rm CondaPkg environment

.CondaPkg/env/Lib/site-packages/networkx/algorithms/connectivity/connectivity.py

@@ -195,7 +195,7 @@ def local_node_connectivity(
     if mapping is None:
         raise nx.NetworkXError("Invalid auxiliary digraph.")
 
-    kwargs = dict(flow_func=flow_func, residual=residual)
+    kwargs = {"flow_func": flow_func, "residual": residual}
     if flow_func is shortest_augmenting_path:
         kwargs["cutoff"] = cutoff
         kwargs["two_phase"] = True
@@ -330,7 +330,7 @@ def node_connectivity(G, s=None, t=None, flow_func=None):
     # Reuse the auxiliary digraph and the residual network
     H = build_auxiliary_node_connectivity(G)
     R = build_residual_network(H, "capacity")
-    kwargs = dict(flow_func=flow_func, auxiliary=H, residual=R)
+    kwargs = {"flow_func": flow_func, "auxiliary": H, "residual": R}
 
     # Pick a node with minimum degree
     # Node connectivity is bounded by degree.
@@ -405,7 +405,7 @@ def average_node_connectivity(G, flow_func=None):
     # Reuse the auxiliary digraph and the residual network
     H = build_auxiliary_node_connectivity(G)
     R = build_residual_network(H, "capacity")
-    kwargs = dict(flow_func=flow_func, auxiliary=H, residual=R)
+    kwargs = {"flow_func": flow_func, "auxiliary": H, "residual": R}
 
     num, den = 0, 0
     for u, v in iter_func(G, 2):
@@ -473,7 +473,7 @@ def all_pairs_node_connectivity(G, nbunch=None, flow_func=None):
     H = build_auxiliary_node_connectivity(G)
     mapping = H.graph["mapping"]
     R = build_residual_network(H, "capacity")
-    kwargs = dict(flow_func=flow_func, auxiliary=H, residual=R)
+    kwargs = {"flow_func": flow_func, "auxiliary": H, "residual": R}
 
     for u, v in iter_func(nbunch, 2):
         K = local_node_connectivity(G, u, v, **kwargs)
@@ -631,7 +631,7 @@ def local_edge_connectivity(
     else:
         H = auxiliary
 
-    kwargs = dict(flow_func=flow_func, residual=residual)
+    kwargs = {"flow_func": flow_func, "residual": residual}
     if flow_func is shortest_augmenting_path:
         kwargs["cutoff"] = cutoff
         kwargs["two_phase"] = True
@@ -760,7 +760,7 @@ def edge_connectivity(G, s=None, t=None, flow_func=None, cutoff=None):
     # reuse auxiliary digraph and residual network
     H = build_auxiliary_edge_connectivity(G)
     R = build_residual_network(H, "capacity")
-    kwargs = dict(flow_func=flow_func, auxiliary=H, residual=R)
+    kwargs = {"flow_func": flow_func, "auxiliary": H, "residual": R}
 
     if G.is_directed():
         # Algorithm 8 in [1]

.CondaPkg/env/Lib/site-packages/networkx/algorithms/connectivity/cuts.py

@@ -140,7 +140,7 @@ def minimum_st_edge_cut(G, s, t, flow_func=None, auxiliary=None, residual=None):
     else:
         H = auxiliary
 
-    kwargs = dict(capacity="capacity", flow_func=flow_func, residual=residual)
+    kwargs = {"capacity": "capacity", "flow_func": flow_func, "residual": residual}
 
     cut_value, partition = nx.minimum_cut(H, s, t, **kwargs)
     reachable, non_reachable = partition
@@ -281,7 +281,7 @@ def minimum_st_node_cut(G, s, t, flow_func=None, auxiliary=None, residual=None):
         raise nx.NetworkXError("Invalid auxiliary digraph.")
     if G.has_edge(s, t) or G.has_edge(t, s):
         return {}
-    kwargs = dict(flow_func=flow_func, residual=residual, auxiliary=H)
+    kwargs = {"flow_func": flow_func, "residual": residual, "auxiliary": H}
 
     # The edge cut in the auxiliary digraph corresponds to the node cut in the
     # original graph.
@@ -414,7 +414,7 @@ def minimum_node_cut(G, s=None, t=None, flow_func=None):
     # Reuse the auxiliary digraph and the residual network.
     H = build_auxiliary_node_connectivity(G)
     R = build_residual_network(H, "capacity")
-    kwargs = dict(flow_func=flow_func, auxiliary=H, residual=R)
+    kwargs = {"flow_func": flow_func, "auxiliary": H, "residual": R}
 
     # Choose a node with minimum degree.
     v = min(G, key=G.degree)
@@ -537,7 +537,7 @@ def minimum_edge_cut(G, s=None, t=None, flow_func=None):
     # reuse auxiliary digraph and residual network
     H = build_auxiliary_edge_connectivity(G)
     R = build_residual_network(H, "capacity")
-    kwargs = dict(flow_func=flow_func, residual=R, auxiliary=H)
+    kwargs = {"flow_func": flow_func, "residual": R, "auxiliary": H}
 
     # Local minimum edge cut if s and t are not None
     if s is not None and t is not None:

.CondaPkg/env/Lib/site-packages/networkx/algorithms/connectivity/disjoint_paths.py

@@ -173,9 +173,12 @@ def edge_disjoint_paths(
 
     # Compute maximum flow between source and target. Flow functions in
     # NetworkX return a residual network.
-    kwargs = dict(
-        capacity="capacity", residual=residual, cutoff=cutoff, value_only=True
-    )
+    kwargs = {
+        "capacity": "capacity",
+        "residual": residual,
+        "cutoff": cutoff,
+        "value_only": True,
+    }
     if flow_func is preflow_push:
         del kwargs["cutoff"]
     if flow_func is shortest_augmenting_path:
@@ -369,7 +372,12 @@ def node_disjoint_paths(
     else:
         cutoff = min(cutoff, possible)
 
-    kwargs = dict(flow_func=flow_func, residual=residual, auxiliary=H, cutoff=cutoff)
+    kwargs = {
+        "flow_func": flow_func,
+        "residual": residual,
+        "auxiliary": H,
+        "cutoff": cutoff,
+    }
 
     # The edge disjoint paths in the auxiliary digraph correspond to the node
     # disjoint paths in the original graph.

.CondaPkg/env/Lib/site-packages/networkx/algorithms/connectivity/edge_augmentation.py

@@ -10,7 +10,7 @@ k-edge-augmentation exists.
 See Also
 --------
 :mod:`edge_kcomponents` : algorithms for finding k-edge-connected components
-:mod:`connectivity` : algorithms for determening edge connectivity.
+:mod:`connectivity` : algorithms for determining edge connectivity.
 """
 import itertools as it
 import math
@@ -374,7 +374,7 @@ def partial_k_edge_augmentation(G, k, avail, weight=None):
 
     # Generate all edges between CCs that could not be k-edge-connected
     for cc1, cc2 in it.combinations(k_edge_subgraphs, 2):
-        for (u, v) in _edges_between_disjoint(H, cc1, cc2):
+        for u, v in _edges_between_disjoint(H, cc1, cc2):
             d = H.get_edge_data(u, v)
             edge = d.get("generator", None)
             if edge is not None:
@@ -1223,7 +1223,7 @@ def greedy_k_edge_augmentation(G, k, avail=None, weight=None, seed=None):
 
     # Incrementally add edges in until we are k-connected
     H = G.copy()
-    for (u, v) in avail_uv:
+    for u, v in avail_uv:
         done = False
         if not is_locally_k_edge_connected(H, u, v, k=k):
             # Only add edges in parts that are not yet locally k-edge-connected
@@ -1241,7 +1241,7 @@ def greedy_k_edge_augmentation(G, k, avail=None, weight=None, seed=None):
 
     # Randomized attempt to reduce the size of the solution
     _compat_shuffle(seed, aug_edges)
-    for (u, v) in list(aug_edges):
+    for u, v in list(aug_edges):
         # Don't remove if we know it would break connectivity
         if H.degree(u) <= k or H.degree(v) <= k:
             continue

.CondaPkg/env/Lib/site-packages/networkx/algorithms/connectivity/edge_kcomponents.py

@@ -239,7 +239,7 @@ def bridge_components(G):
 class EdgeComponentAuxGraph:
     r"""A simple algorithm to find all k-edge-connected components in a graph.
 
-    Constructing the AuxillaryGraph (which may take some time) allows for the
+    Constructing the auxiliary graph (which may take some time) allows for the
     k-edge-ccs to be found in linear time for arbitrary k.
 
     Notes
@@ -288,7 +288,7 @@ class EdgeComponentAuxGraph:
     >>> sorted(map(sorted, aux_graph.k_edge_components(k=4)))
     [[0], [1], [2], [3], [4], [5], [6], [7]]
 
-    The auxiliary graph is primarilly used for k-edge-ccs but it
+    The auxiliary graph is primarily used for k-edge-ccs but it
     can also speed up the queries of k-edge-subgraphs by refining the
     search space.
 

.CondaPkg/env/Lib/site-packages/networkx/algorithms/connectivity/kcomponents.py

@@ -104,7 +104,7 @@ def k_components(G, flow_func=None):
     """
     # Dictionary with connectivity level (k) as keys and a list of
     # sets of nodes that form a k-component as values. Note that
-    # k-compoents can overlap (but only k - 1 nodes).
+    # k-components can overlap (but only k - 1 nodes).
     k_components = defaultdict(list)
     # Define default flow function
     if flow_func is None:
@@ -167,7 +167,7 @@ def _consolidate(sets, k):
 
     """
     G = nx.Graph()
-    nodes = {i: s for i, s in enumerate(sets)}
+    nodes = dict(enumerate(sets))
     G.add_nodes_from(nodes)
     G.add_edges_from(
         (u, v) for u, v in combinations(nodes, 2) if len(nodes[u] & nodes[v]) >= k
@@ -178,10 +178,7 @@ def _consolidate(sets, k):
 
 def _generate_partition(G, cuts, k):
     def has_nbrs_in_partition(G, node, partition):
-        for n in G[node]:
-            if n in partition:
-                return True
-        return False
+        return any(n in partition for n in G[node])
 
     components = []
     nodes = {n for n, d in G.degree() if d > k} - {n for cut in cuts for n in cut}
@@ -198,7 +195,7 @@ def _generate_partition(G, cuts, k):
 
 
 def _reconstruct_k_components(k_comps):
-    result = dict()
+    result = {}
     max_k = max(k_comps)
     for k in reversed(range(1, max_k + 1)):
         if k == max_k:

.CondaPkg/env/Lib/site-packages/networkx/algorithms/connectivity/kcutsets.py

@@ -108,7 +108,7 @@ def all_node_cuts(G, k=None, flow_func=None):
     # Shallow copy is enough.
     original_H_pred = copy.copy(H._pred)
     R = build_residual_network(H, "capacity")
-    kwargs = dict(capacity="capacity", residual=R)
+    kwargs = {"capacity": "capacity", "residual": R}
     # Define default flow function
     if flow_func is None:
         flow_func = default_flow_func
@@ -189,7 +189,7 @@ def all_node_cuts(G, k=None, flow_func=None):
                         cutset.update((u, w) for w in original_H_pred[u] if w not in S)
                     # The edges in H that form the cutset are internal edges
                     # (ie edges that represent a node of the original graph G)
-                    if any([H_nodes[u]["id"] != H_nodes[w]["id"] for u, w in cutset]):
+                    if any(H_nodes[u]["id"] != H_nodes[w]["id"] for u, w in cutset):
                         continue
                     node_cut = {H_nodes[u]["id"] for u, _ in cutset}
 

.CondaPkg/env/Lib/site-packages/networkx/algorithms/connectivity/tests/test_connectivity.py

@@ -47,7 +47,7 @@ def test_average_connectivity():
     G2.add_edges_from([(1, 3), (1, 4), (0, 3), (0, 4), (3, 4)])
     G3 = nx.Graph()
     for flow_func in flow_funcs:
-        kwargs = dict(flow_func=flow_func)
+        kwargs = {"flow_func": flow_func}
         errmsg = f"Assertion failed in function: {flow_func.__name__}"
         assert nx.average_node_connectivity(G1, **kwargs) == 1, errmsg
         assert nx.average_node_connectivity(G2, **kwargs) == 2.2, errmsg
@@ -98,7 +98,7 @@ def test_brandes_erlebach():
         ]
     )
     for flow_func in flow_funcs:
-        kwargs = dict(flow_func=flow_func)
+        kwargs = {"flow_func": flow_func}
         errmsg = f"Assertion failed in function: {flow_func.__name__}"
         assert 3 == local_edge_connectivity(G, 1, 11, **kwargs), errmsg
         assert 3 == nx.edge_connectivity(G, 1, 11, **kwargs), errmsg

.CondaPkg/env/Lib/site-packages/networkx/algorithms/connectivity/tests/test_cuts.py

@@ -70,7 +70,7 @@ def test_brandes_erlebach_book():
         ]
     )
     for flow_func in flow_funcs:
-        kwargs = dict(flow_func=flow_func)
+        kwargs = {"flow_func": flow_func}
         errmsg = f"Assertion failed in function: {flow_func.__name__}"
         # edge cutsets
         assert 3 == len(nx.minimum_edge_cut(G, 1, 11, **kwargs)), errmsg
@@ -104,7 +104,7 @@ def test_white_harary_paper():
     for i in range(7, 10):
         G.add_edge(0, i)
     for flow_func in flow_funcs:
-        kwargs = dict(flow_func=flow_func)
+        kwargs = {"flow_func": flow_func}
         errmsg = f"Assertion failed in function: {flow_func.__name__}"
         # edge cuts
         edge_cut = nx.minimum_edge_cut(G, **kwargs)
@@ -123,7 +123,7 @@ def test_white_harary_paper():
 def test_petersen_cutset():
     G = nx.petersen_graph()
     for flow_func in flow_funcs:
-        kwargs = dict(flow_func=flow_func)
+        kwargs = {"flow_func": flow_func}
         errmsg = f"Assertion failed in function: {flow_func.__name__}"
         # edge cuts
         edge_cut = nx.minimum_edge_cut(G, **kwargs)
@@ -142,7 +142,7 @@ def test_petersen_cutset():
 def test_octahedral_cutset():
     G = nx.octahedral_graph()
     for flow_func in flow_funcs:
-        kwargs = dict(flow_func=flow_func)
+        kwargs = {"flow_func": flow_func}
         errmsg = f"Assertion failed in function: {flow_func.__name__}"
         # edge cuts
         edge_cut = nx.minimum_edge_cut(G, **kwargs)
@@ -161,7 +161,7 @@ def test_octahedral_cutset():
 def test_icosahedral_cutset():
     G = nx.icosahedral_graph()
     for flow_func in flow_funcs:
-        kwargs = dict(flow_func=flow_func)
+        kwargs = {"flow_func": flow_func}
         errmsg = f"Assertion failed in function: {flow_func.__name__}"
         # edge cuts
         edge_cut = nx.minimum_edge_cut(G, **kwargs)

.CondaPkg/env/Lib/site-packages/networkx/algorithms/connectivity/tests/test_disjoint_paths.py

@@ -67,7 +67,7 @@ def test_graph_from_pr_2053():
         ]
     )
     for flow_func in flow_funcs:
-        kwargs = dict(flow_func=flow_func)
+        kwargs = {"flow_func": flow_func}
         errmsg = f"Assertion failed in function: {flow_func.__name__}"
         # edge disjoint paths
         edge_paths = list(nx.edge_disjoint_paths(G, "A", "Z", **kwargs))
@@ -82,7 +82,7 @@ def test_graph_from_pr_2053():
 def test_florentine_families():
     G = nx.florentine_families_graph()
     for flow_func in flow_funcs:
-        kwargs = dict(flow_func=flow_func)
+        kwargs = {"flow_func": flow_func}
         errmsg = f"Assertion failed in function: {flow_func.__name__}"
         # edge disjoint paths
         edge_dpaths = list(nx.edge_disjoint_paths(G, "Medici", "Strozzi", **kwargs))
@@ -97,7 +97,7 @@ def test_florentine_families():
 def test_karate():
     G = nx.karate_club_graph()
     for flow_func in flow_funcs:
-        kwargs = dict(flow_func=flow_func)
+        kwargs = {"flow_func": flow_func}
         errmsg = f"Assertion failed in function: {flow_func.__name__}"
         # edge disjoint paths
         edge_dpaths = list(nx.edge_disjoint_paths(G, 0, 33, **kwargs))
@@ -112,7 +112,7 @@ def test_karate():
 def test_petersen_disjoint_paths():
     G = nx.petersen_graph()
     for flow_func in flow_funcs:
-        kwargs = dict(flow_func=flow_func)
+        kwargs = {"flow_func": flow_func}
         errmsg = f"Assertion failed in function: {flow_func.__name__}"
         # edge disjoint paths
         edge_dpaths = list(nx.edge_disjoint_paths(G, 0, 6, **kwargs))
@@ -127,7 +127,7 @@ def test_petersen_disjoint_paths():
 def test_octahedral_disjoint_paths():
     G = nx.octahedral_graph()
     for flow_func in flow_funcs:
-        kwargs = dict(flow_func=flow_func)
+        kwargs = {"flow_func": flow_func}
         errmsg = f"Assertion failed in function: {flow_func.__name__}"
         # edge disjoint paths
         edge_dpaths = list(nx.edge_disjoint_paths(G, 0, 5, **kwargs))
@@ -142,7 +142,7 @@ def test_octahedral_disjoint_paths():
 def test_icosahedral_disjoint_paths():
     G = nx.icosahedral_graph()
     for flow_func in flow_funcs:
-        kwargs = dict(flow_func=flow_func)
+        kwargs = {"flow_func": flow_func}
         errmsg = f"Assertion failed in function: {flow_func.__name__}"
         # edge disjoint paths
         edge_dpaths = list(nx.edge_disjoint_paths(G, 0, 6, **kwargs))
@@ -157,7 +157,7 @@ def test_icosahedral_disjoint_paths():
 def test_cutoff_disjoint_paths():
     G = nx.icosahedral_graph()
     for flow_func in flow_funcs:
-        kwargs = dict(flow_func=flow_func)
+        kwargs = {"flow_func": flow_func}
         errmsg = f"Assertion failed in function: {flow_func.__name__}"
         for cutoff in [2, 4]:
             kwargs["cutoff"] = cutoff

.CondaPkg/env/Lib/site-packages/networkx/algorithms/connectivity/tests/test_kcutsets.py

@@ -186,7 +186,7 @@ def test_articulation_points():
     Ggen = _generate_no_biconnected()
     for i in range(1):  # change 1 to 3 or more for more realizations.
         G = next(Ggen)
-        articulation_points = list({a} for a in nx.articulation_points(G))
+        articulation_points = [{a} for a in nx.articulation_points(G)]
         for cut in nx.all_node_cuts(G):
             assert cut in articulation_points
 

.CondaPkg/env/Lib/site-packages/networkx/algorithms/connectivity/utils.py

@@ -47,7 +47,7 @@ def build_auxiliary_node_connectivity(G):
         H.add_edge(f"{i}A", f"{i}B", capacity=1)
 
     edges = []
-    for (source, target) in G.edges():
+    for source, target in G.edges():
         edges.append((f"{mapping[source]}B", f"{mapping[target]}A"))
         if not directed:
             edges.append((f"{mapping[target]}B", f"{mapping[source]}A"))
@@ -80,6 +80,6 @@ def build_auxiliary_edge_connectivity(G):
     else:
         H = nx.DiGraph()
         H.add_nodes_from(G.nodes())
-        for (source, target) in G.edges():
+        for source, target in G.edges():
             H.add_edges_from([(source, target), (target, source)], capacity=1)
         return H