'resolved'

AUTHORS.rst

@@ -1,9 +1,10 @@
 EndemicPy is written (and eventually maintained) by Jonas I. Liechti
+=======
 
 Main Authors
 ````````````
 
-- Jonas I. Liechti <jonas.i.liechti@protonmail.ch> `@j-i-l <https://github.com/j-i-l>`_, Creator.
+- Jonas I. Liechti <jonas.i.liechti@gmail.com> `@j-i-l <https://github.com/j-i-l>`_, Creator.
 
 
 Patches and Suggestions

LICENSE.rst

@@ -0,0 +1,13 @@
+Copyright 2015 Jonas I. Liechti
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

README.rst

@@ -0,0 +1,10 @@
+Endemic is a python package to run diseas spreading simulations on various host structures
+=====
+
+
+Features
+--------
+
+- Static and temporal host networks
+
+- Extended .

__init__.py

@@ -0,0 +1 @@
+__author__ = 'Jonas I Liechti'

endemic/__init__.py

@@ -1,9 +1,9 @@
 __title__ = 'endemic'
-__version__ = '0.1.0'
-#__build__ =
+__version__ = '0.3.0'
+# __build__ =
 __author__ = 'Jonas I Liechti'
 __license__ = 'MIT'
-__copyright__ = 'Copyright 2016 Jonas I. Liechti'
+__copyright__ = 'Copyright 2019 Jonas I. Liechti'
 """
 
 

endemic/nw_construct/create_nw.py

@@ -0,0 +1,70 @@
+__author__ = 'Jonas I Liechti'
+#this will be used to create a command line tool, so it is of no importance here.
+from argparse import ArgumentParser
+
+from endemic.nw_construct import Graph
+
+
+def main():
+    parser = ArgumentParser(description='Generate a network following a probabilistic framework.',
+                                     epilog="Note: Only the options specific for the chosen network_type need to be\
+                                      entered."
+    )
+    parser.add_argument('n', metavar='n', type=int,
+                        help='the number of nodes in the network.')
+    parser.add_argument('--type', dest='network_type', type=str,
+                        help='chose the type of network to construct.')
+    parser.add_argument('--method', dest='method', type=str, default='stub',
+                        help="set the network construction method (stub or proba, default: stub).")
+    parser.add_argument('--file', dest='filename', type=str,
+                        help='The file where the network will be stored')
+    parser.add_argument('--format', dest='fileformat', type=str, default='edges',
+                        help="Specify the format of the destination file (default: edges). If the format is given with \
+                        the filename, this argument is ignored.")
+    parser.add_argument('--shape', dest='shape', type=float,
+                        help='The shape parameter of a distribution (for gamma, exponential and weibull)')
+    #is a for weibull
+    parser.add_argument('--centre', dest='loc', type=float,
+                        help='Centre of the normal distribution.')
+    parser.add_argument('--scale', dest='scale', type=float,
+                        help='The scale parameter of a distribution (for gamma, normal and exponential)')
+    parser.add_argument('--trials', dest='n', type=int,
+                        help='The number of trials for a binomial distribution')
+    parser.add_argument('--p', dest='p', type=float,
+                        help='The success probability (for binomial and geometric).')
+    parser.add_argument('--lambda', dest='lam', type=float,
+                        help='Expected value for the poisson distribution')
+    parser.add_argument('--1+exponent', dest='a', type=float,
+                        help='Set the exponent for a power distribution (the dist will be p(x,a)=ax^{a-1}).')
+    parser.add_argument('--l', dest='l', type=int,
+                        help='In l-partition network, the number of partitions in an l-partition graph.')
+    parser.add_argument('--avg_degree', dest='avg_degree', type=float,
+                        help="In l-partition network, the expected average degree of each node in the l-partition \
+                        network.")
+    parser.add_argument('--density_ratio', dest='density_ratio', type=float,
+                        help="In l-partition network, the egde-density ratio between inter- and intra-partition links \
+                        (eg. 2: inside twice as dense as outside)")
+    parser.add_argument('--p_in', dest='p_in', type=float,
+                        help='In l-partition network, the connection probability of any pair inside a partition.')
+    parser.add_argument('--p_out', dest='p_out', type=float,
+                        help='In l-partition network, the connection probability of any pair in-between partitions.')
+    #parser.add_argument('integer', metavar='n', type=str, nargs='+',
+    #                    help='an integer for the accumulator')
+    #parser.add_argument('--sum', dest='accumulate', action='store_const',
+    #                    const=sum, default=max,
+    #                    help='sum the integers (default: find the max)')
+
+    args = parser.parse_args()
+    as_dict = args.__dict__
+    filename = as_dict.pop('filename')
+    fileformat = as_dict.pop('fileformat')
+    if as_dict['network_type'] == 'weibull':
+        as_dict['a'] = as_dict.pop('shape')
+    if filename is None:
+        raise IOError('Please specify a destination to save the graph (--file FILENAME)')
+    for key in as_dict.keys():
+        if as_dict[key] is None:
+            as_dict.pop(key)
+    g = Graph(n=as_dict.pop('n'), method=as_dict.pop('method'), **as_dict)
+    g.export_graph(filename=filename, fileformat=fileformat)
+    return 0

endemic/nw_construct/test_neg_binom.py

@@ -0,0 +1,38 @@
+from GraphConstructor import Graph
+
+n=100000
+k=4
+var=2.0
+#distro = 'negative_binomial'
+distro = 'gamma'
+
+def get_p(k, var):
+    """ 
+        In the numpy implementation the probability of success is needed, so
+        this returns to probability of success.
+    """
+    return k/float(var)
+def get_r(k, var):
+    #return (k**2/var)*(1/(var/float(k)-1))
+    return int(k**2/(var - k))
+def get_scale(k,var):
+    return var / float(k)
+def get_shape(k,var):
+    return k ** 2 / float(var)
+distribution_params = {}
+distribution_params['network_type'] = distro
+if var== 0.0:
+    distribution_params['network_type'] = 'uniform'
+    distribution_params['degree'] = k
+elif distro == 'negative_binomial':
+    distribution_params['p'] = get_p(k,var)
+    distribution_params['n'] = get_r(k,var)
+elif distro == 'gamma':
+    distribution_params['scale'] = get_scale(k,var)
+    distribution_params['shape'] = get_shape(k,var)
+
+m_g = Graph(
+        N=n,
+        method='stub',
+        **distribution_params
+        )

endemic/nw_spread/HostStructure.py

@@ -86,6 +86,7 @@ def __init__(self, from_object, susceptible=1, is_static=True):
 
     @property
     def info(self):
+        # ToDo: would make sense to define this.
         return self.graph_info
 
     def _check_integrity(self):
@@ -193,13 +194,17 @@ def get_events(self, node_id, start_time, delta_t):
         )
         nn1 = self.node1s.view()[the_filter]
         nn2 = self.node2s.view()[the_filter]
+
         nn = np.where(
             node_id != nn1,
             nn1,
             nn2
         )
-        return nn, self.starts.view()[the_filter], \
+        return (
+            nn,
+            self.starts.view()[the_filter],
             self.stops.view()[the_filter]
+        )
 
 
 class Host():

endemic/nw_spread/RateDistribution.py

@@ -89,4 +89,4 @@ def __setstate__(self, d):
             self.queue = SimpleQueue(maxsize=self.pre + 1)
             self.v_put = vectorize(self.queue.put_nowait)  # this is specific to the queue, thus reinit here
             self.draw_fct = no_mut
-            self.fillup()
+            self.fillup()