Merge pull request #33 from nicoloval/bug/fixing_dependencies

Bug/fixing dependencies

README.md

@@ -117,6 +117,7 @@ Simple Example
 As an example we solve the UBCM for zachary karate club network.
 
 ```
+    import numpy as np
     import networkx as nx
     from NEMtropy import UndirectedGraph
 

setup.py

@@ -1,4 +1,4 @@
-from setuptools import setup, find_packages
+from setuptools import setup
 
 
 with open('README.md', "r", encoding="utf-8") as fh:
@@ -8,16 +8,17 @@
 setup(
     name="NEMtropy",
     author="Nicolo' Vallarano, Emiliano Marchese, Matteo Bruno",
-    author_email='nicolo.vallarano@imtlucca.it, emiliano.marchese@imtlucca.it, matteo.bruno@imtlucca.it',
+    author_email='nicolo.vallarano@imtlucca.it, emiliano.marchese@imtlucca.it,'
+                 ' matteo.bruno@imtlucca.it',
     packages=["NEMtropy"],
     package_dir={'': 'src'},
     version="2.0.1",
     description="NEMtropy is a Maximum-Entropy toolbox for networks, it"
-                " provides the user with a state of the art solver for a range variety"
-                " of Maximum Entropy Networks models derived from the ERGM family."
-                " This module allows you to solve the desired model and generate a"
-                " number of randomized graphs from the original one:"
-                " the so-called graphs ensemble.",
+                " provides the user with a state of the art solver for a"
+                " range variety of Maximum Entropy Networks models derived"
+                " from the ERGM family. This module allows you to solve the"
+                " desired model and generate a number of randomized graphs"
+                " from the original one: the so-called graphs ensemble.",
     long_description=long_description,
     long_description_content_type="text/markdown",
     license="GNU General Public License v3",
@@ -26,7 +27,8 @@
     keywords=['Network reconstruction', 'Networks Null Models',
               'Maximum Entrophy Methods'],
     classifiers=[
-                'License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)',
+                'License :: OSI Approved :: GNU Library or Lesser General'
+                ' Public License (LGPL)',
                 'Programming Language :: Python :: 3',
                 'Programming Language :: Python :: 3.5',
                 'Programming Language :: Python :: 3.6',
@@ -38,7 +40,7 @@
                       "scipy>=1.4",
                       "networkx>=2.4",
                       "powerlaw>=1.4"
-                      "tqdm>=4.56.2"
+                      "tqdm>=4.5"
                       ],
     extras_require={
         "dev": [

src/NEMtropy.egg-info/requires.txt

@@ -1,6 +1,7 @@
 numpy>=1.17
 scipy>=1.4
 networkx>=2.4
+powerlaw>=1.4tqdm>=4.5
 
 [dev]
 pytest>=6.0.1

src/NEMtropy/models_functions.py

@@ -1991,10 +1991,10 @@ def linsearch_fun_DCM(xx, args):
     Alpha determines how much to move on the descending direction
     found by the algorithm.
 
-    :param X: Tuple of arguments to find alpha:
+    :param xx: Tuple of arguments to find alpha:
         solution, solution step, tuning parameter beta,
         initial alpha, function f
-    :type X: (numpy.ndarray, numpy.ndarray, float, float, func)
+    :type xx: (numpy.ndarray, numpy.ndarray, float, float, func)
     :param args: Tuple, step function and arguments.
     :type args: (func, tuple)
     :return: Working alpha.
@@ -2073,6 +2073,7 @@ def linsearch_fun_DCM_fixed(xx):
 # DECM functions
 # --------------
 
+
 @jit(nopython=True)
 def iterative_decm(x, args):
     """Returns the next iterative step for the DECM Model.
@@ -2567,10 +2568,10 @@ def linsearch_fun_DECM(xx, args):
     Alpha determines how much to move on the descending direction
     found by the algorithm.
 
-    :param X: Tuple of arguments to find alpha:
+    :param xx: Tuple of arguments to find alpha:
         solution, solution step, tuning parameter beta,
         initial alpha, function f
-    :type X: (numpy.ndarray, numpy.ndarray, float, float, func)
+    :type xx: (numpy.ndarray, numpy.ndarray, float, float, func)
     :param args: Tuple, step function and arguments.
     :type args: (func, tuple)
     :return: Working alpha.
@@ -2627,10 +2628,10 @@ def linsearch_fun_DECM_fixed(xx):
     Alpha determines how much to move on the descending direction
     found by the algorithm.
 
-    :param X: Tuple of arguments to find alpha:
+    :param xx: Tuple of arguments to find alpha:
         solution, solution step, tuning parameter beta,
         initial alpha, step.
-    :type X: (numpy.ndarray, numpy.ndarray, float, float, int)
+    :type xx: (numpy.ndarray, numpy.ndarray, float, float, int)
     :return: Working alpha.
     :rtype: float
     """
@@ -3147,8 +3148,8 @@ def expected_in_degree_dcm_exp(theta):
     """Expected in-degrees after the DBCM. It is based on DBCM
     exponential version.
 
-    :param sol: DBCM solution.
-    :type sol: numpy.ndarray
+    :param theta: DBCM solution.
+    :type theta: numpy.ndarray
     :return: In-degrees DBCM expectation.
     :rtype: numpy.ndarray
     """
@@ -3381,8 +3382,8 @@ def loglikelihood_decm_exp(x, args):
     """Returns DECM [*]_ loglikelihood function evaluated in theta.
     It is based on the exponential version of the DECM.
 
-    :param theta: Evaluating point *theta*.
-    :type theta: numpy.ndarray
+    :param x: Evaluating point *theta*.
+    :type x: numpy.ndarray
     :param args: Arguments to define the loglikelihood function.
         Out and in degrees sequences, and out and in strengths sequences
     :type args: (numpy.ndarray, numpy.ndarray, numpy.ndarray,
@@ -3653,7 +3654,7 @@ def expected_decm_exp(theta):
     It is based on DBCM exponential version.
 
     :param theta: DBCM solution.
-    :type x: numpy.ndarray
+    :type theta: numpy.ndarray
     :return: DBCM expected parameters sequence.
     :rtype: numpy.ndarray
     """