict):
+    """
+    Update the file name for the relative cell.
+    """
     metadata_keys = metadata_dict.keys()
     data["cell_id"] = metadata_dict["cell_name"] if "cell_name" in metadata_keys else metadata_dict["cell_id"]
     data["contributors_affiliations"] = metadata_dict["contributors"] if "contributors" in metadata_keys else metadata_dict["contributors_affiliations"]