From 4ff613453089083094da6d911f2a4081f59d96a2 Mon Sep 17 00:00:00 2001
From: mooscalia project <93492480+mooscaliaproject@users.noreply.github.com>
Date: Sun, 16 Oct 2022 21:33:59 -0300
Subject: [PATCH 01/12] Create new metrics for rank and crowding

---
 pymoo/algorithms/moo/nsga2.py                 | 110 +----
 pymoo/cython/mnn.pyx                          | 397 ++++++++++++++++++
 pymoo/cython/pruning_cd.pyx                   | 312 ++++++++++++++
 .../survival/rank_and_crowding/__init__.py    |   1 +
 .../survival/rank_and_crowding/classes.py     | 206 +++++++++
 .../survival/rank_and_crowding/metrics.py     | 193 +++++++++
 pymoo/util/function_loader.py                 |  12 +
 pymoo/util/mnn.py                             |  67 +++
 pymoo/util/pruning_cd.py                      |  89 ++++
 tests/algorithms/test_nsga2.py                |   2 +-
 tests/misc/test_crowding_distance.py          |   2 +-
 11 files changed, 1292 insertions(+), 99 deletions(-)
 create mode 100644 pymoo/cython/mnn.pyx
 create mode 100644 pymoo/cython/pruning_cd.pyx
 create mode 100644 pymoo/operators/survival/rank_and_crowding/__init__.py
 create mode 100644 pymoo/operators/survival/rank_and_crowding/classes.py
 create mode 100644 pymoo/operators/survival/rank_and_crowding/metrics.py
 create mode 100644 pymoo/util/mnn.py
 create mode 100644 pymoo/util/pruning_cd.py

diff --git a/pymoo/algorithms/moo/nsga2.py b/pymoo/algorithms/moo/nsga2.py
index 8da9d6715..eb8f7b511 100644
--- a/pymoo/algorithms/moo/nsga2.py
+++ b/pymoo/algorithms/moo/nsga2.py
@@ -1,18 +1,17 @@
 import numpy as np
+import warnings
 
 from pymoo.algorithms.base.genetic import GeneticAlgorithm
-from pymoo.core.survival import Survival
 from pymoo.docs import parse_doc_string
 from pymoo.operators.crossover.sbx import SBX
 from pymoo.operators.mutation.pm import PM
+from pymoo.operators.survival.rank_and_crowding import RankAndCrowding
 from pymoo.operators.sampling.rnd import FloatRandomSampling
 from pymoo.operators.selection.tournament import compare, TournamentSelection
 from pymoo.termination.default import DefaultMultiObjectiveTermination
 from pymoo.util.display.multi import MultiObjectiveOutput
 from pymoo.util.dominator import Dominator
-from pymoo.util.misc import find_duplicates, has_feasible
-from pymoo.util.nds.non_dominated_sorting import NonDominatedSorting
-from pymoo.util.randomized_argsort import randomized_argsort
+from pymoo.util.misc import has_feasible
 
 
 # ---------------------------------------------------------------------------------------------------------
@@ -68,47 +67,14 @@ def binary_tournament(pop, P, algorithm, **kwargs):
 # ---------------------------------------------------------------------------------------------------------
 
 
-class RankAndCrowdingSurvival(Survival):
-
-    def __init__(self, nds=None) -> None:
-        super().__init__(filter_infeasible=True)
-        self.nds = nds if nds is not None else NonDominatedSorting()
-
-    def _do(self, problem, pop, *args, n_survive=None, **kwargs):
-
-        # get the objective space values and objects
-        F = pop.get("F").astype(float, copy=False)
-
-        # the final indices of surviving individuals
-        survivors = []
-
-        # do the non-dominated sorting until splitting front
-        fronts = self.nds.do(F, n_stop_if_ranked=n_survive)
-
-        for k, front in enumerate(fronts):
-
-            # calculate the crowding distance of the front
-            crowding_of_front = calc_crowding_distance(F[front, :])
-
-            # save rank and crowding in the individual class
-            for j, i in enumerate(front):
-                pop[i].set("rank", k)
-                pop[i].set("crowding", crowding_of_front[j])
-
-            # current front sorted by crowding distance if splitting
-            if len(survivors) + len(front) > n_survive:
-                I = randomized_argsort(crowding_of_front, order='descending', method='numpy')
-                I = I[:(n_survive - len(survivors))]
-
-            # otherwise take the whole front unsorted
-            else:
-                I = np.arange(len(front))
-
-            # extend the survivors by all or selected individuals
-            survivors.extend(front[I])
-
-        return pop[survivors]
-
+class RankAndCrowdingSurvival(RankAndCrowding):
+    
+    def __init__(self, nds=None, crowding_func="cd"):
+        warnings.warn(
+                "RankAndCrowdingSurvival is deprecated and will be removed in version 0.8.*; use RankAndCrowding operator instead, which supports several and custom crowding diversity metrics.",
+                DeprecationWarning, 2
+            )
+        super().__init__(nds, crowding_func)
 
 # =========================================================================================================
 # Implementation
@@ -123,9 +89,10 @@ def __init__(self,
                  selection=TournamentSelection(func_comp=binary_tournament),
                  crossover=SBX(eta=15, prob=0.9),
                  mutation=PM(eta=20),
-                 survival=RankAndCrowdingSurvival(),
+                 survival=RankAndCrowding(),
                  output=MultiObjectiveOutput(),
                  **kwargs):
+        
         super().__init__(
             pop_size=pop_size,
             sampling=sampling,
@@ -147,55 +114,4 @@ def _set_optimum(self, **kwargs):
             self.opt = self.pop[self.pop.get("rank") == 0]
 
 
-def calc_crowding_distance(F, filter_out_duplicates=True):
-    n_points, n_obj = F.shape
-
-    if n_points <= 2:
-        return np.full(n_points, np.inf)
-
-    else:
-
-        if filter_out_duplicates:
-            # filter out solutions which are duplicates - duplicates get a zero finally
-            is_unique = np.where(np.logical_not(find_duplicates(F, epsilon=1e-32)))[0]
-        else:
-            # set every point to be unique without checking it
-            is_unique = np.arange(n_points)
-
-        # index the unique points of the array
-        _F = F[is_unique]
-
-        # sort each column and get index
-        I = np.argsort(_F, axis=0, kind='mergesort')
-
-        # sort the objective space values for the whole matrix
-        _F = _F[I, np.arange(n_obj)]
-
-        # calculate the distance from each point to the last and next
-        dist = np.row_stack([_F, np.full(n_obj, np.inf)]) - np.row_stack([np.full(n_obj, -np.inf), _F])
-
-        # calculate the norm for each objective - set to NaN if all values are equal
-        norm = np.max(_F, axis=0) - np.min(_F, axis=0)
-        norm[norm == 0] = np.nan
-
-        # prepare the distance to last and next vectors
-        dist_to_last, dist_to_next = dist, np.copy(dist)
-        dist_to_last, dist_to_next = dist_to_last[:-1] / norm, dist_to_next[1:] / norm
-
-        # if we divide by zero because all values in one columns are equal replace by none
-        dist_to_last[np.isnan(dist_to_last)] = 0.0
-        dist_to_next[np.isnan(dist_to_next)] = 0.0
-
-        # sum up the distance to next and last and norm by objectives - also reorder from sorted list
-        J = np.argsort(I, axis=0)
-        _cd = np.sum(dist_to_last[J, np.arange(n_obj)] + dist_to_next[J, np.arange(n_obj)], axis=1) / n_obj
-
-        # save the final vector which sets the crowding distance for duplicates to zero to be eliminated
-        crowding = np.zeros(n_points)
-        crowding[is_unique] = _cd
-
-    # crowding[np.isinf(crowding)] = 1e+14
-    return crowding
-
-
 parse_doc_string(NSGA2.__init__)
diff --git a/pymoo/cython/mnn.pyx b/pymoo/cython/mnn.pyx
new file mode 100644
index 000000000..7ae772e84
--- /dev/null
+++ b/pymoo/cython/mnn.pyx
@@ -0,0 +1,397 @@
+# distutils: language = c++
+# cython: language_level=2, boundscheck=False, wraparound=False, cdivision=True
+
+# This was implemented using the full distances matrix
+# Other strategies can be more efficient depending on the population size and number of objectives
+# This approach was the most promising for N = 3
+# I believe for a large number of objectives M, some strategy based on upper bounds for distances would be helpful
+# Those interested in contributing please contact me at bruscalia12@gmail.com
+
+
+import numpy as np
+
+from libcpp cimport bool
+from libcpp.vector cimport vector
+from libcpp.set cimport set as cpp_set
+
+
+cdef extern from "math.h":
+    double HUGE_VAL
+
+
+def calc_mnn(double[:, :] X, int n_remove=0):
+
+    cdef:
+        int N, M, n
+        cpp_set[int] extremes
+        vector[int] extremes_min, extremes_max
+
+    N = X.shape[0]
+    M = X.shape[1]
+
+    if n_remove <= (N - M):
+        if n_remove < 0:
+            n_remove = 0
+        else:
+            pass
+    else:
+        n_remove = N - M
+
+    extremes_min = c_get_argmin(X)
+    extremes_max = c_get_argmax(X)
+
+    extremes = cpp_set[int]()
+
+    for n in extremes_min:
+        extremes.insert(n)
+
+    for n in extremes_max:
+        extremes.insert(n)
+    
+    X = c_normalize_array(X, extremes_max, extremes_min)
+
+    return c_calc_mnn(X, n_remove, N, M, extremes)
+
+
+def calc_2nn(double[:, :] X, int n_remove=0):
+
+    cdef:
+        int N, M, n
+        cpp_set[int] extremes
+        vector[int] extremes_min, extremes_max
+
+    N = X.shape[0]
+    M = X.shape[1]
+
+    if n_remove <= (N - M):
+        if n_remove < 0:
+            n_remove = 0
+        else:
+            pass
+    else:
+        n_remove = N - M
+
+    extremes_min = c_get_argmin(X)
+    extremes_max = c_get_argmax(X)
+
+    extremes = cpp_set[int]()
+
+    for n in extremes_min:
+        extremes.insert(n)
+
+    for n in extremes_max:
+        extremes.insert(n)
+
+    X = c_normalize_array(X, extremes_max, extremes_min)
+    
+    M = 2
+
+    return c_calc_mnn(X, n_remove, N, M, extremes)
+
+
+cdef c_calc_mnn(double[:, :] X, int n_remove, int N, int M, cpp_set[int] extremes):
+
+    cdef:
+        int n, mm, i, j, n_removed, k, MM
+        double dij
+        cpp_set[int] calc_items
+        cpp_set[int] H
+        double[:, :] D
+        double[:] d
+        int[:, :] Mnn
+    
+    #Define items to calculate distances
+    calc_items = cpp_set[int]()
+    for n in range(N):
+        calc_items.insert(n)
+    for n in extremes:
+        calc_items.erase(n)
+    
+    #Define remaining items to evaluate
+    H = cpp_set[int]()
+    for n in range(N):
+        H.insert(n)
+    
+    #Instantiate distances array
+    _D = np.empty((N, N), dtype=np.double)
+    D = _D[:, :]
+
+    #Shape of X
+    MM = X.shape[1]
+    
+    #Fill values on D
+    for i in range(N - 1):
+        D[i, i] = 0.0
+
+        for j in range(i + 1, N):
+
+            dij = 0
+            for mm in range(MM):
+                dij = dij + (X[j, mm] - X[i, mm]) * (X[j, mm] - X[i, mm])
+
+            D[i, j] = dij
+            D[j, i] = D[i, j]
+
+    D[N-1, N-1] = 0.0
+
+    #Initialize
+    n_removed = 0
+
+    #Initialize neighbors and distances
+    # _Mnn = np.full((N, M), -1, dtype=np.intc)
+    _Mnn = np.argpartition(D, range(1, M+1), axis=1)[:, 1:M+1].astype(np.intc)
+    dd = np.full((N,), HUGE_VAL, dtype=np.double)
+
+    Mnn = _Mnn[:, :]
+    d = dd[:]
+
+    #Obtain distance metrics
+    c_calc_d(d, Mnn, D, calc_items, M)
+
+    #While n_remove not acheived (no need to recalculate if only one item should be removed)
+    while n_removed < (n_remove - 1):
+
+        #Obtain element to drop
+        k = c_get_drop(d, H)
+        H.erase(k)
+
+        #Update index
+        n_removed = n_removed + 1
+
+        #Get items to be recalculated
+        calc_items = c_get_calc_items(Mnn, H, k, M)
+        for n in extremes:
+            calc_items.erase(n)
+        
+        #Fill in neighbors and distance matrix
+        c_calc_mnn_iter(
+                X,
+                Mnn,
+                D,
+                N, M,
+                calc_items,
+                H
+            )
+
+        #Obtain distance metrics
+        c_calc_d(d, Mnn, D, calc_items, M)
+
+    return dd
+
+
+cdef c_calc_mnn_iter(
+    double[:, :] X,
+    int[:, :] Mnn,
+    double[:, :] D,
+    int N, int M,
+    cpp_set[int] calc_items,
+    cpp_set[int] H
+    ):
+
+    cdef:
+        int i, j, m
+    
+    #Iterate over items to calculate
+    for i in calc_items:
+
+        #Iterate over elements in X
+        for j in H:
+
+            #Go to next if same element
+            if (j == i):
+                continue
+            
+            #Replace at least the last neighbor
+            elif (D[i, j] <= D[i, Mnn[i, M-1]]) or (Mnn[i, M-1] == -1):
+                
+                #Iterate over current values
+                for m in range(M):
+
+                    #Set to current if unassigned
+                    if (Mnn[i, m] == -1):
+
+                        #Set last neighbor to index
+                        Mnn[i, m] = j
+                        break
+
+                    #Break if checking already corresponding index
+                    elif (j == Mnn[i, m]):
+                        break
+
+                    #Distance satisfies condition
+                    elif (D[i, j] <= D[i, Mnn[i, m]]):
+                            
+                        #Replace higher values
+                        Mnn[i, m + 1:] = Mnn[i, m:-1]
+                        
+                        #Replace current value
+                        Mnn[i, m] = j
+                        break
+
+
+#Calculate crowding metric
+cdef c_calc_d(double[:] d, int[:, :] Mnn, double[:, :] D, cpp_set[int] calc_items, int M):
+
+    cdef:
+        int i, m
+    
+    for i in calc_items:
+
+        d[i] = 1
+        for m in range(M):
+            d[i] = d[i] * D[i, Mnn[i, m]]
+
+
+#Returns indexes of items to be recalculated after removal
+cdef cpp_set[int] c_get_calc_items(
+    int[:, :] Mnn,
+    cpp_set[int] H,
+    int k, int M):
+
+    cdef:
+        int i, m
+        cpp_set[int] calc_items
+    
+    calc_items = cpp_set[int]()
+
+    for i in H:
+
+        for m in range(M):
+
+            if Mnn[i, m] == k:
+
+                Mnn[i, m:-1] = Mnn[i, m + 1:]
+                Mnn[i, M-1] = -1
+
+                calc_items.insert(i)
+    
+    return calc_items
+
+
+#Returns elements to remove based on crowding metric d and heap of remaining elements H
+cdef int c_get_drop(double[:] d, cpp_set[int] H):
+
+    cdef:
+        int i, min_i
+        double min_d
+
+    min_d = HUGE_VAL
+    min_i = 0
+
+    for i in H:
+
+        if d[i] <= min_d:
+            min_d = d[i]
+            min_i = i
+    
+    return min_i
+
+
+#Elements in condensed matrix
+cdef int c_square_to_condensed(int i, int j, int N):
+
+    cdef int _i = i
+
+    if i < j:
+        i = j
+        j = _i
+
+    return N * j - j * (j + 1) // 2 + i - 1 - j
+
+
+#Returns vector of positions of minimum values along axis 0 of a 2d memoryview
+cdef vector[int] c_get_argmin(double[:, :] X):
+
+    cdef:
+        int N, M, min_i, n, m
+        double min_val
+        vector[int] indexes
+    
+    N = X.shape[0]
+    M = X.shape[1]
+
+    indexes = vector[int]()
+    
+    for m in range(M):
+
+        min_i = 0
+        min_val = X[0, m]
+
+        for n in range(N):
+
+            if X[n, m] < min_val:
+
+                min_i = n
+                min_val = X[n, m]
+        
+        indexes.push_back(min_i)
+    
+    return indexes
+
+
+#Returns vector of positions of maximum values along axis 0 of a 2d memoryview
+cdef vector[int] c_get_argmax(double[:, :] X):
+
+    cdef:
+        int N, M, max_i, n, m
+        double max_val
+        vector[int] indexes
+    
+    N = X.shape[0]
+    M = X.shape[1]
+
+    indexes = vector[int]()
+    
+    for m in range(M):
+
+        max_i = 0
+        max_val = X[0, m]
+
+        for n in range(N):
+
+            if X[n, m] > max_val:
+
+                max_i = n
+                max_val = X[n, m]
+        
+        indexes.push_back(max_i)
+    
+    return indexes
+
+
+#Performs normalization of a 2d memoryview
+cdef double[:, :] c_normalize_array(double[:, :] X, vector[int] extremes_max, vector[int] extremes_min):
+
+    cdef:
+        int N = X.shape[0]
+        int M = X.shape[1]
+        int n, m, l, u
+        double l_val, u_val, diff_val
+        vector[double] min_vals, max_vals
+    
+    min_vals = vector[double]()
+    max_vals = vector[double]()
+
+    m = 0
+    for u in extremes_max:
+        u_val = X[u, m]
+        max_vals.push_back(u_val)
+        m = m + 1
+    
+    m = 0
+    for l in extremes_min:
+        l_val = X[l, m]
+        min_vals.push_back(l_val)
+        m = m + 1
+    
+    for m in range(M):
+
+        diff_val = max_vals[m] - min_vals[m]
+        if diff_val == 0.0:
+            diff_val = 1.0
+
+        for n in range(N):
+
+            X[n, m] = (X[n, m] - min_vals[m]) / diff_val
+    
+    return X
\ No newline at end of file
diff --git a/pymoo/cython/pruning_cd.pyx b/pymoo/cython/pruning_cd.pyx
new file mode 100644
index 000000000..505c29d56
--- /dev/null
+++ b/pymoo/cython/pruning_cd.pyx
@@ -0,0 +1,312 @@
+# distutils: language = c++
+# cython: language_level=2, boundscheck=False, wraparound=False, cdivision=True
+
+import numpy as np
+
+from libcpp cimport bool
+from libcpp.vector cimport vector
+from libcpp.set cimport set as cpp_set
+
+
+cdef extern from "math.h":
+    double HUGE_VAL
+
+
+#Python definition
+def calc_pcd(double[:, :] X, int n_remove=0):
+
+    cdef:
+        int N, M, n
+        cpp_set[int] extremes
+        vector[int] extremes_min, extremes_max
+        int[:, :] I
+
+    N = X.shape[0]
+    M = X.shape[1]
+
+    if n_remove <= (N - M):
+        if n_remove < 0:
+            n_remove = 0
+        else:
+            pass
+    else:
+        n_remove = N - M
+
+    extremes_min = c_get_argmin(X)
+    extremes_max = c_get_argmax(X)
+
+    extremes = cpp_set[int]()
+
+    for n in extremes_min:
+        extremes.insert(n)
+
+    for n in extremes_max:
+        extremes.insert(n)
+
+    _I = np.argsort(X, axis=0, kind='mergesort').astype(np.intc)
+    I = _I[:, :]
+
+    X = c_normalize_array(X, extremes_max, extremes_min)
+
+    return c_calc_pcd(X, I, n_remove, N, M, extremes)
+
+
+#Returns crowding metrics with recursive elimination
+cdef c_calc_pcd(double[:, :] X, int[:, :] I, int n_remove, int N, int M, cpp_set[int] extremes):
+
+    cdef:
+        int n, n_removed, k
+        cpp_set[int] calc_items
+        cpp_set[int] H
+        double[:, :] D
+        double[:] d
+    
+    #Define items to calculate distances
+    calc_items = cpp_set[int]()
+    for n in range(N):
+        calc_items.insert(n)
+    for n in extremes:
+        calc_items.erase(n)
+    
+    #Define remaining items to evaluate
+    H = cpp_set[int]()
+    for n in range(N):
+        H.insert(n)
+
+    #Initialize
+    n_removed = 0
+
+    #Initialize neighbors and distances
+    _D = np.full((N, M), HUGE_VAL, dtype=np.double)
+    dd = np.full((N,), HUGE_VAL, dtype=np.double)
+
+    D = _D[:, :]
+    d = dd[:]
+
+    #Fill in neighbors and distance matrix
+    c_calc_pcd_iter(
+            X,
+            I,
+            D,
+            N, M,
+            calc_items,
+        )
+
+    #Obtain distance metrics
+    c_calc_d(d, D, calc_items, M)
+
+    #While n_remove not acheived
+    while n_removed < (n_remove - 1):
+
+        #Obtain element to drop
+        k = c_get_drop(d, H)
+        H.erase(k)
+
+        #Update index
+        n_removed = n_removed + 1
+
+        #Get items to be recalculated
+        calc_items = c_get_calc_items(I, k, M, N)
+        for n in extremes:
+            calc_items.erase(n)
+        
+        #Fill in neighbors and distance matrix
+        c_calc_pcd_iter(
+                X,
+                I,
+                D,
+                N, M,
+                calc_items,
+            )
+
+        #Obtain distance metrics
+        c_calc_d(d, D, calc_items, M)
+
+    return dd
+
+
+#Iterate
+cdef c_calc_pcd_iter(
+    double[:, :] X,
+    int[:, :] I,
+    double[:, :] D,
+    int N, int M,
+    cpp_set[int] calc_items,
+    ):
+
+    cdef:
+        int i, m, n, l, u
+    
+    #Iterate over items to calculate
+    for i in calc_items:
+
+        #Iterate over elements in X
+        for m in range(M):
+
+            for n in range(N):
+
+                if i == I[n, m]:
+
+                    l = I[n - 1, m]
+                    u = I[n + 1, m]
+
+                    D[i, m] = (X[u, m] - X[l, m]) / M
+
+
+#Calculate crowding metric
+cdef c_calc_d(double[:] d, double[:, :] D, cpp_set[int] calc_items, int M):
+
+    cdef:
+        int i, m
+    
+    for i in calc_items:
+
+        d[i] = 0
+        for m in range(M):
+            d[i] = d[i] + D[i, m]
+
+
+#Returns indexes of items to be recalculated after removal
+cdef cpp_set[int] c_get_calc_items(
+    int[:, :] I,
+    int k, int M, int N
+    ):
+
+    cdef:
+        int n, m
+        cpp_set[int] calc_items
+    
+    calc_items = cpp_set[int]()
+
+    #Iterate over all elements in I
+    for m in range(M):
+
+        for n in range(N):
+
+            if I[n, m] == k:
+
+                #Add to set of items to be recalculated
+                calc_items.insert(I[n - 1, m])
+                calc_items.insert(I[n + 1, m])
+
+                #Remove element from sorted array
+                I[n:-1, m] = I[n + 1:, m]
+    
+    return calc_items
+
+
+#Returns elements to remove based on crowding metric d and heap of remaining elements H
+cdef int c_get_drop(double[:] d, cpp_set[int] H):
+
+    cdef:
+        int i, min_i
+        double min_d
+
+    min_d = HUGE_VAL
+    min_i = 0
+
+    for i in H:
+
+        if d[i] <= min_d:
+            min_d = d[i]
+            min_i = i
+    
+    return min_i
+
+
+#Returns vector of positions of minimum values along axis 0 of a 2d memoryview
+cdef vector[int] c_get_argmin(double[:, :] X):
+
+    cdef:
+        int N, M, min_i, n, m
+        double min_val
+        vector[int] indexes
+    
+    N = X.shape[0]
+    M = X.shape[1]
+
+    indexes = vector[int]()
+    
+    for m in range(M):
+
+        min_i = 0
+        min_val = X[0, m]
+
+        for n in range(N):
+
+            if X[n, m] < min_val:
+
+                min_i = n
+                min_val = X[n, m]
+        
+        indexes.push_back(min_i)
+    
+    return indexes
+
+
+#Returns vector of positions of maximum values along axis 0 of a 2d memoryview
+cdef vector[int] c_get_argmax(double[:, :] X):
+
+    cdef:
+        int N, M, max_i, n, m
+        double max_val
+        vector[int] indexes
+    
+    N = X.shape[0]
+    M = X.shape[1]
+
+    indexes = vector[int]()
+    
+    for m in range(M):
+
+        max_i = 0
+        max_val = X[0, m]
+
+        for n in range(N):
+
+            if X[n, m] > max_val:
+
+                max_i = n
+                max_val = X[n, m]
+        
+        indexes.push_back(max_i)
+    
+    return indexes
+
+
+#Performs normalization of a 2d memoryview
+cdef double[:, :] c_normalize_array(double[:, :] X, vector[int] extremes_max, vector[int] extremes_min):
+
+    cdef:
+        int N = X.shape[0]
+        int M = X.shape[1]
+        int n, m, l, u
+        double l_val, u_val, diff_val
+        vector[double] min_vals, max_vals
+    
+    min_vals = vector[double]()
+    max_vals = vector[double]()
+
+    m = 0
+    for u in extremes_max:
+        u_val = X[u, m]
+        max_vals.push_back(u_val)
+        m = m + 1
+    
+    m = 0
+    for l in extremes_min:
+        l_val = X[l, m]
+        min_vals.push_back(l_val)
+        m = m + 1
+    
+    for m in range(M):
+
+        diff_val = max_vals[m] - min_vals[m]
+        if diff_val == 0.0:
+            diff_val = 1.0
+
+        for n in range(N):
+
+            X[n, m] = (X[n, m] - min_vals[m]) / diff_val
+    
+    return X
\ No newline at end of file
diff --git a/pymoo/operators/survival/rank_and_crowding/__init__.py b/pymoo/operators/survival/rank_and_crowding/__init__.py
new file mode 100644
index 000000000..9a7419410
--- /dev/null
+++ b/pymoo/operators/survival/rank_and_crowding/__init__.py
@@ -0,0 +1 @@
+from pymoo.operators.survival.rank_and_crowding.classes import RankAndCrowding, ConstrRankAndCrowding
\ No newline at end of file
diff --git a/pymoo/operators/survival/rank_and_crowding/classes.py b/pymoo/operators/survival/rank_and_crowding/classes.py
new file mode 100644
index 000000000..516a0dce3
--- /dev/null
+++ b/pymoo/operators/survival/rank_and_crowding/classes.py
@@ -0,0 +1,206 @@
+import numpy as np
+from pymoo.util.randomized_argsort import randomized_argsort
+from pymoo.util.nds.non_dominated_sorting import NonDominatedSorting
+from pymoo.core.survival import Survival, split_by_feasibility
+from pymoo.core.population import Population
+from pymoo.operators.survival.rank_and_crowding.metrics import get_crowding_function
+
+
+class RankAndCrowding(Survival):
+
+    def __init__(self, nds=None, crowding_func="cd"):
+        """
+        A generalization of the NSGA-II survival operator that ranks individuals by dominance criteria
+        and sorts the last front by some user-specified crowding metric. The default is NSGA-II's crowding distances
+        although others might be more effective.
+        
+        For many-objective problems, try using 'mnn' or '2nn'.
+        
+        For Bi-objective problems, 'pcd' is very effective.
+
+        Parameters
+        ----------
+        nds : str or None, optional
+            Pymoo type of non-dominated sorting. Defaults to None.
+
+        crowding_func : str or callable, optional
+            Crowding metric. Options are:
+            
+                - 'cd': crowding distances
+                - 'pcd' or 'pruned-cd': pruned crowding distances
+                - 'ce': crowding entropy
+                - 'mnn': M-Neaest Neighbors
+                - '2nn': 2-Neaest Neighbors
+                
+            If callable, it has the form ``fun(F, filter_out_duplicates=None, n_remove=None, **kwargs)``
+            in which F (n, m) and must return metrics in a (n,) array.
+            
+            The options 'pcd', 'cd', and 'ce' are recommended for two-objective problems, whereas 'mnn' and '2nn' for many objective.
+            When using 'pcd', 'mnn', or '2nn', individuals are already eliminated in a 'single' manner. 
+            Due to Cython implementation, they are as fast as the corresponding 'cd', 'mnn-fast', or '2nn-fast', 
+            although they can singnificantly improve diversity of solutions.
+            Defaults to 'cd'.
+        """
+        
+        crowding_func_ = get_crowding_function(crowding_func)
+
+        super().__init__(filter_infeasible=True)
+        self.nds = nds if nds is not None else NonDominatedSorting()
+        self.crowding_func = crowding_func_
+        
+    def _do(self,
+            problem,
+            pop,
+            *args,
+            n_survive=None,
+            **kwargs):
+
+        # get the objective space values and objects
+        F = pop.get("F").astype(float, copy=False)
+
+        # the final indices of surviving individuals
+        survivors = []
+
+        # do the non-dominated sorting until splitting front
+        fronts = self.nds.do(F, n_stop_if_ranked=n_survive)
+
+        for k, front in enumerate(fronts):
+
+            # current front sorted by crowding distance if splitting
+            while len(survivors) + len(front) > n_survive:
+                
+                #Define how many will be removed
+                n_remove = len(survivors) + len(front) - n_survive
+                
+                # re-calculate the crowding distance of the front
+                crowding_of_front = \
+                    self.crowding_func.do(
+                        F[front, :],
+                        n_remove=n_remove
+                    )
+                
+                I = randomized_argsort(crowding_of_front, order='descending', method='numpy')
+                
+                I = I[:-n_remove]
+                front = front[I]
+
+            # otherwise take the whole front unsorted
+            else:
+                # calculate the crowding distance of the front
+                crowding_of_front = \
+                    self.crowding_func.do(
+                        F[front, :],
+                        n_remove=0
+                    )
+
+            # save rank and crowding in the individual class
+            for j, i in enumerate(front):
+                pop[i].set("rank", k)
+                pop[i].set("crowding", crowding_of_front[j])
+
+            # extend the survivors by all or selected individuals
+            survivors.extend(front)
+
+        return pop[survivors]
+
+
+class ConstrRankAndCrowding(Survival):
+    
+    def __init__(self, nds=None, crowding_func="cd"):
+        """
+        The Rank and Crowding survival approach for handling constraints proposed on
+        GDE3 by Kukkonen, S. & Lampinen, J. (2005).
+
+        Parameters
+        ----------
+        nds : str or None, optional
+            Pymoo type of non-dominated sorting. Defaults to None.
+
+        crowding_func : str or callable, optional
+            Crowding metric. Options are:
+            
+                - 'cd': crowding distances
+                - 'pcd' or 'pruned-cd': pruned crowding distances
+                - 'ce': crowding entropy
+                - 'mnn': M-Neaest Neighbors
+                - '2nn': 2-Neaest Neighbors
+                
+            If callable, it has the form ``fun(F, filter_out_duplicates=None, n_remove=None, **kwargs)``
+            in which F (n, m) and must return metrics in a (n,) array.
+            
+            The options 'pcd', 'cd', and 'ce' are recommended for two-objective problems, whereas 'mnn' and '2nn' for many objective.
+            When using 'pcd', 'mnn', or '2nn', individuals are already eliminated in a 'single' manner. 
+            Due to Cython implementation, they are as fast as the corresponding 'cd', 'mnn-fast', or '2nn-fast', 
+            although they can singnificantly improve diversity of solutions.
+            Defaults to 'cd'.
+        """
+        
+        super().__init__(filter_infeasible=False)
+        self.nds = nds if nds is not None else NonDominatedSorting()
+        self.ranking = RankAndCrowding(nds=nds, crowding_func=crowding_func)
+    
+    def _do(self,
+            problem,
+            pop,
+            *args,
+            n_survive=None,
+            **kwargs):
+
+        if n_survive is None:
+            n_survive = len(pop)
+
+        n_survive = min(n_survive, len(pop))
+
+        #If the split should be done beforehand
+        if problem.n_constr > 0:
+
+            #Split by feasibility
+            feas, infeas = split_by_feasibility(pop, eps=0.0, sort_infeasbible_by_cv=True)
+
+            #Obtain len of feasible
+            n_feas = len(feas)
+
+            #Assure there is at least_one survivor
+            if n_feas == 0:
+                survivors = Population()
+            else:
+                survivors = self.ranking.do(problem, pop[feas], *args, n_survive=min(len(feas), n_survive), **kwargs)
+
+            #Calculate how many individuals are still remaining to be filled up with infeasible ones
+            n_remaining = n_survive - len(survivors)
+
+            #If infeasible solutions need to be added
+            if n_remaining > 0:
+                
+                #Constraints to new ranking
+                G = pop[infeas].get("G")
+                G = np.maximum(G, 0)
+                
+                #Fronts in infeasible population
+                infeas_fronts = self.nds.do(G, n_stop_if_ranked=n_remaining)
+                
+                #Iterate over fronts
+                for k, front in enumerate(infeas_fronts):
+
+                    #Save ranks
+                    pop[infeas][front].set("cv_rank", k)
+
+                    #Current front sorted by CV
+                    if len(survivors) + len(front) > n_survive:
+                        
+                        #Obtain CV of front
+                        CV = pop[infeas][front].get("CV").flatten()
+                        I = randomized_argsort(CV, order='ascending', method='numpy')
+                        I = I[:(n_survive - len(survivors))]
+
+                    #Otherwise take the whole front unsorted
+                    else:
+                        I = np.arange(len(front))
+
+                    # extend the survivors by all or selected individuals
+                    survivors = Population.merge(survivors, pop[infeas][front[I]])
+
+        else:
+            survivors = self.ranking.do(problem, pop, *args, n_survive=n_survive, **kwargs)
+
+        return survivors
diff --git a/pymoo/operators/survival/rank_and_crowding/metrics.py b/pymoo/operators/survival/rank_and_crowding/metrics.py
new file mode 100644
index 000000000..751f4fe73
--- /dev/null
+++ b/pymoo/operators/survival/rank_and_crowding/metrics.py
@@ -0,0 +1,193 @@
+import numpy as np
+from scipy.spatial.distance import pdist, squareform
+from pymoo.util.misc import find_duplicates
+from pymoo.util.function_loader import load_function
+
+
+def get_crowding_function(label):
+
+    if label == "cd":
+        fun = FunctionalDiversity(calc_crowding_distance, filter_out_duplicates=False)
+    elif (label == "pcd") or (label == "pruning-cd"):
+        fun = FunctionalDiversity(load_function("calc_pcd"), filter_out_duplicates=True)
+    elif label == "ce":
+        fun = FunctionalDiversity(calc_crowding_entropy, filter_out_duplicates=True)
+    elif label == "mnn":
+        fun = FunctionalDiversity(load_function("calc_mnn"), filter_out_duplicates=True)
+    elif label == "2nn":
+        fun = FunctionalDiversity(load_function("calc_2nn"), filter_out_duplicates=True)
+    elif hasattr(label, "__call__"):
+        fun = FunctionalDiversity(label, filter_out_duplicates=True)
+    else:
+        raise KeyError("Crwoding function not defined")
+    return fun
+    
+
+class CrowdingDiversity:
+    
+    def do(self, F, n_remove=0):
+        #Converting types Python int to Cython int would fail in some cases converting to long instead
+        n_remove = np.intc(n_remove)
+        F = np.array(F, dtype=np.double)
+        return self._do(F, n_remove=n_remove)
+    
+    def _do(self, F, n_remove=None):
+        pass
+
+
+class FunctionalDiversity(CrowdingDiversity):
+    
+    def __init__(self, function=None, filter_out_duplicates=True):
+        self.function = function
+        self.filter_out_duplicates = filter_out_duplicates
+        super().__init__()
+    
+    def _do(self, F, **kwargs):
+        
+        n_points, n_obj = F.shape
+
+        if n_points <= F.shape[1]:
+            return np.full(n_points, np.inf)
+
+        else:
+
+            if self.filter_out_duplicates:
+                # filter out solutions which are duplicates - duplicates get a zero finally
+                is_unique = np.where(np.logical_not(find_duplicates(F, epsilon=1e-32)))[0]
+            else:
+                # set every point to be unique without checking it
+                is_unique = np.arange(n_points)
+
+            # index the unique points of the array
+            _F = F[is_unique]
+            
+            _d = self.function(_F, **kwargs)
+            
+            d = np.zeros(n_points)
+            d[is_unique] = _d
+        
+        return d
+
+
+def calc_crowding_distance(F, **kwargs):
+    n_points, n_obj = F.shape
+
+    # sort each column and get index
+    I = np.argsort(F, axis=0, kind='mergesort')
+
+    # sort the objective space values for the whole matrix
+    F = F[I, np.arange(n_obj)]
+
+    # calculate the distance from each point to the last and next
+    dist = np.row_stack([F, np.full(n_obj, np.inf)]) - np.row_stack([np.full(n_obj, -np.inf), F])
+
+    # calculate the norm for each objective - set to NaN if all values are equal
+    norm = np.max(F, axis=0) - np.min(F, axis=0)
+    norm[norm == 0] = np.nan
+
+    # prepare the distance to last and next vectors
+    dist_to_last, dist_to_next = dist, np.copy(dist)
+    dist_to_last, dist_to_next = dist_to_last[:-1] / norm, dist_to_next[1:] / norm
+
+    # if we divide by zero because all values in one columns are equal replace by none
+    dist_to_last[np.isnan(dist_to_last)] = 0.0
+    dist_to_next[np.isnan(dist_to_next)] = 0.0
+
+    # sum up the distance to next and last and norm by objectives - also reorder from sorted list
+    J = np.argsort(I, axis=0)
+    cd = np.sum(dist_to_last[J, np.arange(n_obj)] + dist_to_next[J, np.arange(n_obj)], axis=1) / n_obj
+
+    return cd
+            
+
+def calc_crowding_entropy(F, **kwargs):
+    """Wang, Y.-N., Wu, L.-H. & Yuan, X.-F., 2010. Multi-objective self-adaptive differential 
+    evolution with elitist archive and crowding entropy-based diversity measure. 
+    Soft Comput., 14(3), pp. 193-209.
+
+    Parameters
+    ----------
+    F : 2d array like
+        Objective functions.
+
+    Returns
+    -------
+    ce : 1d array
+        Crowding Entropies
+    """
+    n_points, n_obj = F.shape
+
+    # sort each column and get index
+    I = np.argsort(F, axis=0, kind='mergesort')
+
+    # sort the objective space values for the whole matrix
+    F = F[I, np.arange(n_obj)]
+
+    # calculate the distance from each point to the last and next
+    dist = np.row_stack([F, np.full(n_obj, np.inf)]) - np.row_stack([np.full(n_obj, -np.inf), F])
+
+    # calculate the norm for each objective - set to NaN if all values are equal
+    norm = np.max(F, axis=0) - np.min(F, axis=0)
+    norm[norm == 0] = np.nan
+
+    # prepare the distance to last and next vectors
+    dl = dist.copy()[:-1]
+    du = dist.copy()[1:]
+    
+    #Fix nan
+    dl[np.isnan(dl)] = 0.0
+    du[np.isnan(du)] = 0.0
+    
+    #Total distance
+    cd = dl + du
+
+    #Get relative positions
+    pl = (dl[1:-1] / cd[1:-1])
+    pu = (du[1:-1] / cd[1:-1])
+
+    #Entropy
+    entropy = np.row_stack([np.full(n_obj, np.inf),
+                            -(pl * np.log2(pl) + pu * np.log2(pu)),
+                            np.full(n_obj, np.inf)])
+    
+    #Crowding entropy
+    J = np.argsort(I, axis=0)
+    _cej = cd[J, np.arange(n_obj)] * entropy[J, np.arange(n_obj)] / norm
+    _cej[np.isnan(_cej)] = 0.0
+    ce = _cej.sum(axis=1)
+
+    return ce
+
+
+def calc_mnn_fast(F, **kwargs):
+    return _calc_mnn_fast(F, F.shape[1], **kwargs)
+    
+    
+def calc_2nn_fast(F, **kwargs):
+    return _calc_mnn_fast(F, 2, **kwargs)
+
+
+def _calc_mnn_fast(F, n_neighbors, **kwargs):
+
+    # calculate the norm for each objective - set to NaN if all values are equal
+    norm = np.max(F, axis=0) - np.min(F, axis=0)
+    norm[norm == 0] = 1.0
+    
+    # F normalized
+    F = (F - F.min(axis=0)) / norm
+    
+    # Distances pairwise (Inefficient)
+    D = squareform(pdist(F, metric="sqeuclidean"))
+    
+    # M neighbors
+    M = F.shape[1]
+    _D = np.partition(D, range(1, M+1), axis=1)[:, 1:M+1]
+    
+    # Metric d
+    d = np.prod(_D, axis=1)
+    
+    # Set top performers as np.inf
+    _extremes = np.concatenate((np.argmin(F, axis=0), np.argmax(F, axis=0)))
+    d[_extremes] = np.inf
+
+    return d
diff --git a/pymoo/util/function_loader.py b/pymoo/util/function_loader.py
index 085aa41d9..c68282632 100644
--- a/pymoo/util/function_loader.py
+++ b/pymoo/util/function_loader.py
@@ -4,6 +4,7 @@
 
 
 def get_functions():
+    
     from pymoo.util.nds.fast_non_dominated_sort import fast_non_dominated_sort
     from pymoo.util.nds.efficient_non_dominated_sort import efficient_non_dominated_sort
     from pymoo.util.nds.tree_based_non_dominated_sort import tree_based_non_dominated_sort
@@ -11,6 +12,8 @@ def get_functions():
     from pymoo.util.misc import calc_perpendicular_distance
     from pymoo.util.hv import hv
     from pymoo.util.stochastic_ranking import stochastic_ranking
+    from pymoo.util.mnn import calc_mnn, calc_2nn
+    from pymoo.util.pruning_cd import calc_pcd
 
     FUNCTIONS = {
         "fast_non_dominated_sort": {
@@ -34,6 +37,15 @@ def get_functions():
         "hv": {
             "python": hv, "cython": "pymoo.cython.hv"
         },
+        "calc_mnn": {
+            "python": calc_mnn, "cython": "pymoo.cython.mnn"
+        },
+        "calc_2nn": {
+            "python": calc_2nn, "cython": "pymoo.cython.mnn"
+        },
+        "calc_pcd": {
+            "python": calc_pcd, "cython": "pymoo.cython.pruning_cd"
+        },
 
     }
 
diff --git a/pymoo/util/mnn.py b/pymoo/util/mnn.py
new file mode 100644
index 000000000..53a641ace
--- /dev/null
+++ b/pymoo/util/mnn.py
@@ -0,0 +1,67 @@
+import numpy as np
+from scipy.spatial.distance import pdist, squareform
+
+def calc_mnn(X, n_remove=0):
+    return calc_mnn_base(X, n_remove=n_remove, twonn=False)
+
+def calc_2nn(X, n_remove=0):
+    return calc_mnn_base(X, n_remove=n_remove, twonn=True)
+
+def calc_mnn_base(X, n_remove=0, twonn=False):
+
+    N = X.shape[0]
+    M = X.shape[1]
+
+    if n_remove <= (N - M):
+        if n_remove < 0:
+            n_remove = 0
+        else:
+            pass
+    else:
+        n_remove = N - M
+    
+    if twonn:
+        M = 2
+
+    extremes_min = np.argmin(X, axis=0)
+    extremes_max = np.argmax(X, axis=0)
+    
+    min_vals = np.min(X, axis=0)
+    max_vals = np.max(X, axis=0)
+
+    extremes = np.concatenate((extremes_min, extremes_max))
+    
+    X = (X - min_vals) / (max_vals - min_vals)
+    
+    H = np.arange(N)
+    
+    D = squareform(pdist(X, metric="sqeuclidean"))
+    Dnn = np.partition(D, range(1, M+1), axis=1)[:, 1:M+1]
+    d = np.product(Dnn, axis=1)
+    d[extremes] = np.inf
+    
+    n_removed = 0
+
+    #While n_remove not acheived
+    while n_removed < (n_remove - 1):
+
+        #Obtain element to drop
+        _d = d[H]
+        _k = np.argmin(_d)
+        k = H[_k]
+        H = H[H != k]
+
+        #Update index
+        n_removed = n_removed + 1
+        if n_removed == n_remove:
+            break
+
+        else:
+
+            D[:, k] = np.inf
+            Dnn[H] = np.partition(D[H], range(1, M+1), axis=1)[:, 1:M+1]
+            d[H] = np.product(Dnn[H], axis=1)
+            d[extremes] = np.inf
+    
+    return d
+
diff --git a/pymoo/util/pruning_cd.py b/pymoo/util/pruning_cd.py
new file mode 100644
index 000000000..4407a1d81
--- /dev/null
+++ b/pymoo/util/pruning_cd.py
@@ -0,0 +1,89 @@
+import numpy as np
+
+def calc_pcd(X, n_remove=0):
+
+    N = X.shape[0]
+    M = X.shape[1]
+
+    if n_remove <= (N - M):
+        if n_remove < 0:
+            n_remove = 0
+        else:
+            pass
+    else:
+        n_remove = N - M
+
+    extremes_min = np.argmin(X, axis=0)
+    extremes_max = np.argmax(X, axis=0)
+    
+    min_vals = np.min(X, axis=0)
+    max_vals = np.max(X, axis=0)
+
+    extremes = np.concatenate((extremes_min, extremes_max))
+    
+    X = (X - min_vals) / (max_vals - min_vals)
+    
+    H = np.arange(N)
+    d = np.full(N, np.inf)
+    
+    I = np.argsort(X, axis=0, kind='mergesort')
+
+    # sort the objective space values for the whole matrix
+    _X = X[I, np.arange(M)]
+
+    # calculate the distance from each point to the last and next
+    dist = np.row_stack([_X, np.full(M, np.inf)]) - np.row_stack([np.full(M, -np.inf), _X])
+
+    # prepare the distance to last and next vectors
+    dist_to_last, dist_to_next = dist, np.copy(dist)
+    dist_to_last, dist_to_next = dist_to_last[:-1], dist_to_next[1:]
+
+    # if we divide by zero because all values in one columns are equal replace by none
+    dist_to_last[np.isnan(dist_to_last)] = 0.0
+    dist_to_next[np.isnan(dist_to_next)] = 0.0
+
+    # sum up the distance to next and last and norm by objectives - also reorder from sorted list
+    J = np.argsort(I, axis=0)
+    _d = np.sum(dist_to_last[J, np.arange(M)] + dist_to_next[J, np.arange(M)], axis=1)
+    d[H] = _d
+    d[extremes] = np.inf
+    
+    n_removed = 0
+
+    #While n_remove not acheived
+    while n_removed < (n_remove - 1):
+
+        #Obtain element to drop
+        _d = d[H]
+        _k = np.argmin(_d)
+        k = H[_k]
+        
+        H = H[H != k]
+        
+        #Update index
+        n_removed = n_removed + 1
+
+        I = np.argsort(X[H].copy(), axis=0, kind='mergesort')
+
+        # sort the objective space values for the whole matrix
+        _X = X[H].copy()[I, np.arange(M)]
+
+        # calculate the distance from each point to the last and next
+        dist = np.row_stack([_X, np.full(M, np.inf)]) - np.row_stack([np.full(M, -np.inf), _X])
+
+        # prepare the distance to last and next vectors
+        dist_to_last, dist_to_next = dist, np.copy(dist)
+        dist_to_last, dist_to_next = dist_to_last[:-1], dist_to_next[1:]
+
+        # if we divide by zero because all values in one columns are equal replace by none
+        dist_to_last[np.isnan(dist_to_last)] = 0.0
+        dist_to_next[np.isnan(dist_to_next)] = 0.0
+
+        # sum up the distance to next and last and norm by objectives - also reorder from sorted list
+        J = np.argsort(I, axis=0)
+        _d = np.sum(dist_to_last[J, np.arange(M)] + dist_to_next[J, np.arange(M)], axis=1)
+        d[H] = _d
+        d[extremes] = np.inf
+    
+    return d
+
diff --git a/tests/algorithms/test_nsga2.py b/tests/algorithms/test_nsga2.py
index ce692b9c2..262dc94fa 100644
--- a/tests/algorithms/test_nsga2.py
+++ b/tests/algorithms/test_nsga2.py
@@ -3,7 +3,7 @@
 import numpy as np
 import pytest
 
-from pymoo.algorithms.moo.nsga2 import calc_crowding_distance
+from pymoo.operators.survival.rank_and_crowding.metrics import calc_crowding_distance
 from pymoo.util.nds.non_dominated_sorting import NonDominatedSorting
 from tests.test_util import load_to_test_resource
 
diff --git a/tests/misc/test_crowding_distance.py b/tests/misc/test_crowding_distance.py
index 4245f6927..750c67c98 100644
--- a/tests/misc/test_crowding_distance.py
+++ b/tests/misc/test_crowding_distance.py
@@ -3,7 +3,7 @@
 import numpy as np
 import pytest
 
-from pymoo.algorithms.moo.nsga2 import calc_crowding_distance
+from pymoo.operators.survival.rank_and_crowding.metrics import calc_crowding_distance
 from pymoo.config import get_pymoo
 
 

From 8da25a82c1b991e859a37d84364892623ad2b83c Mon Sep 17 00:00:00 2001
From: mooscalia project <93492480+mooscaliaproject@users.noreply.github.com>
Date: Mon, 17 Oct 2022 13:43:50 -0300
Subject: [PATCH 02/12] Fix RankAndCrowding docs pcd

---
 pymoo/operators/survival/rank_and_crowding/classes.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pymoo/operators/survival/rank_and_crowding/classes.py b/pymoo/operators/survival/rank_and_crowding/classes.py
index 516a0dce3..dcf3bc1fd 100644
--- a/pymoo/operators/survival/rank_and_crowding/classes.py
+++ b/pymoo/operators/survival/rank_and_crowding/classes.py
@@ -27,7 +27,7 @@ def __init__(self, nds=None, crowding_func="cd"):
             Crowding metric. Options are:
             
                 - 'cd': crowding distances
-                - 'pcd' or 'pruned-cd': pruned crowding distances
+                - 'pcd' or 'pruning-cd': improved pruning based on crowding distances
                 - 'ce': crowding entropy
                 - 'mnn': M-Neaest Neighbors
                 - '2nn': 2-Neaest Neighbors
@@ -120,7 +120,7 @@ def __init__(self, nds=None, crowding_func="cd"):
             Crowding metric. Options are:
             
                 - 'cd': crowding distances
-                - 'pcd' or 'pruned-cd': pruned crowding distances
+                - 'pcd' or 'pruning-cd': improved pruning based on crowding distances
                 - 'ce': crowding entropy
                 - 'mnn': M-Neaest Neighbors
                 - '2nn': 2-Neaest Neighbors

From 91c7aad6b61cb9f7d1f78238a5eaaa6da1a1fb30 Mon Sep 17 00:00:00 2001
From: mooscalia project <93492480+mooscaliaproject@users.noreply.github.com>
Date: Mon, 31 Oct 2022 21:27:31 -0300
Subject: [PATCH 03/12] Include tests for new crowding metrics

---
 MANIFEST.in                                |   2 +-
 pymoo/cython/mnn.pyx                       | 131 +--------------------
 pymoo/cython/pruning_cd.pyx                | 119 +------------------
 pymoo/cython/utils.pxd                     | 129 ++++++++++++++++++++
 tests/algorithms/test_rank_and_crowding.py | 117 ++++++++++++++++++
 5 files changed, 251 insertions(+), 247 deletions(-)
 create mode 100644 pymoo/cython/utils.pxd
 create mode 100644 tests/algorithms/test_rank_and_crowding.py

diff --git a/MANIFEST.in b/MANIFEST.in
index 182882a01..f84b358f4 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,4 +1,4 @@
 prune .
-recursive-include pymoo *.py *.pyx
+recursive-include pymoo *.py *.pyx *.pxd
 recursive-include pymoo/cython/vendor *.cpp *.h
 include LICENSE Makefile
diff --git a/pymoo/cython/mnn.pyx b/pymoo/cython/mnn.pyx
index 7ae772e84..a3a8552fd 100644
--- a/pymoo/cython/mnn.pyx
+++ b/pymoo/cython/mnn.pyx
@@ -10,6 +10,8 @@
 
 import numpy as np
 
+from pymoo.cython.utils cimport c_get_drop, c_get_argmin, c_get_argmax, c_normalize_array
+
 from libcpp cimport bool
 from libcpp.vector cimport vector
 from libcpp.set cimport set as cpp_set
@@ -266,132 +268,3 @@ cdef cpp_set[int] c_get_calc_items(
                 calc_items.insert(i)
     
     return calc_items
-
-
-#Returns elements to remove based on crowding metric d and heap of remaining elements H
-cdef int c_get_drop(double[:] d, cpp_set[int] H):
-
-    cdef:
-        int i, min_i
-        double min_d
-
-    min_d = HUGE_VAL
-    min_i = 0
-
-    for i in H:
-
-        if d[i] <= min_d:
-            min_d = d[i]
-            min_i = i
-    
-    return min_i
-
-
-#Elements in condensed matrix
-cdef int c_square_to_condensed(int i, int j, int N):
-
-    cdef int _i = i
-
-    if i < j:
-        i = j
-        j = _i
-
-    return N * j - j * (j + 1) // 2 + i - 1 - j
-
-
-#Returns vector of positions of minimum values along axis 0 of a 2d memoryview
-cdef vector[int] c_get_argmin(double[:, :] X):
-
-    cdef:
-        int N, M, min_i, n, m
-        double min_val
-        vector[int] indexes
-    
-    N = X.shape[0]
-    M = X.shape[1]
-
-    indexes = vector[int]()
-    
-    for m in range(M):
-
-        min_i = 0
-        min_val = X[0, m]
-
-        for n in range(N):
-
-            if X[n, m] < min_val:
-
-                min_i = n
-                min_val = X[n, m]
-        
-        indexes.push_back(min_i)
-    
-    return indexes
-
-
-#Returns vector of positions of maximum values along axis 0 of a 2d memoryview
-cdef vector[int] c_get_argmax(double[:, :] X):
-
-    cdef:
-        int N, M, max_i, n, m
-        double max_val
-        vector[int] indexes
-    
-    N = X.shape[0]
-    M = X.shape[1]
-
-    indexes = vector[int]()
-    
-    for m in range(M):
-
-        max_i = 0
-        max_val = X[0, m]
-
-        for n in range(N):
-
-            if X[n, m] > max_val:
-
-                max_i = n
-                max_val = X[n, m]
-        
-        indexes.push_back(max_i)
-    
-    return indexes
-
-
-#Performs normalization of a 2d memoryview
-cdef double[:, :] c_normalize_array(double[:, :] X, vector[int] extremes_max, vector[int] extremes_min):
-
-    cdef:
-        int N = X.shape[0]
-        int M = X.shape[1]
-        int n, m, l, u
-        double l_val, u_val, diff_val
-        vector[double] min_vals, max_vals
-    
-    min_vals = vector[double]()
-    max_vals = vector[double]()
-
-    m = 0
-    for u in extremes_max:
-        u_val = X[u, m]
-        max_vals.push_back(u_val)
-        m = m + 1
-    
-    m = 0
-    for l in extremes_min:
-        l_val = X[l, m]
-        min_vals.push_back(l_val)
-        m = m + 1
-    
-    for m in range(M):
-
-        diff_val = max_vals[m] - min_vals[m]
-        if diff_val == 0.0:
-            diff_val = 1.0
-
-        for n in range(N):
-
-            X[n, m] = (X[n, m] - min_vals[m]) / diff_val
-    
-    return X
\ No newline at end of file
diff --git a/pymoo/cython/pruning_cd.pyx b/pymoo/cython/pruning_cd.pyx
index 505c29d56..a08c07f5a 100644
--- a/pymoo/cython/pruning_cd.pyx
+++ b/pymoo/cython/pruning_cd.pyx
@@ -3,6 +3,8 @@
 
 import numpy as np
 
+from pymoo.cython.utils cimport c_get_drop, c_get_argmin, c_get_argmax, c_normalize_array
+
 from libcpp cimport bool
 from libcpp.vector cimport vector
 from libcpp.set cimport set as cpp_set
@@ -193,120 +195,3 @@ cdef cpp_set[int] c_get_calc_items(
                 I[n:-1, m] = I[n + 1:, m]
     
     return calc_items
-
-
-#Returns elements to remove based on crowding metric d and heap of remaining elements H
-cdef int c_get_drop(double[:] d, cpp_set[int] H):
-
-    cdef:
-        int i, min_i
-        double min_d
-
-    min_d = HUGE_VAL
-    min_i = 0
-
-    for i in H:
-
-        if d[i] <= min_d:
-            min_d = d[i]
-            min_i = i
-    
-    return min_i
-
-
-#Returns vector of positions of minimum values along axis 0 of a 2d memoryview
-cdef vector[int] c_get_argmin(double[:, :] X):
-
-    cdef:
-        int N, M, min_i, n, m
-        double min_val
-        vector[int] indexes
-    
-    N = X.shape[0]
-    M = X.shape[1]
-
-    indexes = vector[int]()
-    
-    for m in range(M):
-
-        min_i = 0
-        min_val = X[0, m]
-
-        for n in range(N):
-
-            if X[n, m] < min_val:
-
-                min_i = n
-                min_val = X[n, m]
-        
-        indexes.push_back(min_i)
-    
-    return indexes
-
-
-#Returns vector of positions of maximum values along axis 0 of a 2d memoryview
-cdef vector[int] c_get_argmax(double[:, :] X):
-
-    cdef:
-        int N, M, max_i, n, m
-        double max_val
-        vector[int] indexes
-    
-    N = X.shape[0]
-    M = X.shape[1]
-
-    indexes = vector[int]()
-    
-    for m in range(M):
-
-        max_i = 0
-        max_val = X[0, m]
-
-        for n in range(N):
-
-            if X[n, m] > max_val:
-
-                max_i = n
-                max_val = X[n, m]
-        
-        indexes.push_back(max_i)
-    
-    return indexes
-
-
-#Performs normalization of a 2d memoryview
-cdef double[:, :] c_normalize_array(double[:, :] X, vector[int] extremes_max, vector[int] extremes_min):
-
-    cdef:
-        int N = X.shape[0]
-        int M = X.shape[1]
-        int n, m, l, u
-        double l_val, u_val, diff_val
-        vector[double] min_vals, max_vals
-    
-    min_vals = vector[double]()
-    max_vals = vector[double]()
-
-    m = 0
-    for u in extremes_max:
-        u_val = X[u, m]
-        max_vals.push_back(u_val)
-        m = m + 1
-    
-    m = 0
-    for l in extremes_min:
-        l_val = X[l, m]
-        min_vals.push_back(l_val)
-        m = m + 1
-    
-    for m in range(M):
-
-        diff_val = max_vals[m] - min_vals[m]
-        if diff_val == 0.0:
-            diff_val = 1.0
-
-        for n in range(N):
-
-            X[n, m] = (X[n, m] - min_vals[m]) / diff_val
-    
-    return X
\ No newline at end of file
diff --git a/pymoo/cython/utils.pxd b/pymoo/cython/utils.pxd
new file mode 100644
index 000000000..1d15b672c
--- /dev/null
+++ b/pymoo/cython/utils.pxd
@@ -0,0 +1,129 @@
+# distutils: language = c++
+# cython: language_level=2, boundscheck=False, wraparound=False, cdivision=True
+
+import numpy as np
+
+from libcpp cimport bool
+from libcpp.vector cimport vector
+from libcpp.set cimport set as cpp_set
+
+
+cdef extern from "math.h":
+    double HUGE_VAL
+    
+
+#Returns elements to remove based on crowding metric d and heap of remaining elements H
+cdef inline int c_get_drop(double[:] d, cpp_set[int] H):
+
+    cdef:
+        int i, min_i
+        double min_d
+
+    min_d = HUGE_VAL
+    min_i = 0
+
+    for i in H:
+
+        if d[i] <= min_d:
+            min_d = d[i]
+            min_i = i
+    
+    return min_i
+
+
+#Returns vector of positions of minimum values along axis 0 of a 2d memoryview
+cdef inline vector[int] c_get_argmin(double[:, :] X):
+
+    cdef:
+        int N, M, min_i, n, m
+        double min_val
+        vector[int] indexes
+    
+    N = X.shape[0]
+    M = X.shape[1]
+
+    indexes = vector[int]()
+    
+    for m in range(M):
+
+        min_i = 0
+        min_val = X[0, m]
+
+        for n in range(N):
+
+            if X[n, m] < min_val:
+
+                min_i = n
+                min_val = X[n, m]
+        
+        indexes.push_back(min_i)
+    
+    return indexes
+
+
+#Returns vector of positions of maximum values along axis 0 of a 2d memoryview
+cdef inline vector[int] c_get_argmax(double[:, :] X):
+
+    cdef:
+        int N, M, max_i, n, m
+        double max_val
+        vector[int] indexes
+    
+    N = X.shape[0]
+    M = X.shape[1]
+
+    indexes = vector[int]()
+    
+    for m in range(M):
+
+        max_i = 0
+        max_val = X[0, m]
+
+        for n in range(N):
+
+            if X[n, m] > max_val:
+
+                max_i = n
+                max_val = X[n, m]
+        
+        indexes.push_back(max_i)
+    
+    return indexes
+
+
+#Performs normalization of a 2d memoryview
+cdef inline double[:, :] c_normalize_array(double[:, :] X, vector[int] extremes_max, vector[int] extremes_min):
+
+    cdef:
+        int N = X.shape[0]
+        int M = X.shape[1]
+        int n, m, l, u
+        double l_val, u_val, diff_val
+        vector[double] min_vals, max_vals
+    
+    min_vals = vector[double]()
+    max_vals = vector[double]()
+
+    m = 0
+    for u in extremes_max:
+        u_val = X[u, m]
+        max_vals.push_back(u_val)
+        m = m + 1
+    
+    m = 0
+    for l in extremes_min:
+        l_val = X[l, m]
+        min_vals.push_back(l_val)
+        m = m + 1
+    
+    for m in range(M):
+
+        diff_val = max_vals[m] - min_vals[m]
+        if diff_val == 0.0:
+            diff_val = 1.0
+
+        for n in range(N):
+
+            X[n, m] = (X[n, m] - min_vals[m]) / diff_val
+    
+    return X
\ No newline at end of file
diff --git a/tests/algorithms/test_rank_and_crowding.py b/tests/algorithms/test_rank_and_crowding.py
new file mode 100644
index 000000000..b08adde36
--- /dev/null
+++ b/tests/algorithms/test_rank_and_crowding.py
@@ -0,0 +1,117 @@
+import pytest
+
+import numpy as np
+from pymoo.optimize import minimize
+from pymoo.problems import get_problem
+from pymoo.indicators.igd import IGD
+from pymoo.algorithms.moo.nsga2 import NSGA2
+from pymoo.operators.survival.rank_and_crowding import RankAndCrowding, ConstrRankAndCrowding
+from pymoo.operators.survival.rank_and_crowding.metrics import calc_crowding_distance
+from pymoo.util.function_loader import load_function
+from pymoo.util.mnn import calc_mnn as calc_mnn_python
+from pymoo.util.mnn import calc_2nn as calc_2nn_python
+
+
+calc_mnn = load_function("calc_mnn")
+calc_2nn = load_function("calc_2nn")
+calc_pcd = load_function("calc_pcd")
+
+
+@pytest.mark.parametrize('crowding_func', ["mnn", "2nn", "cd", "pcd", "ce"])
+@pytest.mark.parametrize('survival', [RankAndCrowding, ConstrRankAndCrowding])
+def test_multi_run(crowding_func, survival):
+    
+    problem = get_problem("truss2d")
+
+    NGEN = 250
+    POPSIZE = 100
+    SEED = 5
+    
+    nsga2 = NSGA2(pop_size=POPSIZE, survival=survival(crowding_func=crowding_func))
+
+    res = minimize(problem,
+                   nsga2,
+                   ('n_gen', NGEN),
+                   seed=SEED,
+                   save_history=False,
+                   verbose=False)
+    
+    assert len(res.opt) > 0
+
+
+def test_cd_and_pcd():
+    
+    problem = get_problem("truss2d")
+
+    NGEN = 200
+    POPSIZE = 100
+    SEED = 5
+    
+    nsga2 = NSGA2(pop_size=POPSIZE, survival=RankAndCrowding(crowding_func="pcd"))
+
+    res = minimize(problem,
+                   nsga2,
+                   ('n_gen', NGEN),
+                   seed=SEED,
+                   save_history=False,
+                   verbose=False)
+    
+    cd = calc_crowding_distance(res.F)
+    pcd = calc_pcd(res.F)
+
+    assert np.sum(np.abs(cd[~np.isinf(cd)] - pcd[~np.isinf(pcd)])) <= 1e-8
+    
+    new_F = res.F.copy()
+    
+    for j in range(10):
+        
+        cd = calc_crowding_distance(new_F)
+        k = np.argmin(cd)
+        new_F = new_F[np.arange(len(new_F)) != k]
+    
+    pcd = calc_pcd(res.F, n_remove=10)
+    ind = np.argpartition(pcd, 10)[:10]
+    
+    new_F_alt = res.F.copy()[np.setdiff1d(np.arange(len(res.F)), ind)]
+    
+    assert np.sum(np.abs(new_F - new_F_alt)) <= 1e-8
+
+
+def test_mnn():
+    
+    problem = get_problem("dtlz2")
+
+    NGEN = 200
+    POPSIZE = 100
+    SEED = 5
+    
+    nsga2 = NSGA2(pop_size=POPSIZE, survival=RankAndCrowding(crowding_func="mnn"))
+
+    res = minimize(problem,
+                   nsga2,
+                   ('n_gen', NGEN),
+                   seed=SEED,
+                   save_history=False,
+                   verbose=False)
+    
+    surv_mnn = RankAndCrowding(crowding_func="mnn")
+    surv_2nn = RankAndCrowding(crowding_func="2nn")
+    
+    surv_mnn_py = RankAndCrowding(crowding_func=calc_mnn_python)
+    surv_2nn_py = RankAndCrowding(crowding_func=calc_2nn_python)
+    
+    np.random.seed(12)
+    pop_mnn = surv_mnn.do(problem, res.pop, n_survive=80)
+    
+    np.random.seed(12)
+    pop_mnn_py = surv_mnn_py.do(problem, res.pop, n_survive=80)
+    
+    assert np.sum(np.abs(pop_mnn.get("F") - pop_mnn_py.get("F"))) <= 1e-8
+    
+    np.random.seed(12)
+    pop_2nn = surv_2nn.do(problem, res.pop, n_survive=70)
+    
+    np.random.seed(12)
+    pop_2nn_py = surv_2nn_py.do(problem, res.pop, n_survive=70)
+    
+    assert np.sum(np.abs(pop_2nn.get("F") - pop_2nn_py.get("F"))) <= 1e-8
\ No newline at end of file

From d850b3d15e87b723bc617cbc9907187e73de9ca0 Mon Sep 17 00:00:00 2001
From: mooscalia project <93492480+mooscaliaproject@users.noreply.github.com>
Date: Mon, 31 Oct 2022 21:40:17 -0300
Subject: [PATCH 04/12] Fix constrained rank and crowding

---
 pymoo/operators/survival/rank_and_crowding/classes.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pymoo/operators/survival/rank_and_crowding/classes.py b/pymoo/operators/survival/rank_and_crowding/classes.py
index dcf3bc1fd..56493bc3f 100644
--- a/pymoo/operators/survival/rank_and_crowding/classes.py
+++ b/pymoo/operators/survival/rank_and_crowding/classes.py
@@ -155,7 +155,7 @@ def _do(self,
         if problem.n_constr > 0:
 
             #Split by feasibility
-            feas, infeas = split_by_feasibility(pop, eps=0.0, sort_infeasbible_by_cv=True)
+            feas, infeas = feas, infeas = split_by_feasibility(pop, sort_infeas_by_cv=True, sort_feas_by_obj=False, return_pop=False)
 
             #Obtain len of feasible
             n_feas = len(feas)

From 97bf0ca21aa9045a972ebcb600cd0801cf9220e7 Mon Sep 17 00:00:00 2001
From: mooscalia project <93492480+mooscaliaproject@users.noreply.github.com>
Date: Mon, 31 Oct 2022 21:46:30 -0300
Subject: [PATCH 05/12] Fix crowding dist w duplicates test

---
 tests/misc/test_crowding_distance.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/tests/misc/test_crowding_distance.py b/tests/misc/test_crowding_distance.py
index 750c67c98..e6434fbe3 100644
--- a/tests/misc/test_crowding_distance.py
+++ b/tests/misc/test_crowding_distance.py
@@ -3,31 +3,32 @@
 import numpy as np
 import pytest
 
-from pymoo.operators.survival.rank_and_crowding.metrics import calc_crowding_distance
+from pymoo.operators.survival.rank_and_crowding.metrics import calc_crowding_distance, FunctionalDiversity
 from pymoo.config import get_pymoo
 
+crowding_func = FunctionalDiversity(calc_crowding_distance, filter_out_duplicates=True)
 
 @pytest.mark.skip(reason="check if this is supposed to work or not at all")
 def test_crowding_distance():
     D = np.loadtxt(os.path.join(get_pymoo(), "tests", "resources", "test_crowding.dat"))
     F, cd = D[:, :-1], D[:, -1]
-    assert np.all(np.abs(cd - calc_crowding_distance(F)) < 0.001)
+    assert np.all(np.abs(cd - crowding_func.do(F)) < 0.001)
 
 
 def test_crowding_distance_one_duplicate():
     F = np.array([[1.0, 1.0], [1.0, 1.0], [0.5, 1.5], [0.0, 2.0]])
-    cd = calc_crowding_distance(F)
+    cd = crowding_func.do(F)
     np.testing.assert_almost_equal(cd, np.array([np.inf, 0.0, 1.0, np.inf]))
 
 
 def test_crowding_distance_two_duplicates():
     F = np.array([[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [0.5, 1.5], [0.0, 2.0]])
-    cd = calc_crowding_distance(F)
+    cd = crowding_func.do(F)
     np.testing.assert_almost_equal(cd, np.array([np.inf, 0.0, 0.0, 1.0, np.inf]))
 
 
 def test_crowding_distance_norm_equals_zero():
     F = np.array([[1.0, 1.5, 0.5, 1.0], [1.0, 0.5, 1.5, 1.0], [1.0, 0.0, 2.0, 1.5]])
-    cd = calc_crowding_distance(F)
+    cd = crowding_func.do(F)
     np.testing.assert_almost_equal(cd, np.array([np.inf, 0.75, np.inf]))
 

From 2cd022cef0151d6481bf818204b19070df41b0c4 Mon Sep 17 00:00:00 2001
From: mooscalia project <93492480+mooscaliaproject@users.noreply.github.com>
Date: Mon, 31 Oct 2022 23:10:11 -0300
Subject: [PATCH 06/12] Fix functional diversity n_points n_obj

---
 pymoo/operators/survival/rank_and_crowding/metrics.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pymoo/operators/survival/rank_and_crowding/metrics.py b/pymoo/operators/survival/rank_and_crowding/metrics.py
index 751f4fe73..f40924ce7 100644
--- a/pymoo/operators/survival/rank_and_crowding/metrics.py
+++ b/pymoo/operators/survival/rank_and_crowding/metrics.py
@@ -46,7 +46,7 @@ def _do(self, F, **kwargs):
         
         n_points, n_obj = F.shape
 
-        if n_points <= F.shape[1]:
+        if n_points <= 2:
             return np.full(n_points, np.inf)
 
         else:

From 5ceda2a0815560f90a32283492983e7150c5c42c Mon Sep 17 00:00:00 2001
From: mooscalia project <93492480+mooscaliaproject@users.noreply.github.com>
Date: Mon, 31 Oct 2022 23:44:02 -0300
Subject: [PATCH 07/12] Fix mnn N versus M

---
 pymoo/cython/mnn.pyx                                  | 3 +++
 pymoo/operators/survival/rank_and_crowding/metrics.py | 2 +-
 pymoo/util/mnn.py                                     | 3 +++
 3 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/pymoo/cython/mnn.pyx b/pymoo/cython/mnn.pyx
index a3a8552fd..854400a95 100644
--- a/pymoo/cython/mnn.pyx
+++ b/pymoo/cython/mnn.pyx
@@ -31,6 +31,9 @@ def calc_mnn(double[:, :] X, int n_remove=0):
     N = X.shape[0]
     M = X.shape[1]
 
+    if N <= M:
+        return np.full(N, HUGE_VAL)
+
     if n_remove <= (N - M):
         if n_remove < 0:
             n_remove = 0
diff --git a/pymoo/operators/survival/rank_and_crowding/metrics.py b/pymoo/operators/survival/rank_and_crowding/metrics.py
index f40924ce7..f1ebeb8bc 100644
--- a/pymoo/operators/survival/rank_and_crowding/metrics.py
+++ b/pymoo/operators/survival/rank_and_crowding/metrics.py
@@ -19,7 +19,7 @@ def get_crowding_function(label):
     elif hasattr(label, "__call__"):
         fun = FunctionalDiversity(label, filter_out_duplicates=True)
     else:
-        raise KeyError("Crwoding function not defined")
+        raise KeyError("Crowding function not defined")
     return fun
     
 
diff --git a/pymoo/util/mnn.py b/pymoo/util/mnn.py
index 53a641ace..39aa63271 100644
--- a/pymoo/util/mnn.py
+++ b/pymoo/util/mnn.py
@@ -11,6 +11,9 @@ def calc_mnn_base(X, n_remove=0, twonn=False):
 
     N = X.shape[0]
     M = X.shape[1]
+    
+    if N <= M:
+        return np.full(N, np.inf)
 
     if n_remove <= (N - M):
         if n_remove < 0:

From f64dbd5133d625def17b970174307679013cbccd Mon Sep 17 00:00:00 2001
From: mooscalia project <93492480+mooscaliaproject@users.noreply.github.com>
Date: Mon, 31 Oct 2022 23:58:33 -0300
Subject: [PATCH 08/12] Include CowdingDiversity as a valid kwarg

---
 .../survival/rank_and_crowding/metrics.py     | 19 +++++++++++++++++--
 1 file changed, 17 insertions(+), 2 deletions(-)

diff --git a/pymoo/operators/survival/rank_and_crowding/metrics.py b/pymoo/operators/survival/rank_and_crowding/metrics.py
index f1ebeb8bc..f6fb4b846 100644
--- a/pymoo/operators/survival/rank_and_crowding/metrics.py
+++ b/pymoo/operators/survival/rank_and_crowding/metrics.py
@@ -13,11 +13,13 @@ def get_crowding_function(label):
     elif label == "ce":
         fun = FunctionalDiversity(calc_crowding_entropy, filter_out_duplicates=True)
     elif label == "mnn":
-        fun = FunctionalDiversity(load_function("calc_mnn"), filter_out_duplicates=True)
+        fun = FuncionalDiversityMNN(load_function("calc_mnn"), filter_out_duplicates=True)
     elif label == "2nn":
-        fun = FunctionalDiversity(load_function("calc_2nn"), filter_out_duplicates=True)
+        fun = FuncionalDiversityMNN(load_function("calc_2nn"), filter_out_duplicates=True)
     elif hasattr(label, "__call__"):
         fun = FunctionalDiversity(label, filter_out_duplicates=True)
+    elif isinstance(label, CrowdingDiversity):
+        fun = label
     else:
         raise KeyError("Crowding function not defined")
     return fun
@@ -69,6 +71,19 @@ def _do(self, F, **kwargs):
         return d
 
 
+class FuncionalDiversityMNN(FunctionalDiversity):
+    
+    def _do(self, F, **kwargs):
+        
+        n_points, n_obj = F.shape
+
+        if n_points <= n_obj:
+            return np.full(n_points, np.inf)
+        
+        else:
+            return super()._do(F, **kwargs)
+
+
 def calc_crowding_distance(F, **kwargs):
     n_points, n_obj = F.shape
 

From e73644db7915241a183a66ce8cba090fc9da9755 Mon Sep 17 00:00:00 2001
From: mooscalia project <93492480+mooscaliaproject@users.noreply.github.com>
Date: Fri, 18 Nov 2022 00:14:40 -0300
Subject: [PATCH 09/12] Style code formatting with autopep8

---
 pymoo/cython/mnn.pyx                          | 84 +++++++++----------
 pymoo/cython/pruning_cd.pyx                   | 58 ++++++-------
 pymoo/cython/utils.pxd                        | 38 ++++-----
 .../survival/rank_and_crowding/classes.py     | 70 ++++++++--------
 .../survival/rank_and_crowding/metrics.py     | 58 ++++++-------
 5 files changed, 154 insertions(+), 154 deletions(-)

diff --git a/pymoo/cython/mnn.pyx b/pymoo/cython/mnn.pyx
index 854400a95..11496135e 100644
--- a/pymoo/cython/mnn.pyx
+++ b/pymoo/cython/mnn.pyx
@@ -52,7 +52,7 @@ def calc_mnn(double[:, :] X, int n_remove=0):
 
     for n in extremes_max:
         extremes.insert(n)
-    
+
     X = c_normalize_array(X, extremes_max, extremes_min)
 
     return c_calc_mnn(X, n_remove, N, M, extremes)
@@ -88,7 +88,7 @@ def calc_2nn(double[:, :] X, int n_remove=0):
         extremes.insert(n)
 
     X = c_normalize_array(X, extremes_max, extremes_min)
-    
+
     M = 2
 
     return c_calc_mnn(X, n_remove, N, M, extremes)
@@ -104,27 +104,27 @@ cdef c_calc_mnn(double[:, :] X, int n_remove, int N, int M, cpp_set[int] extreme
         double[:, :] D
         double[:] d
         int[:, :] Mnn
-    
-    #Define items to calculate distances
+
+    # Define items to calculate distances
     calc_items = cpp_set[int]()
     for n in range(N):
         calc_items.insert(n)
     for n in extremes:
         calc_items.erase(n)
-    
-    #Define remaining items to evaluate
+
+    # Define remaining items to evaluate
     H = cpp_set[int]()
     for n in range(N):
         H.insert(n)
-    
-    #Instantiate distances array
+
+    # Instantiate distances array
     _D = np.empty((N, N), dtype=np.double)
     D = _D[:, :]
 
-    #Shape of X
+    # Shape of X
     MM = X.shape[1]
-    
-    #Fill values on D
+
+    # Fill values on D
     for i in range(N - 1):
         D[i, i] = 0.0
 
@@ -139,10 +139,10 @@ cdef c_calc_mnn(double[:, :] X, int n_remove, int N, int M, cpp_set[int] extreme
 
     D[N-1, N-1] = 0.0
 
-    #Initialize
+    # Initialize
     n_removed = 0
 
-    #Initialize neighbors and distances
+    # Initialize neighbors and distances
     # _Mnn = np.full((N, M), -1, dtype=np.intc)
     _Mnn = np.argpartition(D, range(1, M+1), axis=1)[:, 1:M+1].astype(np.intc)
     dd = np.full((N,), HUGE_VAL, dtype=np.double)
@@ -150,25 +150,25 @@ cdef c_calc_mnn(double[:, :] X, int n_remove, int N, int M, cpp_set[int] extreme
     Mnn = _Mnn[:, :]
     d = dd[:]
 
-    #Obtain distance metrics
+    # Obtain distance metrics
     c_calc_d(d, Mnn, D, calc_items, M)
 
-    #While n_remove not acheived (no need to recalculate if only one item should be removed)
+    # While n_remove not acheived (no need to recalculate if only one item should be removed)
     while n_removed < (n_remove - 1):
 
-        #Obtain element to drop
+        # Obtain element to drop
         k = c_get_drop(d, H)
         H.erase(k)
 
-        #Update index
+        # Update index
         n_removed = n_removed + 1
 
-        #Get items to be recalculated
+        # Get items to be recalculated
         calc_items = c_get_calc_items(Mnn, H, k, M)
         for n in extremes:
             calc_items.erase(n)
-        
-        #Fill in neighbors and distance matrix
+
+        # Fill in neighbors and distance matrix
         c_calc_mnn_iter(
                 X,
                 Mnn,
@@ -178,7 +178,7 @@ cdef c_calc_mnn(double[:, :] X, int n_remove, int N, int M, cpp_set[int] extreme
                 H
             )
 
-        #Obtain distance metrics
+        # Obtain distance metrics
         c_calc_d(d, Mnn, D, calc_items, M)
 
     return dd
@@ -195,51 +195,51 @@ cdef c_calc_mnn_iter(
 
     cdef:
         int i, j, m
-    
-    #Iterate over items to calculate
+
+    # Iterate over items to calculate
     for i in calc_items:
 
-        #Iterate over elements in X
+        # Iterate over elements in X
         for j in H:
 
-            #Go to next if same element
+            # Go to next if same element
             if (j == i):
                 continue
-            
-            #Replace at least the last neighbor
+
+            # Replace at least the last neighbor
             elif (D[i, j] <= D[i, Mnn[i, M-1]]) or (Mnn[i, M-1] == -1):
-                
-                #Iterate over current values
+
+                # Iterate over current values
                 for m in range(M):
 
-                    #Set to current if unassigned
+                    # Set to current if unassigned
                     if (Mnn[i, m] == -1):
 
-                        #Set last neighbor to index
+                        # Set last neighbor to index
                         Mnn[i, m] = j
                         break
 
-                    #Break if checking already corresponding index
+                    # Break if checking already corresponding index
                     elif (j == Mnn[i, m]):
                         break
 
-                    #Distance satisfies condition
+                    # Distance satisfies condition
                     elif (D[i, j] <= D[i, Mnn[i, m]]):
-                            
-                        #Replace higher values
+
+                        # Replace higher values
                         Mnn[i, m + 1:] = Mnn[i, m:-1]
-                        
-                        #Replace current value
+
+                        # Replace current value
                         Mnn[i, m] = j
                         break
 
 
-#Calculate crowding metric
+# Calculate crowding metric
 cdef c_calc_d(double[:] d, int[:, :] Mnn, double[:, :] D, cpp_set[int] calc_items, int M):
 
     cdef:
         int i, m
-    
+
     for i in calc_items:
 
         d[i] = 1
@@ -247,7 +247,7 @@ cdef c_calc_d(double[:] d, int[:, :] Mnn, double[:, :] D, cpp_set[int] calc_item
             d[i] = d[i] * D[i, Mnn[i, m]]
 
 
-#Returns indexes of items to be recalculated after removal
+# Returns indexes of items to be recalculated after removal
 cdef cpp_set[int] c_get_calc_items(
     int[:, :] Mnn,
     cpp_set[int] H,
@@ -256,7 +256,7 @@ cdef cpp_set[int] c_get_calc_items(
     cdef:
         int i, m
         cpp_set[int] calc_items
-    
+
     calc_items = cpp_set[int]()
 
     for i in H:
@@ -269,5 +269,5 @@ cdef cpp_set[int] c_get_calc_items(
                 Mnn[i, M-1] = -1
 
                 calc_items.insert(i)
-    
+
     return calc_items
diff --git a/pymoo/cython/pruning_cd.pyx b/pymoo/cython/pruning_cd.pyx
index a08c07f5a..6a602d60e 100644
--- a/pymoo/cython/pruning_cd.pyx
+++ b/pymoo/cython/pruning_cd.pyx
@@ -14,7 +14,7 @@ cdef extern from "math.h":
     double HUGE_VAL
 
 
-#Python definition
+# Python definition
 def calc_pcd(double[:, :] X, int n_remove=0):
 
     cdef:
@@ -53,7 +53,7 @@ def calc_pcd(double[:, :] X, int n_remove=0):
     return c_calc_pcd(X, I, n_remove, N, M, extremes)
 
 
-#Returns crowding metrics with recursive elimination
+# Returns crowding metrics with recursive elimination
 cdef c_calc_pcd(double[:, :] X, int[:, :] I, int n_remove, int N, int M, cpp_set[int] extremes):
 
     cdef:
@@ -62,30 +62,30 @@ cdef c_calc_pcd(double[:, :] X, int[:, :] I, int n_remove, int N, int M, cpp_set
         cpp_set[int] H
         double[:, :] D
         double[:] d
-    
-    #Define items to calculate distances
+
+    # Define items to calculate distances
     calc_items = cpp_set[int]()
     for n in range(N):
         calc_items.insert(n)
     for n in extremes:
         calc_items.erase(n)
-    
-    #Define remaining items to evaluate
+
+    # Define remaining items to evaluate
     H = cpp_set[int]()
     for n in range(N):
         H.insert(n)
 
-    #Initialize
+    # Initialize
     n_removed = 0
 
-    #Initialize neighbors and distances
+    # Initialize neighbors and distances
     _D = np.full((N, M), HUGE_VAL, dtype=np.double)
     dd = np.full((N,), HUGE_VAL, dtype=np.double)
 
     D = _D[:, :]
     d = dd[:]
 
-    #Fill in neighbors and distance matrix
+    # Fill in neighbors and distance matrix
     c_calc_pcd_iter(
             X,
             I,
@@ -94,25 +94,25 @@ cdef c_calc_pcd(double[:, :] X, int[:, :] I, int n_remove, int N, int M, cpp_set
             calc_items,
         )
 
-    #Obtain distance metrics
+    # Obtain distance metrics
     c_calc_d(d, D, calc_items, M)
 
-    #While n_remove not acheived
+    # While n_remove not acheived
     while n_removed < (n_remove - 1):
 
-        #Obtain element to drop
+        # Obtain element to drop
         k = c_get_drop(d, H)
         H.erase(k)
 
-        #Update index
+        # Update index
         n_removed = n_removed + 1
 
-        #Get items to be recalculated
+        # Get items to be recalculated
         calc_items = c_get_calc_items(I, k, M, N)
         for n in extremes:
             calc_items.erase(n)
-        
-        #Fill in neighbors and distance matrix
+
+        # Fill in neighbors and distance matrix
         c_calc_pcd_iter(
                 X,
                 I,
@@ -121,13 +121,13 @@ cdef c_calc_pcd(double[:, :] X, int[:, :] I, int n_remove, int N, int M, cpp_set
                 calc_items,
             )
 
-        #Obtain distance metrics
+        # Obtain distance metrics
         c_calc_d(d, D, calc_items, M)
 
     return dd
 
 
-#Iterate
+# Iterate
 cdef c_calc_pcd_iter(
     double[:, :] X,
     int[:, :] I,
@@ -138,11 +138,11 @@ cdef c_calc_pcd_iter(
 
     cdef:
         int i, m, n, l, u
-    
-    #Iterate over items to calculate
+
+    # Iterate over items to calculate
     for i in calc_items:
 
-        #Iterate over elements in X
+        # Iterate over elements in X
         for m in range(M):
 
             for n in range(N):
@@ -155,12 +155,12 @@ cdef c_calc_pcd_iter(
                     D[i, m] = (X[u, m] - X[l, m]) / M
 
 
-#Calculate crowding metric
+# Calculate crowding metric
 cdef c_calc_d(double[:] d, double[:, :] D, cpp_set[int] calc_items, int M):
 
     cdef:
         int i, m
-    
+
     for i in calc_items:
 
         d[i] = 0
@@ -168,7 +168,7 @@ cdef c_calc_d(double[:] d, double[:, :] D, cpp_set[int] calc_items, int M):
             d[i] = d[i] + D[i, m]
 
 
-#Returns indexes of items to be recalculated after removal
+# Returns indexes of items to be recalculated after removal
 cdef cpp_set[int] c_get_calc_items(
     int[:, :] I,
     int k, int M, int N
@@ -177,21 +177,21 @@ cdef cpp_set[int] c_get_calc_items(
     cdef:
         int n, m
         cpp_set[int] calc_items
-    
+
     calc_items = cpp_set[int]()
 
-    #Iterate over all elements in I
+    # Iterate over all elements in I
     for m in range(M):
 
         for n in range(N):
 
             if I[n, m] == k:
 
-                #Add to set of items to be recalculated
+                # Add to set of items to be recalculated
                 calc_items.insert(I[n - 1, m])
                 calc_items.insert(I[n + 1, m])
 
-                #Remove element from sorted array
+                # Remove element from sorted array
                 I[n:-1, m] = I[n + 1:, m]
-    
+
     return calc_items
diff --git a/pymoo/cython/utils.pxd b/pymoo/cython/utils.pxd
index 1d15b672c..303807cf2 100644
--- a/pymoo/cython/utils.pxd
+++ b/pymoo/cython/utils.pxd
@@ -10,9 +10,9 @@ from libcpp.set cimport set as cpp_set
 
 cdef extern from "math.h":
     double HUGE_VAL
-    
 
-#Returns elements to remove based on crowding metric d and heap of remaining elements H
+
+# Returns elements to remove based on crowding metric d and heap of remaining elements H
 cdef inline int c_get_drop(double[:] d, cpp_set[int] H):
 
     cdef:
@@ -27,23 +27,23 @@ cdef inline int c_get_drop(double[:] d, cpp_set[int] H):
         if d[i] <= min_d:
             min_d = d[i]
             min_i = i
-    
+
     return min_i
 
 
-#Returns vector of positions of minimum values along axis 0 of a 2d memoryview
+# Returns vector of positions of minimum values along axis 0 of a 2d memoryview
 cdef inline vector[int] c_get_argmin(double[:, :] X):
 
     cdef:
         int N, M, min_i, n, m
         double min_val
         vector[int] indexes
-    
+
     N = X.shape[0]
     M = X.shape[1]
 
     indexes = vector[int]()
-    
+
     for m in range(M):
 
         min_i = 0
@@ -55,25 +55,25 @@ cdef inline vector[int] c_get_argmin(double[:, :] X):
 
                 min_i = n
                 min_val = X[n, m]
-        
+
         indexes.push_back(min_i)
-    
+
     return indexes
 
 
-#Returns vector of positions of maximum values along axis 0 of a 2d memoryview
+# Returns vector of positions of maximum values along axis 0 of a 2d memoryview
 cdef inline vector[int] c_get_argmax(double[:, :] X):
 
     cdef:
         int N, M, max_i, n, m
         double max_val
         vector[int] indexes
-    
+
     N = X.shape[0]
     M = X.shape[1]
 
     indexes = vector[int]()
-    
+
     for m in range(M):
 
         max_i = 0
@@ -85,13 +85,13 @@ cdef inline vector[int] c_get_argmax(double[:, :] X):
 
                 max_i = n
                 max_val = X[n, m]
-        
+
         indexes.push_back(max_i)
-    
+
     return indexes
 
 
-#Performs normalization of a 2d memoryview
+# Performs normalization of a 2d memoryview
 cdef inline double[:, :] c_normalize_array(double[:, :] X, vector[int] extremes_max, vector[int] extremes_min):
 
     cdef:
@@ -100,7 +100,7 @@ cdef inline double[:, :] c_normalize_array(double[:, :] X, vector[int] extremes_
         int n, m, l, u
         double l_val, u_val, diff_val
         vector[double] min_vals, max_vals
-    
+
     min_vals = vector[double]()
     max_vals = vector[double]()
 
@@ -109,13 +109,13 @@ cdef inline double[:, :] c_normalize_array(double[:, :] X, vector[int] extremes_
         u_val = X[u, m]
         max_vals.push_back(u_val)
         m = m + 1
-    
+
     m = 0
     for l in extremes_min:
         l_val = X[l, m]
         min_vals.push_back(l_val)
         m = m + 1
-    
+
     for m in range(M):
 
         diff_val = max_vals[m] - min_vals[m]
@@ -125,5 +125,5 @@ cdef inline double[:, :] c_normalize_array(double[:, :] X, vector[int] extremes_
         for n in range(N):
 
             X[n, m] = (X[n, m] - min_vals[m]) / diff_val
-    
-    return X
\ No newline at end of file
+
+    return X
diff --git a/pymoo/operators/survival/rank_and_crowding/classes.py b/pymoo/operators/survival/rank_and_crowding/classes.py
index 56493bc3f..bc326221c 100644
--- a/pymoo/operators/survival/rank_and_crowding/classes.py
+++ b/pymoo/operators/survival/rank_and_crowding/classes.py
@@ -13,9 +13,9 @@ def __init__(self, nds=None, crowding_func="cd"):
         A generalization of the NSGA-II survival operator that ranks individuals by dominance criteria
         and sorts the last front by some user-specified crowding metric. The default is NSGA-II's crowding distances
         although others might be more effective.
-        
+
         For many-objective problems, try using 'mnn' or '2nn'.
-        
+
         For Bi-objective problems, 'pcd' is very effective.
 
         Parameters
@@ -25,29 +25,29 @@ def __init__(self, nds=None, crowding_func="cd"):
 
         crowding_func : str or callable, optional
             Crowding metric. Options are:
-            
+
                 - 'cd': crowding distances
                 - 'pcd' or 'pruning-cd': improved pruning based on crowding distances
                 - 'ce': crowding entropy
                 - 'mnn': M-Neaest Neighbors
                 - '2nn': 2-Neaest Neighbors
-                
+
             If callable, it has the form ``fun(F, filter_out_duplicates=None, n_remove=None, **kwargs)``
             in which F (n, m) and must return metrics in a (n,) array.
-            
+
             The options 'pcd', 'cd', and 'ce' are recommended for two-objective problems, whereas 'mnn' and '2nn' for many objective.
             When using 'pcd', 'mnn', or '2nn', individuals are already eliminated in a 'single' manner. 
             Due to Cython implementation, they are as fast as the corresponding 'cd', 'mnn-fast', or '2nn-fast', 
             although they can singnificantly improve diversity of solutions.
             Defaults to 'cd'.
         """
-        
+
         crowding_func_ = get_crowding_function(crowding_func)
 
         super().__init__(filter_infeasible=True)
         self.nds = nds if nds is not None else NonDominatedSorting()
         self.crowding_func = crowding_func_
-        
+
     def _do(self,
             problem,
             pop,
@@ -68,19 +68,19 @@ def _do(self,
 
             # current front sorted by crowding distance if splitting
             while len(survivors) + len(front) > n_survive:
-                
-                #Define how many will be removed
+
+                # Define how many will be removed
                 n_remove = len(survivors) + len(front) - n_survive
-                
+
                 # re-calculate the crowding distance of the front
                 crowding_of_front = \
                     self.crowding_func.do(
                         F[front, :],
                         n_remove=n_remove
                     )
-                
+
                 I = randomized_argsort(crowding_of_front, order='descending', method='numpy')
-                
+
                 I = I[:-n_remove]
                 front = front[I]
 
@@ -105,7 +105,7 @@ def _do(self,
 
 
 class ConstrRankAndCrowding(Survival):
-    
+
     def __init__(self, nds=None, crowding_func="cd"):
         """
         The Rank and Crowding survival approach for handling constraints proposed on
@@ -118,27 +118,27 @@ def __init__(self, nds=None, crowding_func="cd"):
 
         crowding_func : str or callable, optional
             Crowding metric. Options are:
-            
+
                 - 'cd': crowding distances
                 - 'pcd' or 'pruning-cd': improved pruning based on crowding distances
                 - 'ce': crowding entropy
                 - 'mnn': M-Neaest Neighbors
                 - '2nn': 2-Neaest Neighbors
-                
+
             If callable, it has the form ``fun(F, filter_out_duplicates=None, n_remove=None, **kwargs)``
             in which F (n, m) and must return metrics in a (n,) array.
-            
+
             The options 'pcd', 'cd', and 'ce' are recommended for two-objective problems, whereas 'mnn' and '2nn' for many objective.
             When using 'pcd', 'mnn', or '2nn', individuals are already eliminated in a 'single' manner. 
             Due to Cython implementation, they are as fast as the corresponding 'cd', 'mnn-fast', or '2nn-fast', 
             although they can singnificantly improve diversity of solutions.
             Defaults to 'cd'.
         """
-        
+
         super().__init__(filter_infeasible=False)
         self.nds = nds if nds is not None else NonDominatedSorting()
         self.ranking = RankAndCrowding(nds=nds, crowding_func=crowding_func)
-    
+
     def _do(self,
             problem,
             pop,
@@ -151,49 +151,49 @@ def _do(self,
 
         n_survive = min(n_survive, len(pop))
 
-        #If the split should be done beforehand
+        # If the split should be done beforehand
         if problem.n_constr > 0:
 
-            #Split by feasibility
+            # Split by feasibility
             feas, infeas = feas, infeas = split_by_feasibility(pop, sort_infeas_by_cv=True, sort_feas_by_obj=False, return_pop=False)
 
-            #Obtain len of feasible
+            # Obtain len of feasible
             n_feas = len(feas)
 
-            #Assure there is at least_one survivor
+            # Assure there is at least_one survivor
             if n_feas == 0:
                 survivors = Population()
             else:
                 survivors = self.ranking.do(problem, pop[feas], *args, n_survive=min(len(feas), n_survive), **kwargs)
 
-            #Calculate how many individuals are still remaining to be filled up with infeasible ones
+            # Calculate how many individuals are still remaining to be filled up with infeasible ones
             n_remaining = n_survive - len(survivors)
 
-            #If infeasible solutions need to be added
+            # If infeasible solutions need to be added
             if n_remaining > 0:
-                
-                #Constraints to new ranking
+
+                # Constraints to new ranking
                 G = pop[infeas].get("G")
                 G = np.maximum(G, 0)
-                
-                #Fronts in infeasible population
+
+                # Fronts in infeasible population
                 infeas_fronts = self.nds.do(G, n_stop_if_ranked=n_remaining)
-                
-                #Iterate over fronts
+
+                # Iterate over fronts
                 for k, front in enumerate(infeas_fronts):
 
-                    #Save ranks
+                    # Save ranks
                     pop[infeas][front].set("cv_rank", k)
 
-                    #Current front sorted by CV
+                    # Current front sorted by CV
                     if len(survivors) + len(front) > n_survive:
-                        
-                        #Obtain CV of front
+
+                        # Obtain CV of front
                         CV = pop[infeas][front].get("CV").flatten()
                         I = randomized_argsort(CV, order='ascending', method='numpy')
                         I = I[:(n_survive - len(survivors))]
 
-                    #Otherwise take the whole front unsorted
+                    # Otherwise take the whole front unsorted
                     else:
                         I = np.arange(len(front))
 
diff --git a/pymoo/operators/survival/rank_and_crowding/metrics.py b/pymoo/operators/survival/rank_and_crowding/metrics.py
index f6fb4b846..ba45dee32 100644
--- a/pymoo/operators/survival/rank_and_crowding/metrics.py
+++ b/pymoo/operators/survival/rank_and_crowding/metrics.py
@@ -23,29 +23,29 @@ def get_crowding_function(label):
     else:
         raise KeyError("Crowding function not defined")
     return fun
-    
+
 
 class CrowdingDiversity:
-    
+
     def do(self, F, n_remove=0):
-        #Converting types Python int to Cython int would fail in some cases converting to long instead
+        # Converting types Python int to Cython int would fail in some cases converting to long instead
         n_remove = np.intc(n_remove)
         F = np.array(F, dtype=np.double)
         return self._do(F, n_remove=n_remove)
-    
+
     def _do(self, F, n_remove=None):
         pass
 
 
 class FunctionalDiversity(CrowdingDiversity):
-    
+
     def __init__(self, function=None, filter_out_duplicates=True):
         self.function = function
         self.filter_out_duplicates = filter_out_duplicates
         super().__init__()
-    
+
     def _do(self, F, **kwargs):
-        
+
         n_points, n_obj = F.shape
 
         if n_points <= 2:
@@ -62,24 +62,24 @@ def _do(self, F, **kwargs):
 
             # index the unique points of the array
             _F = F[is_unique]
-            
+
             _d = self.function(_F, **kwargs)
-            
+
             d = np.zeros(n_points)
             d[is_unique] = _d
-        
+
         return d
 
 
 class FuncionalDiversityMNN(FunctionalDiversity):
-    
+
     def _do(self, F, **kwargs):
-        
+
         n_points, n_obj = F.shape
 
         if n_points <= n_obj:
             return np.full(n_points, np.inf)
-        
+
         else:
             return super()._do(F, **kwargs)
 
@@ -113,7 +113,7 @@ def calc_crowding_distance(F, **kwargs):
     cd = np.sum(dist_to_last[J, np.arange(n_obj)] + dist_to_next[J, np.arange(n_obj)], axis=1) / n_obj
 
     return cd
-            
+
 
 def calc_crowding_entropy(F, **kwargs):
     """Wang, Y.-N., Wu, L.-H. & Yuan, X.-F., 2010. Multi-objective self-adaptive differential 
@@ -148,24 +148,24 @@ def calc_crowding_entropy(F, **kwargs):
     # prepare the distance to last and next vectors
     dl = dist.copy()[:-1]
     du = dist.copy()[1:]
-    
-    #Fix nan
+
+    # Fix nan
     dl[np.isnan(dl)] = 0.0
     du[np.isnan(du)] = 0.0
-    
-    #Total distance
+
+    # Total distance
     cd = dl + du
 
-    #Get relative positions
+    # Get relative positions
     pl = (dl[1:-1] / cd[1:-1])
     pu = (du[1:-1] / cd[1:-1])
 
-    #Entropy
+    # Entropy
     entropy = np.row_stack([np.full(n_obj, np.inf),
                             -(pl * np.log2(pl) + pu * np.log2(pu)),
                             np.full(n_obj, np.inf)])
-    
-    #Crowding entropy
+
+    # Crowding entropy
     J = np.argsort(I, axis=0)
     _cej = cd[J, np.arange(n_obj)] * entropy[J, np.arange(n_obj)] / norm
     _cej[np.isnan(_cej)] = 0.0
@@ -176,8 +176,8 @@ def calc_crowding_entropy(F, **kwargs):
 
 def calc_mnn_fast(F, **kwargs):
     return _calc_mnn_fast(F, F.shape[1], **kwargs)
-    
-    
+
+
 def calc_2nn_fast(F, **kwargs):
     return _calc_mnn_fast(F, 2, **kwargs)
 
@@ -187,20 +187,20 @@ def _calc_mnn_fast(F, n_neighbors, **kwargs):
     # calculate the norm for each objective - set to NaN if all values are equal
     norm = np.max(F, axis=0) - np.min(F, axis=0)
     norm[norm == 0] = 1.0
-    
+
     # F normalized
     F = (F - F.min(axis=0)) / norm
-    
+
     # Distances pairwise (Inefficient)
     D = squareform(pdist(F, metric="sqeuclidean"))
-    
+
     # M neighbors
     M = F.shape[1]
     _D = np.partition(D, range(1, M+1), axis=1)[:, 1:M+1]
-    
+
     # Metric d
     d = np.prod(_D, axis=1)
-    
+
     # Set top performers as np.inf
     _extremes = np.concatenate((np.argmin(F, axis=0), np.argmax(F, axis=0)))
     d[_extremes] = np.inf

From 40fb9f90e4b84f522c4c092218ff5650dac69e23 Mon Sep 17 00:00:00 2001
From: mooscalia project <93492480+mooscaliaproject@users.noreply.github.com>
Date: Sat, 7 Jan 2023 20:36:48 -0300
Subject: [PATCH 10/12] DOCS survival rank and crowding

---
 docs/source/operators/survival.ipynb | 276 +++++++++++++++++++++++++++
 docs/source/references.bib           |  45 ++++-
 2 files changed, 320 insertions(+), 1 deletion(-)
 create mode 100644 docs/source/operators/survival.ipynb

diff --git a/docs/source/operators/survival.ipynb b/docs/source/operators/survival.ipynb
new file mode 100644
index 000000000..f0a1e5f29
--- /dev/null
+++ b/docs/source/operators/survival.ipynb
@@ -0,0 +1,276 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    ".. _nb_survival:"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Survival"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rank and Crowding"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The original survival strategy proposed in [NSGA-II](../algorithms/moo/nsga2.ipynb#nsga-ii-non-dominated-sorting-genetic-algorithm) <cite data-cite=\"nsga2\"></cite> ranks solutions in fronts by dominance criterion and uses a diversity metric denoted crowding distances to sort individuals in each front. This is used as criterion to compare individuals in elitist parent selection schemes and to truncate the population in the survival selection stage of algorithms.\n",
+    "\n",
+    "Variants of the original algorithm have been proposed in the literature to address different performance aspects. Therefore the class ``RankAndCrowding`` from pymoo is a generalization of NSGA-II's survival in which several crowding metrics can be used. Some are already implemented and can be parsed as strings in the ``crowding_func`` argument, while others might be defined from scratch and parsed as callables. The ones available are:\n",
+    "\n",
+    "- **Crowding Distance** (*'cd'*): Proposed by Deb et al. <cite data-cite=\"nsga2\"></cite> in NSGA-II.\n",
+    "- **Pruning Crowding Distance** (*'pruning-cd'* or *'pcd'*): Proposed by Kukkonen & Deb <cite data-cite=\"kukkonen2006improved\"></cite>, it recursively recalculates crowding distances as removes individuals from a population to improve diversity.\n",
+    "- ***M*-Nearest Neighbors** (*'mnn'*): Proposed by Kukkonen & Deb <cite data-cite=\"kukkonen2006fast\"></cite> in an extension of GDE3 to many-objective problems.\n",
+    "- **2-Nearest Neighbors** (*'2nn'*): Also proposed by Kukkonen & Deb <cite data-cite=\"kukkonen2006fast\"></cite>, it is a variant of M-Nearest Neighbors in which the number of neighbors is two.\n",
+    "- **Crowding Entropy** (*'ce'*): Proposed by Wang et al. <cite data-cite=\"wangmosade\"></cite> it considers the relative position of a solution between its neighors.\n",
+    "\n",
+    "We encourage users to try ``crowding_func='pcd'`` for two-objective problems and ``crowding_func='mnn'`` for problems with more than two objectives.\n",
+    "\n",
+    "If callable, it has the form ``fun(F, filter_out_duplicates=None, n_remove=None, **kwargs)`` in which F (n, m) and must return metrics in a (n,) array.\n",
+    "\n",
+    "The ``ConstrRankAndCrowding`` class has the constraint handling approach proposed by Kukkonen, S. & Lampinen, J. <cite data-cite=\"gde3\"></cite> implemented in which solutions are also sorted in constraint violations space."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following examples the code for plotting was ommited although it is available in the [end of the page](#plots)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Pymoo imports\n",
+    "from pymoo.algorithms.moo.nsga2 import NSGA2\n",
+    "from pymoo.operators.survival.rank_and_crowding import RankAndCrowding\n",
+    "from pymoo.problems import get_problem\n",
+    "from pymoo.optimize import minimize\n",
+    "\n",
+    "# External imports\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Problem definition Truss-2d - a two-objective problem\n",
+    "problem = get_problem(\"truss2d\")\n",
+    "\n",
+    "# Algorithms\n",
+    "nsga2 = NSGA2(70, survival=RankAndCrowding(crowding_func=\"cd\"))\n",
+    "nsga2_p = NSGA2(70, survival=RankAndCrowding(crowding_func=\"pcd\"))\n",
+    "\n",
+    "# Minimization results\n",
+    "res_nsga2 = minimize(\n",
+    "    problem,\n",
+    "    nsga2,\n",
+    "    ('n_gen', 200),\n",
+    "    seed=12,\n",
+    ")\n",
+    "\n",
+    "# Minimization results\n",
+    "res_nsga2_p = minimize(\n",
+    "    problem,\n",
+    "    nsga2_p,\n",
+    "    ('n_gen', 200),\n",
+    "    seed=12,\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Problem definition DTLZ2 - a three-objective problem\n",
+    "problem = get_problem(\"dtlz2\")\n",
+    "\n",
+    "# Algorithms\n",
+    "nsga2 = NSGA2(130, survival=RankAndCrowding(crowding_func=\"cd\"))\n",
+    "nsga2_mnn = NSGA2(130, survival=RankAndCrowding(crowding_func=\"mnn\"))\n",
+    "\n",
+    "# Minimization results\n",
+    "res_nsga2 = minimize(\n",
+    "    problem,\n",
+    "    nsga2,\n",
+    "    ('n_gen', 150),\n",
+    "    seed=12,\n",
+    ")\n",
+    "\n",
+    "# Minimization results\n",
+    "res_nsga2_mnn = minimize(\n",
+    "    problem,\n",
+    "    nsga2_mnn,\n",
+    "    ('n_gen', 150),\n",
+    "    seed=12,\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    ".. autoclass:: pymoo.operators.survival.rank_and_crowding.RankAndCrowding\n",
+    "    :noindex:\n",
+    "\n",
+    ".. autoclass:: pymoo.operators.survival.rank_and_crowding.ConstrRankAndCrowding\n",
+    "    :noindex:"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Plots"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "fig, ax = plt.subplots(1, 2, figsize=[12, 5], dpi=100)\n",
+    "\n",
+    "ax[0].scatter(\n",
+    "    res_nsga2.F[:, 0], res_nsga2.F[:, 1],\n",
+    "    color=\"indigo\", label=\"NSGA-II (original)\", marker=\"o\",\n",
+    ")\n",
+    "ax[0].set_ylabel(\"$f_2$\")\n",
+    "ax[0].set_xlabel(\"$f_1$\")\n",
+    "ax[0].legend()\n",
+    "\n",
+    "ax[1].scatter(\n",
+    "    res_nsga2_p.F[:, 0], res_nsga2_p.F[:, 1],\n",
+    "    color=\"firebrick\", label=\"NSGA-II (pcd)\", marker=\"o\",\n",
+    ")\n",
+    "ax[1].set_ylabel(\"$f_2$\")\n",
+    "ax[1].set_xlabel(\"$f_1$\")\n",
+    "ax[1].legend()\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "plt.show()\n",
+    "```\n",
+    "\n",
+    "```python\n",
+    "fig, ax = plt.subplots(1, 2, figsize=[12, 5], dpi=100, subplot_kw={'projection':'3d'})\n",
+    "\n",
+    "ax[0].scatter(\n",
+    "    res_nsga2.F[:, 0], res_nsga2.F[:, 1], res_nsga2.F[:, 2],\n",
+    "    color=\"indigo\", label=\"NSGA-II (original)\", marker=\"o\",\n",
+    ")\n",
+    "ax[0].set_ylabel(\"$f_2$\")\n",
+    "ax[0].set_xlabel(\"$f_1$\")\n",
+    "ax[0].set_zlabel(\"$f_3$\")\n",
+    "ax[0].legend()\n",
+    "\n",
+    "ax[1].scatter(\n",
+    "    res_nsga2_mnn.F[:, 0], res_nsga2_mnn.F[:, 1], res_nsga2_mnn.F[:, 2],\n",
+    "    color=\"firebrick\", label=\"NSGA-II (mnn)\", marker=\"o\",\n",
+    ")\n",
+    "ax[1].set_ylabel(\"$f_2$\")\n",
+    "ax[1].set_xlabel(\"$f_1$\")\n",
+    "ax[1].set_zlabel(\"$f_3$\")\n",
+    "ax[1].legend()\n",
+    "\n",
+    "ax[0].view_init(elev=30, azim=30)\n",
+    "ax[1].view_init(elev=30, azim=30)\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "plt.show()\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "8ec0d6c9b8d50a94217d7ab4804e268ea3c783f3ca99db20a683c9c8ae9602ac"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/references.bib b/docs/source/references.bib
index 4f6259e1a..e0e640192 100644
--- a/docs/source/references.bib
+++ b/docs/source/references.bib
@@ -715,4 +715,47 @@ @ARTICLE{isres
 volume={35},
 number={2},
 pages={233-243},
-doi={10.1109/TSMCC.2004.841906}}
\ No newline at end of file
+doi={10.1109/TSMCC.2004.841906}}
+
+
+@inproceedings{gde3,
+  title={GDE3: The third evolution step of generalized differential evolution},
+  author={Kukkonen, Saku and Lampinen, Jouni},
+  booktitle={2005 IEEE congress on evolutionary computation},
+  volume={1},
+  pages={443--450},
+  year={2005},
+  organization={IEEE}
+}
+
+
+@inproceedings{kukkonen2006improved,
+  title={Improved pruning of non-dominated solutions based on crowding distance for bi-objective optimization problems},
+  author={Kukkonen, Saku and Deb, Kalyanmoy},
+  booktitle={2006 IEEE International Conference on Evolutionary Computation},
+  pages={1179--1186},
+  year={2006},
+  organization={IEEE}
+}
+
+
+@incollection{kukkonen2006fast,
+  title={A fast and effective method for pruning of non-dominated solutions in many-objective problems},
+  author={Kukkonen, Saku and Deb, Kalyanmoy},
+  booktitle={Parallel problem solving from nature-PPSN IX},
+  pages={553--562},
+  year={2006},
+  publisher={Springer}
+}
+
+
+@article{wangmosade,
+  title={Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure},
+  author={Wang, Yao-Nan and Wu, Liang-Hong and Yuan, Xiao-Fang},
+  journal={Soft Computing},
+  volume={14},
+  number={3},
+  pages={193--209},
+  year={2010},
+  publisher={Springer}
+}
\ No newline at end of file

From c4fc3ebd36ea46af0733c96c884593d0a228a82d Mon Sep 17 00:00:00 2001
From: mooscalia project <93492480+mooscaliaproject@users.noreply.github.com>
Date: Sun, 8 Jan 2023 22:44:30 -0300
Subject: [PATCH 11/12] DOCS Rank and Crowding

---
 docs/source/operators/survival.ipynb | 10 +++++-----
 docs/source/references.bib           | 11 +++--------
 2 files changed, 8 insertions(+), 13 deletions(-)

diff --git a/docs/source/operators/survival.ipynb b/docs/source/operators/survival.ipynb
index f0a1e5f29..f26344b01 100644
--- a/docs/source/operators/survival.ipynb
+++ b/docs/source/operators/survival.ipynb
@@ -34,10 +34,10 @@
     "Variants of the original algorithm have been proposed in the literature to address different performance aspects. Therefore the class ``RankAndCrowding`` from pymoo is a generalization of NSGA-II's survival in which several crowding metrics can be used. Some are already implemented and can be parsed as strings in the ``crowding_func`` argument, while others might be defined from scratch and parsed as callables. The ones available are:\n",
     "\n",
     "- **Crowding Distance** (*'cd'*): Proposed by Deb et al. <cite data-cite=\"nsga2\"></cite> in NSGA-II.\n",
-    "- **Pruning Crowding Distance** (*'pruning-cd'* or *'pcd'*): Proposed by Kukkonen & Deb <cite data-cite=\"kukkonen2006improved\"></cite>, it recursively recalculates crowding distances as removes individuals from a population to improve diversity.\n",
-    "- ***M*-Nearest Neighbors** (*'mnn'*): Proposed by Kukkonen & Deb <cite data-cite=\"kukkonen2006fast\"></cite> in an extension of GDE3 to many-objective problems.\n",
-    "- **2-Nearest Neighbors** (*'2nn'*): Also proposed by Kukkonen & Deb <cite data-cite=\"kukkonen2006fast\"></cite>, it is a variant of M-Nearest Neighbors in which the number of neighbors is two.\n",
-    "- **Crowding Entropy** (*'ce'*): Proposed by Wang et al. <cite data-cite=\"wangmosade\"></cite> it considers the relative position of a solution between its neighors.\n",
+    "- **Pruning Crowding Distance** (*'pruning-cd'* or *'pcd'*): Proposed by Kukkonen & Deb <cite data-cite=\"gde3pruning\"></cite>, it recursively recalculates crowding distances as removes individuals from a population to improve diversity.\n",
+    "- ***M*-Nearest Neighbors** (*'mnn'*): Proposed by Kukkonen & Deb <cite data-cite=\"gde3many\"></cite> in an extension of GDE3 to many-objective problems.\n",
+    "- **2-Nearest Neighbors** (*'2nn'*): Also proposed by Kukkonen & Deb <cite data-cite=\"gde3many\"></cite>, it is a variant of M-Nearest Neighbors in which the number of neighbors is two.\n",
+    "- **Crowding Entropy** (*'ce'*): Proposed by Wang et al. <cite data-cite=\"mosade\"></cite> it considers the relative position of a solution between its neighors.\n",
     "\n",
     "We encourage users to try ``crowding_func='pcd'`` for two-objective problems and ``crowding_func='mnn'`` for problems with more than two objectives.\n",
     "\n",
@@ -262,7 +262,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.7 (tags/v3.9.7:1016ef3, Aug 30 2021, 20:19:38) [MSC v.1929 64 bit (AMD64)]"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/docs/source/references.bib b/docs/source/references.bib
index e0e640192..dcb616dfd 100644
--- a/docs/source/references.bib
+++ b/docs/source/references.bib
@@ -164,11 +164,6 @@ @ARTICLE{nsga3-part2
 }
 
 
-
-
-
-
-
 @ARTICLE{moead,
 author = {Qingfu Zhang and Hui Li},
 title = {A multi-objective evolutionary algorithm based on decomposition},
@@ -729,7 +724,7 @@ @inproceedings{gde3
 }
 
 
-@inproceedings{kukkonen2006improved,
+@inproceedings{gde3pruning,
   title={Improved pruning of non-dominated solutions based on crowding distance for bi-objective optimization problems},
   author={Kukkonen, Saku and Deb, Kalyanmoy},
   booktitle={2006 IEEE International Conference on Evolutionary Computation},
@@ -739,7 +734,7 @@ @inproceedings{kukkonen2006improved
 }
 
 
-@incollection{kukkonen2006fast,
+@incollection{gde3many,
   title={A fast and effective method for pruning of non-dominated solutions in many-objective problems},
   author={Kukkonen, Saku and Deb, Kalyanmoy},
   booktitle={Parallel problem solving from nature-PPSN IX},
@@ -749,7 +744,7 @@ @incollection{kukkonen2006fast
 }
 
 
-@article{wangmosade,
+@article{mosade,
   title={Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure},
   author={Wang, Yao-Nan and Wu, Liang-Hong and Yuan, Xiao-Fang},
   journal={Soft Computing},

From 3c729f43b63f0c1442ef48cd805bf362f1527e8f Mon Sep 17 00:00:00 2001
From: mooscalia project <93492480+mooscaliaproject@users.noreply.github.com>
Date: Tue, 10 Jan 2023 14:40:29 -0300
Subject: [PATCH 12/12] DOCS plots for rank and crowding

---
 docs/source/operators/plots.py       |  54 ++++++++++
 docs/source/operators/survival.ipynb | 152 ++++++++++++++++-----------
 2 files changed, 146 insertions(+), 60 deletions(-)
 create mode 100644 docs/source/operators/plots.py

diff --git a/docs/source/operators/plots.py b/docs/source/operators/plots.py
new file mode 100644
index 000000000..a20e5598a
--- /dev/null
+++ b/docs/source/operators/plots.py
@@ -0,0 +1,54 @@
+import matplotlib.pyplot as plt
+
+
+def plot_pairs_3d(first, second, colors=("indigo", "firebrick"), **kwargs):
+    
+    fig, ax = plt.subplots(1, 2, subplot_kw={'projection':'3d'}, **kwargs)
+
+    ax[0].scatter(
+        *first[1].T,
+        color=colors[0], label=first[0], marker="o",
+    )
+    ax[0].set_ylabel("$f_2$")
+    ax[0].set_xlabel("$f_1$")
+    ax[0].set_zlabel("$f_3$")
+    ax[0].legend()
+
+    ax[1].scatter(
+        *second[1].T,
+        color=colors[1], label=second[0], marker="o",
+    )
+    ax[1].set_ylabel("$f_2$")
+    ax[1].set_xlabel("$f_1$")
+    ax[1].set_zlabel("$f_3$")
+    ax[1].legend()
+
+    ax[0].view_init(elev=30, azim=30)
+    ax[1].view_init(elev=30, azim=30)
+
+    fig.tight_layout()
+    plt.show()
+
+
+def plot_pairs_2d(first, second, colors=("indigo", "firebrick"), **kwargs):
+    
+    fig, ax = plt.subplots(1, 2, **kwargs)
+
+    ax[0].scatter(
+        *first[1].T,
+        color=colors[0], label=first[0], marker="o",
+    )
+    ax[0].set_ylabel("$f_2$")
+    ax[0].set_xlabel("$f_1$")
+    ax[0].legend()
+
+    ax[1].scatter(
+        *second[1].T,
+        color=colors[1], label=second[0], marker="o",
+    )
+    ax[1].set_ylabel("$f_2$")
+    ax[1].set_xlabel("$f_1$")
+    ax[1].legend()
+
+    fig.tight_layout()
+    plt.show()
\ No newline at end of file
diff --git a/docs/source/operators/survival.ipynb b/docs/source/operators/survival.ipynb
index f26344b01..e966d4b3c 100644
--- a/docs/source/operators/survival.ipynb
+++ b/docs/source/operators/survival.ipynb
@@ -51,12 +51,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the following examples the code for plotting was ommited although it is available in the [end of the page](#plots)."
+    "In the following examples the code for plotting was ommited although it is available at the [end of the page](#plots)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,12 +67,12 @@
     "from pymoo.optimize import minimize\n",
     "\n",
     "# External imports\n",
-    "import matplotlib.pyplot as plt"
+    "from plots import plot_pairs_2d, plot_pairs_3d"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,20 +101,33 @@
    ]
   },
   {
-   "attachments": {
-    "image.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 3,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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amrzONzU1ea71rQD2+vzzz7V3716vGF/36Psc/mL6Xh8ql/7i4+OVmJjo9RUsx+emKc46+C8HcVaLjs9NC9pzAkCoRFLx/Ze//KX+9re/DbrsqNcJJ5wgqbsxdVxcnM4//3w9+uijnp49/rS2tuqvf/2rnnjiCa8B1GuvvaZ9+/Zp/fr1mjhxoo499ljP10h3YTn55JMlSW+88YbnXEZGhj7//HPPEq7enN566y3PawrEF77wBY0fP17Vff4a29bWprfffntEufZ6+eWXlZubq8svv1xf/vKXdeyxx+rf//73kI+rqalRWlqa7Hb7YT1/tIn28ZM0emOo5Jwc2RwOyV+B1WKRzelUck5OUJ4PAEIlkgrvY2Hs1KuqqmrAcUZGxqD3O+qoo9TR0aFPPvnEc65v8/HRMmvWLL3++utevTer/cygq6mp0Ze//OVRzSdqClPHHHOMHA6H1zrK9vZ2bdu2zTNlbt68efr444+1Y8cOT8w//vEPdXV1eZYkzJs3T1u2bPGqQm7YsEGzZs3S1KlTPTH912tu2LDB8zyB5BJKb1Z+oC7DV/OV/+gyTL1Z6bvxHABEssy8dNnTEiR/9XeLZJ+RqMy8wNfpj9Ts2bO1aNEi3XXXXV7nf/zjH2vlypV6+eWX9d5776mqqkrf//73ddRRR3k+F1atWqXp06frtNNO0x/+8Aft2rVL//73v/WXv/xFW7duldVqlSQ98sgjSk5O1vnnn6+srCzP10knnaSzzjorqI08jzrqKJ1yyil66aWXPOeOO+44nXPOOfrRj36kl156Sf/85z/1ve99T9OnT9c555wT8L0TEhJ0ySWX6Nprr9WLL76o2tpaLV68WHFxcYc10+q4447T9u3b5Xa79fbbb2v58uV+B1J9VVRUaMGCBSN+3mjF+Mk/i9WqrOLinoN+P5M9x1nLl8vS898mAESLSCq8j4WxU6+XX35Zt99+u95++22VlJToqaee0pIlSwa935w5c3TEEUdo2bJl+ve//63HHntMDz30UNDy9ee73/2uurq6VFhYqLq6Orndbv3617+W5N1Ufvfu3dqzZ8+AHpLBFlGFqf3793uqm1J3k8ydO3eqvr5eFotFS5cu1S233KJ169bp9ddf1/e//31NmzbNszV1RkaGzjzzTP3oRz/SK6+8opdffllXXHGFLrzwQk8n/+9+97uaMGGCFi9erNraWj355JNas2aNrrnmGk8eS5Ys0fPPP6/f/OY3evPNN3XzzTdr+/btuuKKKyQpoFxCKVKWuQDAaLBa41S4xtV90H981XNcuHqBrNbQfKStWLFiwDTo/Px8VVVV6Tvf+Y6+9KUv6bzzzpPNZtMLL7yg5ORkSd19ml555RV9//vf1x133KHTTjtNs2fP1s0336wLLrhA9913nyTpD3/4g7797W/7LN6cd955WrdunVpaWoL2en74wx8O6LPw4IMP6tRTT9U3vvENzZs3T6Zp6rnnnhswJXwov/3tbzVv3jx94xvfUH5+vr7yla8oIyPDq5/BcP33f/+3Fi5cqAsuuEBz5sxRa2vrgO2i++vs7NTTTz+tH/3oRyN+3kjG+GnknC6XsktKZOvX98rmcCi7pEROlysseQHA4Yi0wvtYGDtJ0k9+8hNt375dX/7yl3XLLbfot7/9rVxDfI4ceeSRevTRR/Xcc89p9uzZevzxx3XzzTcHLVd/EhMT9be//U07d+7UySefrJ///Ocq7vmZ6TtOe/zxx7VgwQIdffTRo5uQGUFefPFFU917LHl9XXLJJaZpmmZXV5e5fPlyMzU11YyPjze/9rWvmW+99ZbXPVpbW82LLrrInDx5spmYmGhedtllZkdHh1fMP//5T/OrX/2qGR8fb06fPt385S9/OSCXP/3pT+aXvvQlc8KECWZmZqb57LPPel0PJJfBtLW1mZLMtra2gB/jzz9ffNc8WyuG/Prni+8e9nMBwHB99tln5htvvGF+9tlnh3Wfl//8hnlJ2p1e/1+7ZMZq8+U/vxGkTMemTz/91JwxY4ZZWVk56s+1f/9+Mykpybz//vtH/bn6uueee8z/+q//GjRmsJ/TYH5mj4axNH4yzdH5fnR9/rn50dat5gd//av50datZtfnnwft3gAwXMEaO334/PPm+txcc90XvuD5Wv+Vr5gfPv98kDIdm3yNnY4++mjzzjvvDF9SQfDoo4+a48ePNz/99FPTNE3zwIEDZnp6uvnSSy/5fUywxk8W0/S1hyRGW3t7u5KSktTW1nbYvRIMo0uLZ96llj0dvrdTt0j2tEQ98O6VIZtRAAC9Ojs79e677+qYY445rJkyUvf/72or6rWvYb+mOicrMy+d/68FwaZNm9TR0aFvfvObQb3va6+9pjfffFOnnXaa2tratGLFCm3atEn/+te/Qtrr6f7771deXt6gOwIO9nMazM9sHD6+HwBiXTDHTqZhqLW6WgeamxWfkqLknByWKAdB/7HTzJkztXTpUi1dujS8iQ3DH//4R33hC1/Q9OnT9c9//lNXXHGFTj/9dD366KOSpH/961964YUX9N///d9+7xGs8dO4w385CLfeZS6rCsq6l7X0LU6FYZkLAIwWqzVOJ54+M9xpxJzTTz991O7961//Wm+99ZYmTJigU089VRUVFSFvQP7DH/4wpM+H6MQvbwBikcVqlX3u3HCnEXNGc+wUKo2NjSouLlZjY6OcTqe+853v6NZbb/Vc720UHwoUpmJE7sIMLSsrUOkSt1o+6PCcT7QfoctLvq7chYPvBgAAQLB9+ctf9mqoDUSqBrdbNStWqLOx0XPO5nAoq7iYPlMAgCHt3r073CkM23XXXafrrrsu3GlIirDm5zg8uQsz9MM7FyjxqCM859o/+lT3X7NeleV1YcwMAAAgMjW43dpeVORVlJKkzqYmbS8qUoPbHabMAAAYGyhMxZDK8jr98vw/q/2jT73Ot+zp0KqCMopTAMKKloaIZPx8jk2mYahmxQrJ1/e/51zNypUyDSPEmQEAn02IfMH6GaUwFSMMo0ulS9y+m5/3nCtdul6G0eUjAABGz/jx4yVJn3766RCRQPj0/nz2/rxibGitrh4wU8qLaaqzoUGt1dWhSwrAmMfYCdEiWOMnekzFiNqKeq/eUgOYUsv77aqtqKdxMICQslqtmjJlipqbmyVJRxxxhCwWS5izArqZpqlPP/1Uzc3NmjJliqw0ux5TDvT8fylYcQAQDIydEOmCPX6iMBUj9jXsD2ocAASTw+GQJM8AC4g0U6ZM8fycYuyIT0kJahwABAtjJ0SDYI2fKEzFiKnOyUGNA4BgslgscjqdSklJ0aFDh8KdDuBl/PjxzJQao5JzcmRzONTZ1OS7z5TFIpvDoeScnNAnB2BMY+yESBfM8ROFqRiRmZcue1qCWvZ0+O4zZZHsaYnKzEsPeW4A0MtqtVIAABAxLFarsoqLtb2oSLJYvItTPctmspYvl4X/bwEIE8ZOGAtofh4jrNY4Fa5xdR/0X37cc1y4eoGsVr7lAAAAvZwul7JLSmRLTfU6b3M4lF1SIqfLFabMAAAYG5gxFUNyF2ZoWVmBSpe4vRqh29MSVbh6gXIXZoQxOwAAgMjkdLnkyM9Xa3W1DjQ3Kz4lRck5OcyUAgAgBChMxZjchRmac84s1VbUa1/Dfk11TlZmXjozpQAAAAZhsVplnzs33GkAADDmUJiKQVZrnE48fWa40wAAAIg6pmEwcwoAgBCiMAUAAABIanC7VbNihTobGz3nbA6HsoqL6TUFAMAoYX1XjDKMLu3atFubH6/Rrk27ZRhd4U4JAAAgYjW43dpeVORVlJKkzqYmbS8qUoPbHabMAACIbcyYikGV5XUDGqAnHnWELr/n6/pqwQlhzAwAACDymIahmhUrJNP0cdGULBbVrFwpR34+y/oAAAgyZkzFmMryOq0qKPMqSklS+0ef6pff+bP+cN3GMGUGAAAQmVqrqwfMlPJimupsaFBrdXXokgIAYIygMBVDDKNLpUvcko8/9vUqv2OrXnrqjdAlBQAAEOEONDcHNQ4AAASOwlQMqa2oHzBTypd7iv5OzykAAIAe8SkpQY0DAACBozAVQ/Y17A8orv2jT1VbUT/K2QAAAESH5Jwc2RwOyWLxHWCxyOZ0KjknJ7SJAQAwBlCYiiFTnZMDjg20iAUAABDrLFarsoqLew76Fad6jrOWL6fxOQAAo4DCVAzJzEtX4lFHBBQ7nCIWAABArHO6XMouKZEtNdXrvM3hUHZJiZwuV5gyAwAgto0LdwIIHqs1Tpff83X98jt/HjTOPiNRmXnpIcoKAAAgOjhdLqWecYbeffRRfVpfryPS03XM976nuAkTwp0aAAAxi8JUjPlqwQlaeO2HKr9jq+8Ai1S4eoGsVibLAQAA9NXgdqtmxQp1NjZ6zv3fAw8oq7iYGVMAAIwSqhMx6Ae35+uGP52nRPtEr/P2GYlaVlag3IUZYcoMAAAgMjW43dpeVORVlJKkzqYmbS8qUoPbHabMAACIbcyYilFf/c4JmrfweNVW1Gtfw35NdU5WZl46M6UAAAD6MQ1DNStWSKbp46IpWSyqWblSjvx8GqADABBkVCkAAAAwprVWVw+YKeXFNNXZ0KDW6urQJQUAwBjBjKkYVVlep9IlbrV80OE5Z09LUOEaF0v5AAAA+jjQ3BzUOAAAEDhmTMWgyvI6rSoo8ypKSVLLng6tKihTZXldmDIDAACIPPEpKUGNAwAAgaMwFWMMo0ulS9ySjxYJvedKl66XYXSFNC8AAIBIlZyTI5vDIVksvgMsFtmcTiXn5IQ2MQAAxgAKUzGmtqJ+wEwpL6bU8n67aivqQ5cUAABABLNYrcoqLu456Fec6jnOWr6cxucAAIwCClMxZl/D/qDGAQAAjAVOl0vZJSWypaZ6nbc5HMouKZHT5QpTZgAAxDaan8eYqc7JQY0DAAAYK5wulxz5+WqtrtaB5mbFp6QoOSeHmVIAAIwiClMxJjMvXfa0BLXs6fDdZ8oi2dMSlZmXHvLcAAAAIp3FapV97txwpwEAwJjBUr4YY7XGqXBNz1Tz/v07e44LVy+Q1cq3HgAAwB/TMNRSVaU969appapKpmGEOyUAAGISM6ZiUO7CDC0rK1DpErdXI3R7WqIKVy9Q7sKMMGYHAAAQ2RrcbtWsWKHOxkbPOZvDoaziYnpNAQAQZBSmYlTuwgzNOWeWaivq1bqnQ20ffaKkoyZp8pETZRhdzJgCAADwocHt1vaiIsn07onQ2dSk7UVFNEIHACDIKEzFMKs1Tvv3fqaHb3ih38ypBBWucTFzCgAAoA/TMFSzYsWAolT3RVOyWFSzcqUc+fk0RAcAIEiYNhPDKsvrtKqgzKsoJUktezq0qqBMleV1YcoMAAAg8rRWV3st3xvANNXZ0KDW6urQJQUAQIyjMBWjDKNLpUvcvnfm6zlXunS9DKMrpHkBAABEqgPNzUGNAwAAQ6MwFaNqK+oHzJTyYkot77ertqI+dEkBAABEsPiUlKDGAQCAoVGYilH7GvYHNQ4AACDWJefkyOZwSBaL7wCLRTanU8k5OaFNDACAGEZhKkZNdU4OahwAAECss1ityiou7jnoV5zqOc5avpzG5wAABBGFqRiVmZcue1qC5OcPfrJI9hmJysxLD2leAAAAkczpcim7pES21FSv8zaHQ9klJXK6XGHKDACA2DQu3AlgdFitcSpc49KqgrLu4pSPJuiFqxfIaqU2CQAA0JfT5ZIjP1+t1dU60Nys+JQUJefkMFMKAIBRQGEqhuUuzNCysgL9rvBZdbR+5nUt4UhbmLICAACIfBarVfa5c8OdBgAAMY/pMmNA/6KUJHXs7dSqgjJVlteFISMAAAAAAAAKUzHNMLpUusTt+2LP0r7SpetlGF2hSwoAAAAAAKAHhakYVltRr5YPOvwHmFLL++2qragPXVIAAABRwjQMtVRVac+6dWqpqpJpGOFOCQCAmEOPqRi2r2F/UOMAAADGiga3WzUrVqizsdFzzuZwKKu4mJ35AAAIImZMxbCpzslBjQMAABgLGtxubS8q8ipKSVJnU5O2FxWpwe2nVQIAABg2ClMxLDMvXfa0BMniJ8Ai2WckKjMvPaR5AQAARCrTMFSzYoVkmj4udp+rWbmSZX0AAAQJhakYZrXGqXBNz1Tz/sWpnuPC1QtktfJjAAAAIEmt1dUDZkp5MU11NjSotbo6dEkBABDDqEjEuNyFGVpWViD79ASv8/a0RC0rK1DuwowwZQYAABB5DjQ3BzUOAAAMjubnY0DuwgzNOWeWaivqta9hv6Y6JyszL52ZUgAAAP3Ep6QENQ4AAAyOwtQYYbXG6cTTZ4Y7DQAAgIiWnJMjm8OhzqYm332mLBbZHA4l5+SEPjkAAGIQU2YAAACAHharVVnFxT0H/Zp09hxnLV8ui9Ua4swAAIhNFKYAAACAPpwul7JLSmRLTfU6b3M4lF1SIqfLFabMAACIPSzlAwAAAPpxulxy5OertbpaB5qbFZ+SouScHGZKAQAQZBSmAAAAAB8sVqvsc+eGOw0AAGIahakxxjC62J0PAAAAAABEBApTY0hleZ1Kl7jV8kGH55w9LUGFa1zKXZgRxswAAAAil2kYLOkDAGCUUJgaIyrL67SqoEzqt+txy54OrSoo07KyAopTAAAA/TS43apZsUKdjY2eczaHQ1nFxTRBBwAgCFjDNQYYRpdKl7gHFKUkec6VLl0vw+gKaV4AAACRrMHt1vaiIq+ilCR1NjVpe1GRGtzuMGUGAEDsoDA1BtRW1Hst3xvAlFreb1dtRX3okgIAAIhgpmGoZsUKyfTxl72eczUrV8o0jBBnBgBAbKEwNQbsa9gf1DgAAIBY11pdPWCmlBfTVGdDg1qrq0OXFAAAMYjC1Bgw1Tk5qHEAAACx7kBzc1DjAACAbxSmxoDMvHTZ0xIki58Ai2SfkajMvPSQ5gUAABCp4lNSghoHAAB8ozA1BlitcSpcM8iuMaY0/8JMWa38OAAAAEhSck6ObA6HZPHzlz2LRTanU8k5OaFNDACAGEMlYozIXZihhT+d5/d6+a+3qrK8LoQZAQAARC6L1aqs4uKeg37FqZ7jrOXLZbFaQ5wZAACxhcLUGGEYXdryeM2gMaVL18swukKUEQAAQGRzulzKLimRLTXV67zN4VB2SYmcrkFmpAMAgICMC3cCCI3ainq1fNDhP8CUWt5vV21FvU48fWbI8gIAAIhkTpdLjvx8tVZX60Bzs+JTUpSck8NMKQAAgoQZU2PEvob9QY0DAAAYKyxWq5JzchSfkqIDzc1qra6WaRjhTgsAgJgQVYUpwzC0fPlyHXPMMZo4caK++MUvauXKlTJN0xNjmqaKi4vldDo1ceJE5efn65133vG6z969e7Vo0SIlJiZqypQpWrx4sfbv9y7I7Nq1S3l5ebLZbJoxY4Zuv/32Afk89dRTOv7442Wz2TR79mw999xzo/PCg2Cqc3JQ4wAAQHRg/HT4GtxubZw/X1sXLdKrV1+trYsWaeP8+Wpwu8OdGgAAUS+qClO/+tWv9Pvf/15333236urq9Ktf/Uq33367fve733libr/9dt1111269957tW3bNk2aNEkul0udnZ2emEWLFqm2tlYbNmzQM888oy1btqiwsNBzvb29XQsWLNDRRx+tHTt26I477tDNN9+s0tJST0xlZaUuuugiLV68WK+99prOPfdcnXvuuaqpGbyPU7hk5qXLnpYg+dlYRhbJPiNRmXnpIc0LAACMLsZPh6fB7db2oiJ1NjZ6ne9satL2oiKKUwAAHCaL2ffPZRHuG9/4hlJTU/XAAw94zp133nmaOHGiHn30UZmmqWnTpuknP/mJfvrTn0qS2tralJqaqoceekgXXnih6urqdMIJJ6i6ulrZ2dmSpOeff15nnXWWPvjgA02bNk2///3v9fOf/1yNjY2aMGGCJOmGG27Q008/rTfffFOSdMEFF+iTTz7RM88848ll7ty5Ovnkk3XvvfcO+Vra29uVlJSktrY2JSYmBu09GkxleZ1WFZR1H/T9rvcUq5aVFSh3YUZIcgEAIFqE4zM7mGJp/CSF9vthGoY2zp8/oCjlYbHI5nAof/Nmek4BANDHcD6vo2rGVG5url544QW9/fbbkqR//vOfeumll/T1r39dkvTuu++qsbFR+fn5nsckJSVpzpw52rp1qyRp69atmjJlimdQJUn5+fmKi4vTtm3bPDHz58/3DKokyeVy6a233tK+ffs8MX2fpzem93n6O3DggNrb272+Qi13YYaWlRXIPj3B67w9LZGiFAAAMSqax09SeMdQrdXV/otSkmSa6mxoUGt1dchyAgAg1kTVrnw33HCD2tvbdfzxx8tqtcowDN16661atGiRJKmxZ+CQ2m9L39TUVM+1xsZGpaSkeF0fN26cjjzySK+YY445ZsA9eq9NnTpVjY2Ngz5Pf7fddpt+8YtfjORlB1XuwgzNOWeWaivqta9hv6Y6JyszL11Wa1TVKAEAQICiefwkhXcMdaC5OahxAABgoKiqRvzpT3/S2rVr9dhjj+nVV1/Vww8/rF//+td6+OGHw53akH72s5+pra3N8/X++++HLRerNU6Zeema6pysfQ37VVtRL8PoCls+AABg9ETz+EkK7xgqvl8x7nDjAADAQFE1Y+raa6/VDTfcoAsvvFCSNHv2bL333nu67bbbdMkll8jhcEiSmpqa5HQ6PY9ramrSySefLElyOBxq7vdXrc8//1x79+71PN7hcKipqckrpvd4qJje6/3Fx8crPj5+JC876CrL61S6xK2WDzo85+xpCSpc42I5HwAAMSaax09SeMdQyTk5sjkc6mxqkny1Ze3pMZWckxP65AAAiBFRNWPq008/VVycd8pWq1VdXd2zfY455hg5HA698MILnuvt7e3atm2b5s2bJ0maN2+ePv74Y+3YscMT849//ENdXV2aM2eOJ2bLli06dOiQJ2bDhg2aNWuWpk6d6onp+zy9Mb3PE6l6G6D3LUpJUsueDq0qKFNleV2YMgMAAKOB8dPIWaxWZRUX9xz029q45zhr+XIanwMAcBiiqjD1zW9+U7feequeffZZ7d69W3/5y1/029/+Vt/+9rclSRaLRUuXLtUtt9yidevW6fXXX9f3v/99TZs2Teeee64kKSMjQ2eeeaZ+9KMf6ZVXXtHLL7+sK664QhdeeKGmTZsmSfrud7+rCRMmaPHixaqtrdWTTz6pNWvW6JprrvHksmTJEj3//PP6zW9+ozfffFM333yztm/friuuuCLk70ugDKNLpUvc3jvy9eo5V7p0Pcv6AACIIYyfDo/T5VJ2SYls/Xpj2RwOZZeUyOlyhSkzAABig8U0fc1LjkwdHR1avny5/vKXv6i5uVnTpk3TRRddpOLiYs8OMKZp6qabblJpaak+/vhjffWrX9U999yjL33pS5777N27V1dccYX+9re/KS4uTuedd57uuusuTZ482ROza9cuFRUVqbq6Wna7XVdeeaWuv/56r3yeeuop3Xjjjdq9e7eOO+443X777TrrrLMCei3h2Hp616bdWnbGI0PGrXrxYp14+szRTwgAgCgQjs/sYIql8ZMUvu+HaRieXfoO7N2r+ORk2VJTlZyTw4wpAAD6Gc7ndVQVpmJJOAZVmx+v0R3f/cuQcdc+9m39v4uyQpARAACRL9oLU7EmnN+PBrdbNStWqLPPLoI2h0NZxcXMnAIAoI/hfF5H1VI+HJ6pzslDBw0jDgAAYKxocLu1vajIqyglSZ1NTdpeVKQGtztMmQEAEN0oTI0hmXnpsqclSBY/ARbJPiNRmXnpIc0LAAAgkpmGoZoVK3zvzNdzrmblSpmGEeLMAACIfhSmxhCrNU6Fa3qmmfcvTvUcF65eIKuVHwsAAIBevb2l/DJNdTY0qLW6OnRJAQAQI6hAjDG5CzO0rKxA9ukJXuftaYlaVlag3IUZYcoMAAAgMh1obg5qHAAA+I9x4U4AoZe7MENzzpml1ze9p9c37ZYkzT79aM1mJz4AAIAB4lNSghoHAAD+g8LUGLXtr2+pdIlbLR90SJKevOUl2dMSVLjGxawpAACAPpJzcmRzONTZ1OS7z5TFIpvDoeScnNAnBwBAlGMp3xhUWV6nVQVlnqJUr5Y9HVpVUKbK8rowZQYAABB5LFarsoqLew76NersOc5avlwWqzXEmQEAEP0oTI0xhtGl0iVuyccf+3rPlS5dL8PoCmleAAAAkczpcim7pES21FSv8+OnTtWpv/udnC5XmDIDACC6UZgaY2or6gfMlPJiSi3vt6u2oj50SQEAAEQBp8ulzJ//XBOOPNJz7tDevaq95RY1uN1hzAwAgOhFYWqM2dewP6hxAAAAY0WD260dV12lg3v3ep3vbGrS9qIiilMAAIwAhakxZqpzclDjAAAAxgLTMFSzYoXv5uc952pWrpRpGCHODACA6EZhaozJzEuXPS1BsvgJsEj2GYnKzEsPaV4AAACRrLW6Wp2Njf4DTFOdDQ1qra4OXVIAAMQAClNjjNUap8I1Pc05+xeneo4LVy+Q1cqPBgAAQK8Dzc1BjQMAAN2oPoxBuQsztKysQPbpCV7n7WmJWlZWoNyFGWHKDAAAIDLFp6QENQ4AAHQbF+4EEB65CzM055xZqq2o176G/ZrqnKzMvHRmSgEAAPiQnJMjm8OhzqYm332mLBbZHA4l5+SEPjkAAKIYhakxzGqN04mnzwx3GgAAABHPYrUqq7hY24uKJIvFuzhl6e6HkLV8uSxWa5gyBAAgOjE9BgAAAAiA0+VSdkmJbKmpXudtDoeyS0rkdLnClBkAANGLGVMAAABAgJwulxz5+d279DU16UBrq+KPPFLjk5JkGgYzpgAAGCYKUwAAAMAwWKxWHWprU93tt6uzsdFz3uZwKKu4mJlTAAAMA0v5AAAAgGFocLu1vajIqyglSZ1NTdpeVKQGtztMmQEAEH0oTAEAAAABMg1DNStW+N6Zr+dczcqVMg0jxJkBABCdKEzBwzC6tGvTbm1+vEa7Nu2WYXSFOyUAAICI0lpdPWCmlBfTVGdDg1qrq0OXFAAAUYweU5AkVZbXqXSJWy0fdHjO2dMSVLjGpdyFGWHMDAAAIHIcaG4OahwAAGMdM6agyvI6rSoo8ypKSVLLng6tKihTZXldmDIDAACILPEpKUGNAwBgrKMwNcYZRpdKl7glH20Ses+VLl3Psj4AAABJyTk5sjkcksXiO8Bikc3pVHJOTmgTAwAgSlGYGuNqK+oHzJTyYkot77ertqI+dEkBAABEKIvVqqzi4p6DfsWpnuOs5ctlsVpDnBkAANGJwtQYt69hf1DjAAAAYp3T5VJ2SYlsqale520Oh7JLSuR0ucKUGQAA0Yfm52PcVOfkoMYBAACMBU6XS478/O5d+pqadKC1VfFHHqnxSUkyDYMZUwAABIjC1BiXmZcue1qCWvZ0+O4zZZHsaYnKzEsPeW4AAACRzGK16lBbm+puv12djY2e8zaHQ1nFxcycAgAgACzlG+Os1jgVrukZNPXv4dlzXLh6gaxWflQAAAD6anC7tb2oyKsoJUmdTU3aXlSkBrc7TJkBABA9qDZAuQsztKysQPbpCV7n7WmJWlZWoNyFGWHKDAAAIDKZhqGaFSsk08eU855zNStXyjSMEGcGAEB0YSkfJHUXp+acM0uvb9qt1ze9J0maffpMzT796DBnBgAAEHlaq6sHzJTyYprqbGhQa3W17HPnhi4xAACiDIUpeGz761sqXeJWywcdkqQnb3lJ9rQEFa5xMWsKAACgjwPNzUGNAwBgrGIpHyRJleV1WlVQ5ilK9WrZ06FVBWWqLK8LU2YAAACRJz4lJahxAACMVRSmIMPoUukSt+9d+XrOlS5dL8PoCmleAAAAkSo5J0c2h0Oy9N89pofFIpvTqeScnNAmBgBAlKEwBdVW1A+YKeXFlFreb1dtRX3okgIAAIhgFqtVWcXFPQf9ilM9x1nLl8titYY4MwAAoguFKWhfw/6gxgEAAIwFTpdL2SUlsqWmep23ORzKLimR0+UKU2YAAEQPmp9DU52TgxoHAAAwVjhdLjny89VaXa0Dzc2KT0lRck4OM6UAAAgQM6agzLx02dMSJD8tEmSR7DMSlZmXHtK8AAAAooHFapV97lxNO/tsSdKHzz6rlqoqmYYR5swAAIh8zJiCrNY4Fa5xaVVBWXdxqn8TdFP64W/+S1YrdUwAAABfGtxu1axYoc7GRs85m8OhrOJilvQBADAIKg2QJOUuzNCysgLZpyf4vH7/NetVWV4X4qwAAAAiX4Pbre1FRV5FKUnqbGrS9qIiNbjdYcoMAIDIR2EKHrkLM/TDOxf4vNayp0OrCsooTgEAAPRhGoZqVqyQzP5TzuU5V7NyJcv6AADwg8IUPAyjS/dfvd73xZ6xVunS9TKMrtAlBQAAEMFaq6sHzJTyYprqbGhQa3V16JICACCKUJiCR21FvVo+6PAfYEot77ertqI+dEkBAABEsAPNzUGNAwBgrKEwBY99DfuDGgcAABDr4lNSghoHAMBYQ2EKHlOdk4MaBwAAEOuSc3Jkczgki8V3gMUim9Op5Jyc0CYGAECUoDAFj8y8dNnTEiQ/4ypZJPuMRGXmpYc0LwAAgEhlsVqVVVzcc9BvENVznLV8uSxWa4gzAwAgOlCYgofVGqfCNa7ug/7FqZ7jwtULZLXyYwMAANDL6XIpu6REttRUr/M2h0PZJSVyulxhygwAgMg3LtwJILLkLszQsrIClS5xezVCt6clqnD1AuUuzAhjdgAAAJHJ6XLJkZ/v2aXvwN69ik9O1vikJJmGwYwpAAD8oDCFAXIXZmjOObP0+qbden3Te5Kk2afP1OzTjw5zZgAAAJHLYrXqUFub6u64Q52NjZ7zNodDWcXFzJwCAMAHClPwadtf3/KaNfXkLS/JnpagwjUuZk0BAAD40OB2a3tRkWSaXuc7m5q0vaiIZX0AAPhAsyAMUFlep1UFZV5L+SSp5YMOrTqvTI+v2CzD6ApTdgAAAJHHNAzVrFgxoCjVfbH7XM3KlTINI8SZAQAQ2ShMwYthdKl0iVvyMabqtfamLfrBzLtUWV4XusQAAAAiWG9vKb9MU50NDWqtrg5dUgAARAEKU/BSW1E/YKaUL60fdGhVQRnFKQAAAEkHmpuDGgcAwFhBYQpe9jXsH1Z86dL1LOsDAABjXnxKSlDjAAAYKyhMwctU5+TAg02p5f121VbUj15CAAAAUSA5J0c2h0OyWHwHWCyyOZ1KzskJbWIAAEQ4ClPwkpmXLntaguRnTOXLcGdZAQAAxBqL1aqs4mL/Aaap6d/4hixWa+iSAgAgClCYgherNU6Fa4a3jfGwZlkBAADEKKfLpS/+8Id+r//7/vvV4HaHMCMAACIfhSkMkLswQ8vKCpQ8feiCkz0tUZl56SHICgAAILKZhqE9f/vboDE1K1fKNIwQZQQAQOSjMAWfchdm6A/vLdF3f/H/Bo078NlBbfvrWyHKCgAAIHK1Vlers7HRf4BpqrOhQa3V1aFLCgCACEdhCn5ZrXH6bvF8LftzgRKSJ/qM6djbqVUFZaosrwtxdgAAAJHlQHNzUOMAABgLKExhSHPOmaUJNj+NOs3uf5QuXS/D6ApdUgAAABEmPiUlqHEAAIwFFKYwpNqKerXuGWTnPVNqeb9dtRX1oUsKAAAgwiTn5MjmcEgWP9sbWyyyOZ1KzskJbWIAAEQwClMY0r6GQYpSI4gDAACIRRarVVnFxT0H/YpTPcdZy5fLYvUzEx0AgDGIwhSGNNU59O58w4kDAACIVU6XS9klJbKlpnqdtzkcyi4pkdPlClNmAABEpnHhTgCRLzMvXfa0BLV80OE3xj4jUZl56SHMCgAAIDI5XS458vPVWl2tA83Nik9JUXJODjOlAADwgcIUhmS1xmn+RVkqv2Or35j5F2bKamUCHgAAgNS9rM8+d2640wAAIOJRScCQDKNLWx6vGTRmyxO17MoHAAAAAACGhcIUhlRbUT/oMj6JXfkAAAD8MQ1DLVVV2rNunVqqqmQaRrhTAgAgYrCUD0NiVz4AAICRaXC7VbNihTobGz3nbA6HsoqLaYQOAICYMYUAsCsfAADA8DW43dpeVORVlJKkzqYmbS8qUoPbHabMAACIHBSmMKTeXflk8RNgYVc+AACAvkzDUM2KFZJp+rjYfa5m5UqW9QEAxjwKUxiS1RqnwjU9U837F6d6jgtXL2BXPgAAgB6t1dUDZkp5MU11NjSotbo6dEkBABCBoq6SsGfPHn3ve99TcnKyJk6cqNmzZ2v79u2e66Zpqri4WE6nUxMnTlR+fr7eeecdr3vs3btXixYtUmJioqZMmaLFixdr/37v/ki7du1SXl6ebDabZsyYodtvv31ALk899ZSOP/542Ww2zZ49W88999zovOgIkLswQ8vKCmSfnuB13p6WqGVlBcpdmBGmzAAAwFAYP4XegebmoMYBABCroqowtW/fPn3lK1/R+PHj9fe//11vvPGGfvOb32jq1KmemNtvv1133XWX7r33Xm3btk2TJk2Sy+VSZ2enJ2bRokWqra3Vhg0b9Mwzz2jLli0qLCz0XG9vb9eCBQt09NFHa8eOHbrjjjt08803q7S01BNTWVmpiy66SIsXL9Zrr72mc889V+eee65qampC82aEQe7CDD2w+yqtevFiXfvYt7XqxYv1wLtXUpQCACCCMX4Kj/iUlKDGAQAQqyym6Wvhe2S64YYb9PLLL6uiosLnddM0NW3aNP3kJz/RT3/6U0lSW1ubUlNT9dBDD+nCCy9UXV2dTjjhBFVXVys7O1uS9Pzzz+uss87SBx98oGnTpun3v/+9fv7zn6uxsVETJkzwPPfTTz+tN998U5J0wQUX6JNPPtEzzzzjef65c+fq5JNP1r333jvka2lvb1dSUpLa2tqUmJh4WO8LAAAYPdH+mR1L4ycper4fpmFo4/z56mxq8t1nymKRzeFQ/ubNslitoU8QAIBRNJzP66iaMbVu3TplZ2frO9/5jlJSUvTlL39Z9913n+f6u+++q8bGRuXn53vOJSUlac6cOdq6daskaevWrZoyZYpnUCVJ+fn5iouL07Zt2zwx8+fP9wyqJMnlcumtt97Svn37PDF9n6c3pvd5+jtw4IDa29u9vgAAAEZbNI+fpOgdQ1msVmUVF/cc+NhBxjSV+fOfU5QCAIx5UVWY+r//+z/9/ve/13HHHSe3260f//jHuuqqq/Twww9Lkhp7GkympqZ6PS41NdVzrbGxUSn9pkyPGzdORx55pFeMr3v0fQ5/MY1+mlzedtttSkpK8nzNmDFj2K8/khhGl3Zt2q3Nj9do16bdMoyucKcEAAB8iObxkxTdYyiny6XskhLZ+r3mXrW33KIGtzvEWQEAEFnGhTuB4ejq6lJ2drZWrVolSfryl7+smpoa3XvvvbrkkkvCnN3gfvazn+maa67xHLe3t0fVwKqvyvI6lS5xq+WDDs85e1qCCte46DcFAECEiebxkxT9YyinyyXTMLTjyisHXOtsatL2oiJll5TI6XKFITsAAMIvqmZMOZ1OnXDCCV7nMjIyVF9fL0lyOBySpKamJq+YpqYmzzWHw6HmfruffP7559q7d69XjK979H0OfzG91/uLj49XYmKi11c0qiyv06qCMq+ilCS17OnQqoIyVZbXhSkzAADgSzSPn6ToH0OZhqHaW2/1c7G791TNypUyDSOEWQEAEDmiqjD1la98RW+99ZbXubfffltHH320JOmYY46Rw+HQCy+84Lne3t6ubdu2ad68eZKkefPm6eOPP9aOHTs8Mf/4xz/U1dWlOXPmeGK2bNmiQ4cOeWI2bNigWbNmeXawmTdvntfz9Mb0Pk8sMowulS5xS77a5fecK126nmV9AABEEMZP4dVaXa3OQZYqyjTV2dCg1urq0CUFAEAEiarC1NVXX62qqiqtWrVK//rXv/TYY4+ptLRURUVFkiSLxaKlS5fqlltu0bp16/T666/r+9//vqZNm6Zzzz1XUvdfCM8880z96Ec/0iuvvKKXX35ZV1xxhS688EJNmzZNkvTd735XEyZM0OLFi1VbW6snn3xSa9as8ZpGvmTJEj3//PP6zW9+ozfffFM333yztm/friuuuCLk70uo1FbUD5gp5cWUWt5vV21FfeiSAgAAg2L8FF4H+s00O9w4AABiTVT1mMrJydFf/vIX/exnP9OKFSt0zDHHaPXq1Vq0aJEn5rrrrtMnn3yiwsJCffzxx/rqV7+q559/XjabzROzdu1aXXHFFfra176muLg4nXfeebrrrrs815OSkrR+/XoVFRXp1FNPld1uV3FxsQoLCz0xubm5euyxx3TjjTdq2bJlOu644/T0008rKysrNG9GGOxr2B/UOAAAMPoYP4VXfL+m8YcbBwBArLGYpulrYRZGWXt7u5KSktTW1hY1vRJ2bdqtZWc8MmTcBTd+VSd97Rhl5qXLao2qSXkAAAwQjZ/ZsSzavh+mYWjj/PnqbGry9JTyYrHI5nAof/NmWazW0CcIAMAoGM7nNVUDBCwzL132tATJMnjck7e8pGVnPKLFM++iGToAABjTLFarsoqLew76DaJ6jrOWL6coBQAYsyhMIWBWa5wK1/RsZTxEcUpipz4AAABJcrpcyi4pkS011eu8zeFQdkmJnC5XmDIDACD8WMoXJtE2Db2vyvI6lS5xD94IvZdFsqcl6oF3r2RZHwAgKkXzZ3Ysiubvh2kYaq2u1oHmZsWnpCg5J4eZUgCAmDScz+uoan6OyJC7MENzzpml2op6/fOFd/XkLS/5D+6zU9+Jp88MWY4AAACRxmK1yj53rufYNAy1VFVRqAIAjGkUpjAiVmucTjx9Jjv1AQAAjECD262aFSvU2djoOWdzOJRVXMzSPgDAmMLaKhyWqc7JQY0DAACIdQ1ut7YXFXkVpSSps6lJ24uK1OB2hykzAABCj8IUDsuQO/VZJPuMRGXmpYc0LwAAgEhkGoZqVqyQfLV57TlXs3KlTMMIcWYAAIQHhSkclkF36us5Lly9gMbnAAAAklqrqwfMlPJimupsaFBrdXXokgIAIIyoFuCw5S7M0LKyAtmnJ3idt6clallZgXIXZoQpMwAAgMhyoLk5qHEAAEQ7mp8jKPru1LevYb+mOicrMy+dmVIAAAB9xKekBDUOAIBoR9UAQdO7U99Xzz9BkvTSn97Qrk27ZRhdYc4MAAAgMiTn5MjmcEgWPw06LRbZnE4l5+SENjEAAMKEGVMIqsryOpUucavlgw7POXtaggrXuFjSBwAAxjyL1aqs4mJtLyrqLk71b4Jumsr8+c9lsVrDkyAAACHGjCkETWV5nVYVlHkVpSSpZU+HVhWUqbK8LkyZAQAARA6ny6XskhLZUlN9Xq+95RY1uN0hzgoAgPCgMIWgMIwulS5xSz52Pu49V7p0Pcv6AAAA1F2cyvz5z31e62xq0vaiIopTAIAx4bALU5999pn27Nkz4Hxtbe3h3hpRpLaifsBMKS+m1PJ+u2or6kOXFAAAEYwx1NhmGoZqb73Vz8Xuv+rVrFwp0zBCmBUAAKF3WIWpsrIyHXfccTr77LN14oknatu2bZ5rF1988WEnh+ixr2F/UOMAAIhljKHQWl2tzsZG/wGmqc6GBrVWV4cuKQAAwuCwClO33HKLduzYoZ07d+rBBx/U4sWL9dhjj0mSzP6NHBHTpjonBzUOAIBYxhgKB5qbgxoHAEC0Crgwdd1116mzs9Pr3KFDh5Ta07Tx1FNP1ZYtW/S///u/WrFihSz+tsBFTMrMS5c9LUHy9223SPYZicrMSw9pXgAAhBtjKPgSn5IS1DgAAKJVwIWp1atXq62tTZJ06aWX6pNPPlFKSop27drliTnyyCO1YcMG1dXVeZ1H7LNa41S4xtV90H883XNcuHqBrFb67QMAxhbGUPAlOSdHNodD8leItFhkczqVnJMT2sQAAAixgKsE06ZN086dOyVJjzzyiD755BM98sgjSun3V5wJEybo8ccf1+bNm4OaKCJf7sIMLSsrkH16gtd5e1qilpUVKHdhRpgyAwAgfBhDwReL1aqs4uKeAx/FKdNU5rJlslitoU0MAIAQGxdo4E9+8hN985vf1Jw5cyRJa9eu1Ve+8hXNnj3bZ/xXvvKV4GSIqJK7MENzzpml2op67WvYr6nOycrMS2emFABgzGIMBX+cLpeyS0pUs2KFz0botbfeKovVKqfLFYbsAAAIDYs5jA6bu3bt0t/+9jctX75cX/jCF7R7925ZLBYde+yxOumkk3TyySfrpJNO0te//vXRzDkmtLe3KykpSW1tbUpMTAx3OgAAwI9gfGYzhgqeWBxDffj3v2vHFVcMvNAzkyq7pITiFAAgqgzn83pYhalexx13nLZu3apJkyZp165d2rlzp+erpqZGHR0dI05+rIjFQRUAALEomJ/ZjKEOX6yNoUzD0Mb5833OmJLU3WvK4VD+5s0s6wMARI3hfF4HvJSvr3feecfz73PmzPFMTZfY4hgAAMAfxlDor7W62n9RSpJMU50NDWqtrpZ97tzQJQYAQIiMqDA1GLY4Ri/D6KLXFAAAAWIMNTYdaG4OahwAANEm6IUpQJIqy+tUusStlg/+syTBnpagwjUuducDAADoEd9vd8bDjQMAINowfQVBV1lep1UFZV5FKUlq2dOhVQVlqiyvC1NmAAAAkSU5J0c2h8PT6HwAi0U2p1PJOTmhTQwAgBChMIWgMowulS5xS77aZPScK126XobRFdK8AAAAIpHFalVWcXHPgY/ilGkq/YILQpsUAAAhRGEKQVVbUT9gppQXU2p5v121FfWhSwoAACCCOV0uZZeUyJaa6vP626tXa+P8+Wpwu0OcGQAAo4/CFIJqX8P+oMYBAACMBU6XS/lbtuhLS5f6vN7Z1KTtRUUUpwAAMYfCFIJqqnNyQHF73tk7ypkAAABEn/onnvB9wezuiVCzcqVMwwhhRgAAjC4KUwiqzLx02dMSpCF2vH7s5s00QQcAAOijtbpanY2N/gNMU50NDWqtrg5dUgAAjDIKUwgqqzVOhWtcvpuf90MTdAAAgP840Nwc1DgAAKIBhSkEXe7CDC36xfzBg2iCDgAA4CU+JSWocQAARAMKUxgV045LDiiOJugAAADdknNyZHM4JIufnggWi2xOp5JzckKbGAAAo4jCFEZFoE3QA40DAACIdRarVVnFxT0HPopTpqn0Cy4IbVIAAIwyClMYFUM2QbdI9hmJysxLD2leAAAAkczpcim7pES21FSf199evVob589Xg9sd4swAABgdFKYwKjxN0KWBxSmLJFP6ynnHq7aingboAAAAfThdLuVv2aIvLV3q83pnU5O2FxVRnAIAxAQKUxg1uQsztKysQPbpCV7n4+K6K1V/Xf2Klp3xiBbPvEuV5XXhSBEAACBi1T/xhO8LZvf2xzUrV8o0jBBmBABA8FGYwqjKXZihB3ZfpVUvXqxzlp4mSeoyTK+Ylj0dWlVQRnEKAACgR2t1tTobG/0HmKY6GxrUWl0duqQAABgFFKYw6qzWOGXmpevlMj+Fp546VenS9SzrAwAAkHSguTmocQAARCoKUwiJ2op6tXzQ4T/AlFreb1dtRX3okgIAAIhQ8SkpQY0DACBSUZhCSOxr2B/UOAAAgFiWnJMjm8MhWfxtcSzZnE4l5+SEMCsAAIKPwhRCYqpzclDjAAAAYpnFalVWcXHPge/ilPHZZ2rcuDGEWQEAEHwUphASmXnpsqclSP7+6GeR7DMSlZmXHtK8AAAAIpXT5VJ2SYnGJyX5vH6orU3bi4rU4HaHODMAAIKHwhRCwmqNU+EaV/dB/+JUz3Hh6gWyWvmRBAAA6OXIz1dcfLzvi2b3DjI1K1fKNIwQZgUAQPBQBUDI5C7M0LKyAtmnJ3idt6clallZgXIXZoQpMwAAgMjUWl2tA01N/gNMU50NDWqtrg5dUgAABNG4cCeAsSV3YYbmnDNLtRX12tewX1Odk5WZl85MKQAAAB8ONDcHNQ4AgEhDYQohZ7XG6cTTZ4Y7DQAAgIgXn5ISUNwEu32UMwEAYHQwTQUAAACIUMk5ObI5HH535uv12k9/ShN0AEBUojAFAAAARCiL1aqs4uKeA//FqQPNzezQBwCIShSmAAAAgAjmdLmUXVIiW2qq/yB26AMARCkKUwAAAECEc7pcOvmOOwYPYoc+AEAUovk5IoZhdLFbHwAAgB8HW1oCimOHPgBANKEwhYhQWV6n0iVutXzQ4TlnT0tQ4RqXchdmhDEzAACAyBDoDn2BxgEAEAmYjoKwqyyv06qCMq+ilCS17OnQqoIyVZbXhSkzAACAyDHkDn0Wi2xOp5JzckKbGAAAh4HCFMLKMLpUusQtmT4u9pwrXbpehtEV0rwAAAAizZA79Jmm0i+4ILRJAQBwmChMIaxqK+oHzJTyYkot77ertqI+dEkBAABEqKF26Ht79WptnD9fDW53iDMDAGBkKEwhrPY17A8ornXPIMUrAACAMcTpcil/yxZ9aelSn9c7m5q0vaiI4hQAICpQmEJYTXVODijuvqvX02sKAACgj/onnvB9wezuh1CzcqVMwwhhRgAADB+FKYRVZl667GkJkp8enr3aWz6lEToAAECP1upqdTY2+g8wTXU2NKi1ujp0SQEAMAIUphBWVmucCte4hg6kEToAAIDHgebmoMYBABAuFKYQdrkLM7SsrECJ9omDB9IIHQAAQJIUn5IS1DgAAMKFwhQiQu7CDP1odQAzpxR4w3QAAIBYlZyTI5vDIVn89EOwWGRzOpWckxPaxAAAGCYKU4gYydMTAooLtGE6AABArLJYrcoqLu456Fec6jnOWr5cFqs1xJkBADA8FKYQMYZshG6R7DMSlZmXHtK8AAAAIpHT5VJ2SYlsqale520Oh760ZIm6DhxQS1UVO/MBACLauHAnAPTqbYS+qqCsuzhl9rnYU6wqXL1AViv1VAAAAKm7OOXIz1drdbUONDdr/3vv6b3HH9fbq1d7YmwOh7KKi+V0BdY2AQCAUOI3fESU3kbo9n7L+pLsR+icJadp8pET2ZUPAACgD4vVKvvcuYqLj9fba9boQFOT1/XOpiZtLypSg9sdpgwBAPDPYpqmOXQYgq29vV1JSUlqa2tTYmJiuNOJOIbRpdqKem3761t68dHX1d7ymeeaPS1BhWtcyl2YEcYMAQBjBZ/ZkYXvh2+mYWjj/PnqbGz0HWCxyOZwKH/zZvpOAQBG3XA+r5kxhYhktcZp/97P9Nc1r3gVpSSpZU+HVhWUqbK8LkzZAQAARJbW6mr/RSlJMk11NjSotbo6dEkBABAAClOISIbRpdIlbu8+U716zpUuXc+yPgAAAEkHmpuDGgcAQKhQmEJEqq2oV8sHHf4DTKnl/XbVVtSHLikAAIAIFZ+SEtQ4AABChcIUItK+hv0BxVX+uU67Nu1m5hQAABjTknNyZHM4JIvFb8z4KVNkdnXJNIwQZgYAwOCiujD1y1/+UhaLRUuXLvWc6+zsVFFRkZKTkzV58mSdd955auq3M0l9fb3OPvtsHXHEEUpJSdG1116rzz//3Ctm06ZNOuWUUxQfH69jjz1WDz300IDnLykp0cyZM2Wz2TRnzhy98soro/Eyx6SpzskBxT1z93YtO+MRLZ55Fz2nAAAIAOOn2GSxWpVVXNxz4Ls4dejjj1V18cXaOH8+O/QBACJG1Bamqqur9b//+7868cQTvc5fffXV+tvf/qannnpKmzdv1ocffqiFCxd6rhuGobPPPlsHDx5UZWWlHn74YT300EMq7v0gl/Tuu+/q7LPP1hlnnKGdO3dq6dKl+uEPfyh3nw/wJ598Utdcc41uuukmvfrqqzrppJPkcrnUzLr9oMjMS5c9LUHy/0c/LzREBwBgaIyfYpvT5VJ2SYlsqamDxnU2NWl7URHFKQBARLCYpumrvXRE279/v0455RTdc889uuWWW3TyySdr9erVamtr01FHHaXHHntMBQUFkqQ333xTGRkZ2rp1q+bOnau///3v+sY3vqEPP/xQqT0f2vfee6+uv/56ffTRR5owYYKuv/56Pfvss6qpqfE854UXXqiPP/5Yzz//vCRpzpw5ysnJ0d133y1J6urq0owZM3TllVfqhhtuGPI1sNXx0CrL67SqoKz7IJCfUotkT0vUA+9eKas1amuuAIAIEyuf2bEwfpJi5/sxmkzDUMu2bdpxxRU61NbmO8hikc3hUP7mzbJYraFNEAAQ84bzeR2Vv70XFRXp7LPPVn5+vtf5HTt26NChQ17njz/+eKWnp2vr1q2SpK1bt2r27NmeQZUkuVwutbe3q7a21hPT/94ul8tzj4MHD2rHjh1eMXFxccrPz/fE9HfgwAG1t7d7fWFwuQsztKysQPbpCYE9gIboAAD4FY3jJ4kx1EhYrFZZ4uL8F6UkyTTV2dCg1urq0CUGAIAP48KdwHA98cQTevXVV1Xt40O0sbFREyZM0JQpU7zOp6amqrGx0ROT2m96c+/xUDHt7e367LPPtG/fPhmG4TPmzTff9Jn3bbfdpl/84heBv1BI6i5OzTlnlmor6lX55zo9c/f2IR8TaON0AADGimgdP0mMoUbqQIDLIwONAwBgtETVjKn3339fS5Ys0dq1a2Wz2cKdzrD87Gc/U1tbm+fr/fffD3dKUcNqjdOJp89U7nkZAcUH2jgdAICxIJrHTxJjqJGKT0kJahwAAKMlqgpTO3bsUHNzs0455RSNGzdO48aN0+bNm3XXXXdp3LhxSk1N1cGDB/Xxxx97Pa6pqUkOh0OS5HA4Buwy03s8VExiYqImTpwou90uq9XqM6b3Hv3Fx8crMTHR6wvDM2RDdItkn5GozLz0kOYFAEAki+bxk8QYaqSSc3Jkczj87tAnSeOnTJHZ1SXTMEKYGQAA3qKqMPW1r31Nr7/+unbu3On5ys7O1qJFizz/Pn78eL3wwguex7z11luqr6/XvHnzJEnz5s3T66+/7rX7y4YNG5SYmKgTTjjBE9P3Hr0xvfeYMGGCTj31VK+Yrq4uvfDCC54YBJ/VGqfCNa7ug/5jrJ7jwtULaHwOAEAfjJ/GJovVqqzeXRP9FKcOffyxqi6+WBvnz2eHPgBA2ERVj6mEhARlZWV5nZs0aZKSk5M95xcvXqxrrrlGRx55pBITE3XllVdq3rx5mjt3riRpwYIFOuGEE3TxxRfr9ttvV2Njo2688UYVFRUpPj5ekvQ///M/uvvuu3XdddfpBz/4gf7xj3/oT3/6k5599lnP815zzTW65JJLlJ2drdNOO02rV6/WJ598ossuuyxE78bY1NsQvXSJWy0fdHjO29MSVbh6gXIXBrbcDwCAsYLx09jldLmUXVKimhUr1NnTC8yXzqYmbS8qUnZJiZwuVwgzBAAgygpTgbjzzjsVFxen8847TwcOHJDL5dI999zjuW61WvXMM8/oxz/+sebNm6dJkybpkksu0YoVKzwxxxxzjJ599lldffXVWrNmjdLS0nT//ffL1eeD+oILLtBHH32k4uJiNTY26uSTT9bzzz8/oKEngq9vQ/R9Dfs11TlZmXnpzJQCAGCEGD/FLqfLJUd+vlq2bdOOK67wvVOfaUoWi2pWrpQjP18WqzX0iQIAxiyLaZpmuJMYi9rb25WUlKS2tjZ6JQSZYXRRtAIABA2f2ZGF78fItFRVaeuiRUPGzVu7VvaemXIAAIzUcD6vY27GFMa2yvI6H8v8ElS4xsUyPwAAMGYd6NMfLBhxAAAEC9NIEDMqy+u0qqDMqyglSS17OrSqoEyV5XVhygwAACC84lNSAo4zDUMtVVXas26dWqqq2LUPADCqmDGFmGAYXSpd4pZ8LUw1JVmk0qXrNeecWSzrAwAAY05yTo5sDoc6m5q6e0r1Z7HI5nDo4N692jh/vlezdJvDoaziYhqjAwBGBb+hIybUVtQPmCnlxZRa3m9XbUV96JICAACIEBarVVnFxT0Hln4Xu4+nf+Mb2nHVVQN28Ovdta/B7Q5FqgCAMYbCFGLCvob9QY0DAACINU6XS9klJbL12wXR5nDo1N/9Tnv+9jffs6l6ztWsXMmyPgBA0LGUDzFhqnNyUOMAAABikdPlkiM/X63V1TrQ3Kz4lBQl5+Sotbp6wEwpL6apzoYGtVZXs2sfACCoKEwhJmTmpcuelqCWPR2++0xZJHtaojLz0kOeGwAAQCSxWK0Dikvs2gcACBeW8iEmWK1xKlzT05CzX9uE3uPC1QtofA4AAOBDoLv2TbDbRzkTAMBYw2/piBm5CzO0rKxA9ukJXueTpydo0c3zdeiAoV2bdsswusKUIQAAQGTq3bVvQGP0fnZeey1N0AEAQWUxTV8dDjHa2tvblZSUpLa2NiUmJoY7nZhiGF2qrajXvob92vPOXrnve1WtfXbss6clqHCNS7kLM8KYJQAgWvCZHVn4foyeBrdb24uKug/8/YrQU7jKLimR0+UKUWYAgGgznM9rZkwh5litcTrx9JkaH2/VYzdv9ipKSVLLng6tKihTZXldmDIEAACIPL279g26rI8d+gAAQUZhCjHJMLpUusTtuxF6z7nSpetZ1gcAANCH0+XSl3/968GD+uzQBwDA4aIwhZhUW1Gvln4zpbyYUsv77aqtqA9dUgAAAFHgYEtLQHHs0AcACAYKU4hJ+xr2BzUOAABgrAh0h75A4wAAGAyFKcSkqc7JQY0DAAAYK4bcoc9ikc3pVHJOTmgTAwDEJApTiEmZeemypyVI/nY8tkj2GYnKzEsPaV4AAACRzmK1Kqu4uOfAx2DKNJV+wQWhTQoAELMoTCEmWa1xKlzTs4Vx//FUz/GCH35ZL/3pDe3atJsm6AAAAH307tBnS031ef3t1au1cf58NbjdIc4MABBrLKZp+tq3DKOsvb1dSUlJamtrU2JiYrjTiVmV5XUqXeL2aoSemDxRpkx1tHZ6ztnTElS4xqXchRnhSBMAEMH4zI4sfD9CyzQMvX3PPXp79eqBF3tmU2WXlMjpcoU2MQBARBvO5zWFqTBhUBU6htGl2op67WvYrw/fadXam7YMDOqZRbWsrIDiFADAC5/ZkYXvR2iZhqGN8+ers7HRd4DFIpvDofzNm2WxWkObHAAgYg3n85qlfIh5VmucTjx9pr56/gly3/ea76Ce8mzp0vUs6wMAAOjRWl3tvyglSaapzoYGtVZXhy4pAEBMoTCFMaO2ot5rSd8AptTyfrtqK+pDlxQAAEAEO9DcHNQ4AAD6ozCFMWNfw/6gxgEAAMS6+JSUoMYBANAfhSmMGVOdk4MaBwAAEOuSc3Jkczg8jc4HsFhkczpldnVpz7p1aqmqkmkYoU0SABDVxoU7ASBUMvPSZU9LUMueDk9PKS8WyZ6WqMy8dEneTdOnOicrMy9dViu1XAAAMHZYrFZlFRdre1FRd3Gq775JPcfGZ5+p6uKLPadtDoeyiovZqQ8AEBB+y8aYYbXGqXBNzwCp/x/9eo4LVy+Q1RqnyvI6LZ55l5ad8Yju+O5ftOyMR7R45l2qLK8Lac4AAADh5nS5lF1SIltqqtf58VOmSJIOffyx1/nOpiZtLypSg9sdogwBANHMYpqmr7kjGGVsdRw+leV1Kl3i9mqEbp+RqMLVC5S7MEOV5XVaVVA2cFZVT/FqWVmBchdmhC5hAEBY8ZkdWfh+hI9pGGqtrtaB5mZNsNv12k9/qgNNTb6DLRbZHA7lb94si9Ua2kQBAGE3nM9rlvJhzMldmKE558zyuUzPMLpUusTte6mfKckilS5drznnzGJZHwAAGFMsVqvsc+dKklqqqvwXpSTJNNXZ0KDW6mrPYwAA8IXCFMYkqzVOJ54+c8D52op6r5lUA5hSy/vtqq2o9/l4AACAseBAc3NQ4wAAYxdTPoA+9jXsD2ocAABALIpPSQlqHABg7KIwBfQx1Tk5qHEAAACxKDknRzaHo3tnPl8sFsU7HDK7urRn3Tq1VFXJNIzQJgkAiAos5QP6yMxLlz0tQS17Onz3mZKUeNQROj43ze89DKPLZ/8qAACAWGGxWpVVXKztRUXdxam++yn1HHd1dqrq4os9p20Oh7KKi+V0ucKQMQAgUvHbMtCH1RqnwjU9gyU/fwBs/+hTFX7xblWW1w24Vllep8Uz79KyMx7RHd/9i5ad8YgWz7zLZywAAEA0c7pcyi4pkS011ev8+KQkSdKhjz/2Ot/Z1KTtRUVqcLtDlSIAIApYTNP0My8Eo4mtjiNbZXmdSpe4/TdC7ylaLSsrUO7CDM9jVhWUDZxp5SMWABA9+MyOLHw/Io9pGGqtrtaB5mZNsNu189pr1dnY6DvYYpHN4VD+5s2yWK2hTRQAEDLD+bxmxhTgQ+7CDJX++wolHnWE74Ce4lPp0vUyjC4ZRpdKl7h9L//rFwsAABBLLFar7HPnavq3viVLXJz/opQkmaY6GxrUWl0dugQBABGNHlOAH29WfqD2jz71H2BKLe+3q7aiXpL8z67qF3vi6TODmygAAECEONDcHFBcS2WlDjQ3Kz4lRck5OcyeAoAxjMIU4Me+hv1BjRtuLAAAQLSJT0kJKO6dkhLPv9MUHQDGNpbyAX5MdU4OOG44sQAAALEqOSdHNoeje2e+ANEUHQDGNgpTgB+ZeemypyX43Z1PFsk+I1GZeenDigUAAIhVFqtVWcXFPQcBFqd69mKqWblSpmGMUmYAgEhFYQrww2qNU+Ganinl/cdVPceFqxfIao0bViwAAEAsc7pcyi4pkS01NfAH0RQdAMYsfksGBpG7MEPLygpkn57gdd6elqhlZQXKXZgxolgAAIBY5nS5lL9li+atXatT7rxTxxUVBfS4QJunAwBih8U0TV8b3GOUtbe3KykpSW1tbUpMTAx3OhiCYXSptqJe+xr2a6pzsjLz0v3OfhpOLAAg8vGZHVn4fkSnlqoqbV20aMi4E37+c9nsdnbrA4AoN5zPa3blAwJgtcbpxNNnBj0WAABgLOhtit7Z1OTpKTVAXJzeuPVWzyG79QHA2MA0DiBEDKNLuzbt1ubHa7Rr024ZRle4UwIAAAiJgJqid3mPjXzt1mcahlqqqrRn3Tq1VFXRLB0AYgAzpoAQqCyvU+kSt1o+6PCcs6clqHCNi95TAABgTOhtil6zYoU6Gxv/cyEubkBRSlL3zCqLRTUrV8qRn6/GjRsHPJZZVQAQ/egxFSb0Rxg7KsvrtKqgTOr/X1rPHwuXlRVozjmz6EsFABGKz+zIwvcj+pmGodbqah1oblZnS4vX8j1/vrR0qd5es2bgMsCe2VfZJSUUpwAgggzn85rCVJgwqBobDKNLi2fe5TVTyotFSjxyosbbrGrds99zmtlUABA5+MyOLHw/Ysuedev06tVXDxk3PilJh9rafF+0WGRzOJS/eTPN0gEgQgzn85opGcAoqq2o91+UkiRTam/9zKsoJUktezq0qqBMleV1o5whAABA+MSnpAQU57coJUmmqc6GBrVWVwcpKwBAKFGYAkbRvob9Qwf50jOPsXTpepqkAwCAmNW7W5/fhugWi8ZPmRLQvQ40NwcvMQBAyFCYAkbRVOfkkT/YlFreb1dtRX3wEgIAAIggg+7W13N8zKWXBnSvQGdfAQAiC4UpYBRl5qXLnpbgaXQ+EiOedQUAABAFenfrs6Wmep23ORzKLinRly6/fPBZVZImHHmkjjzllNFOFQAwCihMAaPIao1T4ZqeHWJGWJw6rFlXAAAAUcDpcil/yxbNW7tWp9x5p+atXav8zZvldLkGn1XV4+DevXrhjDPU4HaHMGsAQDBQmAJGWe7CDC0rK5B9eoLXeXtaohKSbf4LVhbJPiNRmXnpo58kAABAmFmsVtnnztX0b31L9rlzvXbY8zerqq/OpiZtLyqiOAUAUWZcuBMAxoLchRmac84s1VbUa1/Dfk11TlZmXrq2/fUtrSoo6y5OmX0e0FOsKly9QFZrd/3YMLoGPL73GgAAQKxzulxKPeMMrc/N1aF9+wYGmKZksahm5Uo58vO9ClsAgMhFYQoIEas1TieePtPrXO9sqtIlbrV80OE5b09LVOHqBcpdmCFJqiyv8xGToMI1Lk8MAABArNv76qu+i1K9TFOdDQ1qra6Wfe7c0CUGABgxClNAmPmbTdU7G6qyvK57VpXp/biWPR1aVVCmZWUFFKcAAMCYcKC5OaC4lspKHWhuVnxKipJzcpg9BQARjMIUEAF8zaaSupfvlS5xDyhKSeo+Z5FKl67XnHNmsawPAADEvPiUlIDi3ikp8fy7zeFQVnGxnC7XaKUFADgM/CYLRLDainqv5XsDmFLL++2qragPXVIAAABhkpyTI5vD4Xd3Pl9oig4AkY3CFBDB9jXsD2ocAABANLNYrcoqLu45CLA4ZXZPPa9ZuVKmYYxSZgCAkaIwBUSwqc7JQY0DAACIdk6XS9klJbKlpgb+oJ6m6P/38MPas26dWqqqKFIBQISgxxQQwTLz0mVPS1DLng7ffaYs3Tv4Zealhzw3AACAcHG6XHLk56u1uloHmpvV8a9/efWV8ueNW2/1/Du9pwAgMjBjCohgVmucCtf0DJb6z1bvOS5cvYDG5wAAYMyxWK2yz52r6d/6luy5ucN+PL2nACAy8NssEOFyF2ZoWVmB7NMTvM7b0xK1rKxAuQszwpQZAABAZBhJU3R6TwFAZGApHxAFchdmaM45s1RbUa99Dfs11TlZmXnpPmdKGUaXXt/0nl7ftFuSNPv0ozX79JnMqgIAADGrtyn69qKi7uKU6asHgg89vadaq6uVnJPjWRoYn5Ki5JwcWazW0U0cACCLaQb6f20EU3t7u5KSktTW1qbExMRwp4MYUVlep98VPquO1s+8zick23Rl6TeYXQUAI8BndmTh+4HBNLjdqlmxQp2NjcN63DGXXaaGv//d63H0oAKAkRvO5zWFqTBhUIVgqyyv06rzygaNWfZnlv4BwHDxmR1Z+H5gKKZheGY+dba0eDU8H5aeZYHZJSUUpwBgmIbzec3aHiAGGEaX/nfJ0I07/3eJW4bRFYKMAAAAwqNvU/QvXHLJ4L2nLBYpzs+vRPSgAoCQoDAFxIDainq1ftAxZFzrBx2qragPQUYAAADh19t7qvugX3GqtxdV1yB/tOvTgwoAMDooTAExYF/D/lGJBQAAiHZOl0vZJSWypaZ6nbc5HDrmsssCukdnY6Naqqq0Z906tVRVMYMKAIKIXfmAGDDVOXlUYgEAAGKB0+WSIz9/wK57rdXVevfBB4d8fO2tt+rg3r2eYxqjA0DwUJgCYkBmXrqS0xKGXM6XnJagzLx0n9cMo0u1FfXa17BfU52TlZmXLquVSZUAACA29Pae6is5J0c2h0OdTU2enlK+9C1KSVJnU5O2FxV5NUbv23S9t/BlsVqD/0IAIMZQmAJigNUap/9e4xpyV77/XuPyWWyqLK9T6RK3WvoUtuxpCSpc42IXPwAAELN6e1BtLyr6T8+pQJimZLGoZuVKOfLz1bhxo2pWrFBnY6MnhFlVABAYpkMAMSJ3YYaW/blACckTB1xLSJ6oZX8u8Flkqiyv06qCMq+ilCS17OnQqoIyVZbXjVrOAAAA4eavB9X4I48c/IE9jdHfvucebS8q8ipKSf+ZVdXgHnrnZAAYyyymGeifBRBM7e3tSkpKUltbmxITE8OdDmKIYXTp9U3v6fVNuyVJs08/WrNPn+lzppRhdGnxzLsGFKU8LJI9LVEPvHsly/oAjFl8ZkcWvh8YLf2X4nU2Nem1a64Z8nHjk5J0qK3N90WLRTaHQ/mbN7OsD8CYMpzP66j6TfO2225TTk6OEhISlJKSonPPPVdvvfWWV0xnZ6eKioqUnJysyZMn67zzzlNTU5NXTH19vc4++2wdccQRSklJ0bXXXqvPP//cK2bTpk065ZRTFB8fr2OPPVYPPfTQgHxKSko0c+ZM2Ww2zZkzR6+88krQXzMwXFZrnE7+2jG6eOUZunjlGTr5a1/wW1Sqraj3X5SSJFNqeb9dtRX1o5QtAGC0MX4CAtPbg2r6t74l+9y5A2ZQ+eO3KCV5ZlW1VlcHKUsAiD1RVZjavHmzioqKVFVVpQ0bNujQoUNasGCBPvnkE0/M1Vdfrb/97W966qmntHnzZn344YdauHCh57phGDr77LN18OBBVVZW6uGHH9ZDDz2k4uJiT8y7776rs88+W2eccYZ27typpUuX6oc//KHcfabhPvnkk7rmmmt000036dVXX9VJJ50kl8ul5ubm0LwZQBDsa9g/ojjD6NKuTbu1+fEa7dq0W4bRNRrpAQCCgPETMDK9jdFlsfgOsFg0fsqUgO7VUlmpPevWqaWqSqZhBC9JAIgBUb2U76OPPlJKSoo2b96s+fPnq62tTUcddZQee+wxFRQUSJLefPNNZWRkaOvWrZo7d67+/ve/6xvf+IY+/PBDpfb8FeTee+/V9ddfr48++kgTJkzQ9ddfr2effVY1NTWe57rwwgv18ccf6/nnn5ckzZkzRzk5Obr77rslSV1dXZoxY4auvPJK3XDDDUPmzjR0RIJdm3Zr2RmPDBm36sWLdeLpMyXRKB3A2BNrn9nRPH6SYu/7gcjW4HZ3N0aXvBuj9xSrvrRkid5evXpY96QpOoCxIGaX8vXX1jNt9siexoQ7duzQoUOHlJ+f74k5/vjjlZ6erq1bt0qStm7dqtmzZ3sGVZLkcrnU3t6u2tpaT0zfe/TG9N7j4MGD2rFjh1dMXFyc8vPzPTH9HThwQO3t7V5fQLhl5qXLnpYg+flDoCySfUaiMvPSJdEoHQBiQTSNnyTGUAgvf43RbQ6HsktK9KXLLx98VpUPNEUHAG9RW5jq6urS0qVL9ZWvfEVZWVmSpMbGRk2YMEFT+k2pTU1NVWPPLhmNjY1eg6re673XBotpb2/XZ599ppaWFhmG4TOmsd9uHL1uu+02JSUleb5mzJgxshcOBJHVGqfCNT1/res/nuo5Lly9QFZrnAyjS6VL3JKvOZY950qXrvcs62O5HwBEnmgbP0mMoRB+TpdL+Vu2aN7atTrlzjs1b+1a5W/eLKfLJYvVqqzeJa2BFqd6Zl7VrFzpWdZnGoZaqqpY7gdgTBoX7gRGqqioSDU1NXrppZfCnUpAfvazn+maPrt6tLe3M7BCRMhdmKFlZQU+luclqnD1As/yvOE0St+/9zOW+wFABIq28ZPEGAqRobcxui+9s6pqVqxQ5yBFVi99mqIfamsb8FiW+wEYS6KyMHXFFVfomWee0ZYtW5SWluY573A4dPDgQX388cdef/VramqSw+HwxPTf/aV315m+Mf13omlqalJiYqImTpwoq9Uqq9XqM6b3Hv3Fx8crPj5+ZC8YGGW5CzM055xZqq2o176G/ZrqnKzMvHSv3fwCbZS+7a9v6a9rXhkws6p3ud+ysgKKUwAQBtE4fpIYQyE6OF0uOfLz1VpdrQPNzer417/0TknJkI9r3LhR7z70kHf/Kv1nuV92SQnFKQAxL6qW8pmmqSuuuEJ/+ctf9I9//EPHHHOM1/VTTz1V48eP1wsvvOA599Zbb6m+vl7z5s2TJM2bN0+vv/661+4vGzZsUGJiok444QRPTN979Mb03mPChAk69dRTvWK6urr0wgsveGKAaGO1xunE02fq/12UpRNPn+lVlJKkqc7JAd3nxbU1gy73K/mf5/Ti2tdZ3gcAIcL4CQiN3llV07/1LdlzcwN6zAdPPz2gKCXJ53I/AIhVUTVjqqioSI899pj++te/KiEhwdOPICkpSRMnTlRSUpIWL16sa665RkceeaQSExN15ZVXat68eZrbM/V2wYIFOuGEE3TxxRfr9ttvV2Njo2688UYVFRV5/hr3P//zP7r77rt13XXX6Qc/+IH+8Y9/6E9/+pOeffZZTy7XXHONLrnkEmVnZ+u0007T6tWr9cknn+iyyy4L/RsDhEBvo/SWPR2+C08WKdF+hNo/+tT/TUyp7aNP9ZvvPS0p8OV9htE16GwuAIB/jJ+A0EvOyZHN4VBnU5PvwpPFoglTp+rg3r3+b9JnuZ997lyZhuGZkRWfkqLknBxZrNbRexEAECIW0/T1f8rIZPHTUPDBBx/UpZdeKknq7OzUT37yEz3++OM6cOCAXC6X7rnnHq8p4u+9955+/OMfa9OmTZo0aZIuueQS/fKXv9S4cf+p023atElXX3213njjDaWlpWn58uWe5+h1991364477lBjY6NOPvlk3XXXXZozZ05Ar4WtjhGNenflk+RdnOr5T/OcJafpr6tfGfA4v3oeN9jyvsryOvpVAQiraP/MjqXxkxT93w+MHQ1ut7YXFXUf9P2Vq+e/yWMuvVTvPvjgkPc55c47FRcfTx8qAFFlOJ/XUVWYiiUMqhCtfBaKZnQ3Sp985EQtO+OR4d3Q0t1o/YF3rxwwC8pTCOv/f6kACloAECx8ZkcWvh+IJg1u98CCktOprOXLNT4pSVsXLRryHl9aulRvr1kzcOZVT4GLPlQAIhGFqSjAoArRzN/SOsPo0uKZd/lf7jeIVS9erBNPn+n1HItn3uV/J8A+BS1JLPUDMGr4zI4sfD8QbfwtwTMNQxvnzx90uZ/N4ZDZ1aUD/TYN6B+Tv3kzy/oARJThfF5HVY8pAJGht1G6r/OFa1zds5wsGlZxqv+uf7UV9f6LUuq+d8v77Xry1pe0/r5XWeoHAAAiUm9TdF/ns4qLu5f7WSw+l/ulX3CB3l692v/N+/Wh8pymHxWAKMKUAgBBlbswQ8vKCmSfnjCsx/Xf9a9/ocqfx27aPKCA1bKnQ6sKylRZXjesHAAAAELJ6XIpu6REttRUr/M2h0PZJSWafPTRAd3nQJ8dMxvcbm2cP19bFy3Sq1dfra2LFmnj/PlqcLuDmjsABAszpgAEXe7CDM05Z5ZqK+rVuqdD9y11q73lM9/BPUvyMvPSvU73L1QNi9l939Kl6zXnnFks6wMAABHL6XLJkZ/vc4ZTS1VVQPeIT0mR1Kfher+lgZ1NTdpeVOTVj4pZVQAiBYUpAKOi73K/+InjBt3Nr3D1ggHFo8y8dNnTEkbUr6r3eVreb1dtRb3PZYcAAACRwt9yv+ScHNkcjiH7UCXn5Mg0DNWsWOE7zjQli0U1K1fKkZ+vxo0b2eUPQMRgGgGAUedveZ89LdHvznq9/aokeQpYHr53Pvfpny+8q82P12jXpt0yjK5hZg4AABA+vX2oug/6DYB6jrOWL5fFalVrdbVXoWmAnn5Ub99zj7YXFQ2I7Z1V1XfJn2kYaqmq0p5169RSVSXTMILyugCgL3blCxN2lMFY5G83v8FUltepdInbu7n5jES5fniy1t60ZVjPT1N0ACPBZ3Zk4fuBsajB7R44w8npVNby5Z4ZTnvWrdOrV1895L3GJyXpUFub74t9dvljVhWAwzGcz2sKU2HCoAoInK+CliQtnnnX8Jb69fyh0d8sLQDwhc/syML3A2PVUD2hWqqqtHXRoqA815eWLtXba9YMXBbYM0urt1cVfaoA+DOcz2t6TAGIeH37VfVVuMbV3bvKosCKUzRFBwAAUcpfH6pegfSjGp+UpEMffzzkc7374IND9qoyu7pUe8stzKgCcNj4rQxA1PLXu2pQfZqiD8YwurRr0276UwEAgKgQSD+qYy69NKB7+V3qJ3l6Ve244oqA+lQBwFCYMQUgquUuzNCcc2Z5lvrVv/GRnrzlpSEft69hv99rPvta0Z8KAABEOKfLpeySEt+9oZYvlyM/X/VPPBGUWVU+9dv9z2K1stwPwJAoTAGIen2X+u3atDugwtRU52Sf5yvL67qXB/Ybq7Xs6dCqgjL6UwEAgIjmdLnkyM/3WwzKKi7W9qKi7llUfYtTfWZVvb169cgT6JlR1VpdrUNtbQE3UKeABYxdLOUDEFMy89JlT0vwNDofwNK9q19vA/W+DKNLpUvcvvtV9ZwrXbqeZX0AACCi9fajmv6tb8k+d65Xgad3VpUtNdXrMTaHQ9klJfrS5ZfL5nAMXA44TI0bN2p7UVFAy/0a3G5tnD9fWxct0qtXX62tixZp4/z5LAkExggKUwBiitUap8I1PX+B6z+e6jkuXL3AZ+Pz2op6r+V7AwTYn0qiRxUAAIhcTpdL+Vu2aN7atTrlzjs1b+1a5W/eLKfLFVCvqkDs+etf/TdQl7obqBuGGtzugAtYAGITS/kAxJzepugD+0QlqnD1Ar9L8QbrOzWcuMF6VPXthzXVOVmZeensDggAAEJusF3+ButVlblsmWpvvXXwPlVTp+rg3r3+n7xnuV/Ltm2qWbFiyB0AxyUk6GBLC0v8gBhFYQpATOrfFD2QIpC/vlPDiRu0R9V5ZUpInqiO1s8852mqDgAAItFgvaosVuugfarSzjlH7z744JDP0VpVNWCmlJeeAlbVxRd7TvnrUQUgevFnegAxq7cp+v+7KEsnnj5zyJlJh9OfSgqsR1XfopT0n6bqleV1Q7waAACA0PLXq2qoPlWO/PxRy8nXEj/TMNRSVaU969appapKpmGM2vMDCD5mTAFAj97+VKsKyrqLU30LTEP0p5IC6FHli9l979Kl6zXnnFk+720YXXp90269vuk9SdLs02dq9ulHswQQAACEzWAzqkzDkM3hGHS5n83hUPLcuXqnpGR4T9xniZ8jP1+NGzcGvPMfgMhEYQoA+hhpfyop8B5VA/Rpqn7i6TO9LlWW1+l3hc+oo7XTc+7JW15SQvJEXVl6NksAAQBA2PjrU9XbQH2w5X5Zy5fLPmfO4AUsf3qW+L19zz16e82aAY/tnVWVXVIyaHHKNAyfhbVBn3oEjwEwOApTANDPSPpTSYH3qPKnf2GrsrxOq84r8xnb0fqZVp1XpmV/LvBbnDKMLhqtAwCAsBisgXrW8uWegpHfAlYA3n3wwSEbpzvy830Wjhrc7mHPtBrJYwAMzWKaw/yvH0HR3t6upKQktbW1KTExMdzpAAgCw+jS4pl3qWVPh+8+U0NY9eLFnhlThtGlHxy9Rq17Bp+FZU9L1AO7rxxQcBpsZ0BmWQHDw2d2ZOH7AUSXQGYY+Sr4BMu8tWsHzOpqcLu7i2H9fxXumc3la6bVSB4DjGXD+bzmT+cAECS9Paok+W+g7ouPpuq1FfVDFqUkqeWD7iWAffXuDNi/35W/RuuG0aVdm3Zr8+M12rVptwyjaxjJAwAA+OevgXpfTpdL+Vu2aN7atTrlzjs195FHFJ+a6in6DLypReOnTAno+Q80N3sdm4ahmhUr/M+0klSzcqVXA/UhH2Oa2nXjjeo6eDCgnAB4ozAFAEHU26PKPj3B63xC8sTuf+k/vvLTVH04/ar6xgayM2Dp0vWe4lNleZ0Wz7xLy854RHd89y9adsYjWjzzLnYJBAAAIdW3gHVUbq5m33RTz4V+g6ee42MuvTSg+8anpHgdt1ZXDz4zq6d/VWt1deCPkXRw716tz8312i0QQGDoMQUAQeavR9W2v74VcFP14fSr6hs75M6AfRqt79/7WfcOhP2KWL0zq5aV/ad/Vd9+VUkpkySZamv+lN5VAABgVAzVo8qRn6/6J54Yeue/nByv0/1nUPnTNy7Qxxzaty+gpusAvFGYAoBRYLXGDdhhbzhN1TPz0pU8fXJAPab6LgEMdKZV654OPXzDC/5nVlm6Z1bNOWeWz4Kadw70rgIAAMHndLnkyM/326MqkJ3/+i8d7D+Dyp++cYE+ptdgTdfZ1Q8YiMIUAISQr4KVv7j/vutMv7vy9Spc470EMNCZVm0ffRLQzKonb31Jj928edBm7r5mWPXHDoEAAGAkepf4+RLozn99JefkyOZwDGum1ZCP6avPUkBfTdeHu6sfhSyMBRSmACBC5S7M0LI/F+h3hc+oo7XT61pi8kRdUXr2gEJQZl667GkJ/ncGtHTPsko6alJAOaxbs23oHQb7zbAKZIfA5OmTdWbhKZp2XDKFKgAAMGJDzarqz2K1DnumlddjAtR/+Z+/Xf06m5r8Lv8bSSELiEYW0xyq5IvRwFbHAAJlGF16fdNuvb7pPUnS7NNnavbpR/st5PTuyifJu6jU0zt0WVmBJh85UcvOeCToua568WKvGWGeXIb4pBlqOSAzrhBOfGZHFr4fAILBZ9HH6fQ706r3MbtuvFEH9+4d8v7z1q71zJgyDUMb58/330C9Z5ZW/ubNnoKYv0JWb/FssD5WzLJCJBjO5zWFqTBhUAVgNPmapWSf8Z9G64bRpcUz7xp0ZlXC1Inq2PvZsJ732se+rf93UZYk/ec5Blsy2Of5JPlcDujztdDXCiHEZ3Zk4fsBIFhGUsDpOnhQ63NzdWjfPt8BPopMLVVV2rpo0ZD59BazRlLI6sUsK0SK4Xxe8+dmAIhBuQsz9MDuq7TqxYt17WPf1qoXL9YD717pKeRYrXEqXNMzOOm3C3Pv8beW5Gi4hrVDYF89xbHSpetlGF2e070zrvrfp7evVWV5nc/bGUaXdm3arc2P12jXpt1e9wQAAJD+079q+re+JfvcuQHNKoqbMEEn3Xpr98wlS79BlJ+lgMPdCbC1utp/UUry6mPVV+8sq/6P7V0u2OB2+7+lYailqkp71q1TS1WVTMMIKGcgGOgxBQAxaqhG67kLM7SsrMDHbKTumVVzzpkl932v+Z9V1Zdl5DsEevQ0XK+tqNeJp8+UYXSpdIk7oJ0D+y7rG+kMK5YLAgCAQAy36fpwdwIcbiFL6i4s1axY4bs5u2lKFovf3QJpyo5wozAFAGNY7sIMzTlnlt+CTOEaV3ePKIv8F6d6/lhYuHpkOwT211vQGnLGVb9CluS/p9VQOweyXBAAAAzHcJquD3cnwOEWsqThzbLqu1tgqJqyU8jCYPhTMACMcb0zq/7fRVk68fSZXsWl3llV9ukJfh9vT0v0WfDp3SFwwFLBIfQWtAKdcdUbN+QMKw1cKiixXBAAAIxMoEsBe3f16z4YevlfbyFrQGyfx9icTk8hSxqlWVaSalau9FrWN5Llgg1utzbOn6+tixbp1auv1tZFi7Rx/vxBlxZibGHGFABgUP1nVSWlTJJkqq3500GXvPX2sRpyxlWvfssBA51x1Rs3khlWoV4u2PucLBkEAGBsGc7yv95C1vaiou7iVN/CkZ8+VqGYZTWS5YIjmZHluSWzrMYMClMAgCEN1a/KH399rAbwsRywd8bVYDsH9i1kDXeGlRTa5YK9jx1uQYtCFgAAsWE4y/+G28dquMsFpdFtyj7SQlavke4uSDErOlGYAgCMqv4zrva8s1fu+15Vq4+G632LM4POuPJRyBruDCtpFJYL+plhJY2soBXq3lcHD36u5+7ZrsZ/75Pji1N11uXZmjCBoQIAAMHSu/wvEMMpZIViltVoF7J6jXSWVaDFrOEUryh0hQajTQDAqOs/4+qCn381oFlAQ+0c2Lc4M9wZVlJolgtKIytoHc7MrJH4w3Ub9fRvq9Rl/OcJ//DTjTr3mrn6we35QXseAAAQuOEWskZzllUk7y4YaDFrODOxhjtra6RFLIpfFKYAAGEwnKWBQ+0c2Peew5lhJYVmuaA0/ILW4czMGok/XLdR5XdsHXC+yzA954cqTo1kyeFgjxnO/VjuCABAt9GcZRWpuwsGWswyDUM7rroqoJlYw521NdKlhyN9nD/DLXJFSlGMwhQAIOIFWsgazgyr3vuO9nJBafgFrZHOzBqJgwc/19O/rRo05unfVul7t5zud1nfSJYcDvYYSQHfL9TLHQEAiHSjNctqtAtZ0shmWQVazHr9ppsCmoklaViztg5n6eFIG8P7MtwiV7CLYoeDPycCAGJK7sIMPbD7Kq168WJd+9i3terFi/XAu1f6LVL0FrPs0xO8ztvTEgcsl+udYSU/uzfLItlneC8XlIZf0BrpzKyReO6e7V7L93zpMkw9d892n9d6lxz2L6T1LjmsLK8b3mPOK9Oq8wK730ieGwAAeHO6XMrfskXz1q7VKXfeqXlr1yp/82afxYneQpYtNdXrvM3hGFBI6S1kdR/0GzwFcXfBQItZB/fu9X+xz0ys4czaGnK2luSZreV1aYSP86e3yNU/794iV4PbfVjxo40ZUwCAmDPcXQRHc7mgNPwlgyOdmTUSjf/eN+K4kSw5HPIx/vS7n6SQLncEACCWjVZT9lDsLhhoMSsQgRa5emNH2uB9pI/zGTrMvlyHs1viaKEwBQCARm+5YO+9h1PQGkkj95FyfHHqiONGsuRwyMcMps/9JIVsuSMAAPAWSbsLBlLMGj91qg4NNmOqx3CKXPEpKSNaeujrONDH+TLcIlcwi2LBwp8QAQAYpuEuF+x9TKBLBnsLWZIGLhscZGbWSJx1ebbirP7WJnaLs1p01uXZA86PZMlhMJYf7mvYH9LljgAA4PD0FrKmf+tbss+dO+hMnOEsF+y991BLBk9csUI2h2Pg9T5xNqdTyTk5nkJXILEjWXro6zjQx/ky3CJXMItiwcKMKQAARmC4ywWlwJcM9sYOd2bWSEyYME7nXjPX5658vc69Zq7PxucjWXIYjOWHw7lHMJ4PAACE1nBmWfXGD7Vk0BIXF/BMrEBnbY1k6aE0siWL/gy3yBXMoliwUJgCACCEhlPQGk4h63D84PbuHWie/m2VVyP0OKtF514z13O9v5EsORzyMYPpd79QLXcEAAChN5zlgtLQxazh9LsKNHYkSw8P53G+DLfIFcyiWLBYTNNXJhht7e3tSkpKUltbmxITE8OdDgAAOnjwcz13z3Y1/nufHF+cqrMuz/Y5U6qv3p3xJPnsndV/meKQjzF9/Luf+43kuUeCz+zIwvcDAHA4TMMIeCZWoLENbvfAIpbT6bPBezAe5+s+24uKepIeWOTqvwRyuPEjMZzPawpTYcKgCgAQKyrL6wYuOZwx+JLDwR4jKeD7jeS5h4vP7MjC9wMAEImGU/AKxuP6G26RK1hFMX8oTEUBBlUAgFhiGF3DXnI42GOGc7+RPPdw8JkdWfh+AADg23CLXMEqivlCYSoKMKgCACA68JkdWfh+AAAQ+YbzeR3c7qkAAAAAAABAgChMAQAAAAAAICwoTAEAAAAAACAsKEwBAAAAAAAgLChMAQAAAAAAICwoTAEAAAAAACAsKEwBAAAAAAAgLChMAQAAAAAAICwoTAEAAAAAACAsKEwBAAAAAAAgLChMAQAAAAAAICzGhTuBsco0TUlSe3t7mDMBAACD6f2s7v3sRngxhgIAIPINZ/xEYSpMOjo6JEkzZswIcyYAACAQHR0dSkpKCncaYx5jKAAAokcg4yeLyZ//wqKrq0sffvihEhISZLFYwp1O1Ghvb9eMGTP0/vvvKzExMdzpjBm876HHex56vOfhEQ3vu2ma6ujo0LRp0xQXRxeEcGMMNTLR8N9arOE9Dz3e89DjPQ+9aHnPhzN+YsZUmMTFxSktLS3caUStxMTEiP6PMFbxvoce73no8Z6HR6S/78yUihyMoQ5PpP+3Fot4z0OP9zz0eM9DLxre80DHT/zZDwAAAAAAAGFBYQoAAAAAAABhQWEKUSU+Pl433XST4uPjw53KmML7Hnq856HHex4evO9AaPDfWujxnoce73no8Z6HXiy+5zQ/BwAAAAAAQFgwYwoAAAAAAABhQWEKAAAAAAAAYUFhCgAAAAAAAGFBYQoAAAAAAABhQWEKYVdSUqKZM2fKZrNpzpw5euWVVwaNf+qpp3T88cfLZrNp9uzZeu6557yul5eXa8GCBUpOTpbFYtHOnTtHMfvoFMz3/NChQ7r++us1e/ZsTZo0SdOmTdP3v/99ffjhh6P9MqJOsH/Wb775Zh1//PGaNGmSpk6dqvz8fG3btm00X0LUCfZ73tf//M//yGKxaPXq1UHOOroF+z2/9NJLZbFYvL7OPPPM0XwJQFRg/BR6jJ9Cj7FT6DF2Cj3GTpJMIIyeeOIJc8KECeYf/vAHs7a21vzRj35kTpkyxWxqavIZ//LLL5tWq9W8/fbbzTfeeMO88cYbzfHjx5uvv/66J+aPf/yj+Ytf/MK87777TEnma6+9FqJXEx2C/Z5//PHHZn5+vvnkk0+ab775prl161bztNNOM0899dRQvqyINxo/62vXrjU3bNhg/vvf/zZramrMxYsXm4mJiWZzc3OoXlZEG433vFd5ebl50kknmdOmTTPvvPPOUX4l0WM03vNLLrnEPPPMM82GhgbP1969e0P1koCIxPgp9Bg/hR5jp9Bj7BR6jJ26UZhCWJ122mlmUVGR59gwDHPatGnmbbfd5jP+/PPPN88++2yvc3PmzDH/+7//e0Dsu+++y8DKh9F8z3u98sorpiTzvffeC07SMSAU73tbW5spydy4cWNwko5yo/Wef/DBB+b06dPNmpoa8+ijj2Zw1cdovOeXXHKJec4554xKvkC0YvwUeoyfQo+xU+gxdgo9xk7dWMqHsDl48KB27Nih/Px8z7m4uDjl5+dr69atPh+zdetWr3hJcrlcfuPhLVTveVtbmywWi6ZMmRKUvKNdKN73gwcPqrS0VElJSTrppJOCl3yUGq33vKurSxdffLGuvfZaZWZmjk7yUWo0f843bdqklJQUzZo1Sz/+8Y/V2toa/BcARAnGT6HH+Cn0GDuFHmOn0GPs9B8UphA2LS0tMgxDqampXudTU1PV2Njo8zGNjY3Dioe3ULznnZ2duv7663XRRRcpMTExOIlHudF835955hlNnjxZNptNd955pzZs2CC73R7cFxCFRus9/9WvfqVx48bpqquuCn7SUW603vMzzzxTf/zjH/XCCy/oV7/6lTZv3qyvf/3rMgwj+C8CiAKMn0KP8VPoMXYKPcZOocfY6T/GhTsBALHj0KFDOv/882Wapn7/+9+HO50x4YwzztDOnTvV0tKi++67T+eff762bdumlJSUcKcWc3bs2KE1a9bo1VdflcViCXc6Y8aFF17o+ffZs2frxBNP1Be/+EVt2rRJX/va18KYGQAEB+On0GLsFDqMncIjGsdOzJhC2NjtdlmtVjU1NXmdb2pqksPh8PkYh8MxrHh4G833vHdQ9d5772nDhg38ta+P0XzfJ02apGOPPVZz587VAw88oHHjxumBBx4I7guIQqPxnldUVKi5uVnp6ekaN26cxo0bp/fee08/+clPNHPmzFF5HdEkVP9P/8IXviC73a5//etfh580EIUYP4Ue46fQY+wUeoydQo+x039QmELYTJgwQaeeeqpeeOEFz7muri698MILmjdvns/HzJs3zytekjZs2OA3Ht5G6z3vHVS988472rhxo5KTk0fnBUSpUP6sd3V16cCBA4efdJQbjff84osv1q5du7Rz507P17Rp03TttdfK7XaP3ouJEqH6Of/ggw/U2toq5/9v7+5Zo1jDMAA/yrqBuOIHCImIaaKNiB9IIBDQQiwsAoKli1goWNmk0Cp/wC4/QLEKWgVMJ1hJQghko6KVmE4RrDR+FT4WssHogcM5u2fe3eN1wRTLTPG8bzHc3DvMDA93Z3DoM/JT9eSn6slO1ZOdqic7/aT029f5s83OzubAwEDeuXMnnz9/nlevXs1du3blmzdvMjOz2WzmjRs3Nq5//Phx1mq1vHXrVr548SKnp6d/+zzmu3fvcmVlJefn5zMicnZ2NldWVvL169eVr68XdXvPv379mpOTk7l///5stVqbPkv65cuXImvsRd3e9w8fPuTNmzdzYWEh19bWcnl5OS9fvpwDAwP57NmzImvsNf/F/eVXviyzWbf3/P379zk1NZULCwv56tWrfPjwYZ44cSIPHjyYnz9/LrJG6AXyU/Xkp+rJTtWTnaonO/2gmKK4mZmZPHDgQNbr9RwbG8vFxcWNc6dOncpLly5tuv7evXt56NChrNfrefjw4Zyfn990/vbt2xkRvx3T09MVrKY/dHPP25+V/qvj0aNHFa2oP3Rz3z99+pTnz5/Pffv2Zb1ez+Hh4ZycnMylpaWqltMXun1/+ZVw9btu7vnHjx/z7NmzuXfv3ty2bVuOjIzklStXNsIa/Mnkp+rJT9WTnaonO1VPdsrckplZ3fNZAAAAAPCDd0wBAAAAUIRiCgAAAIAiFFMAAAAAFKGYAgAAAKAIxRQAAAAARSimAAAAAChCMQUAAABAEYopAAAAAIpQTAEAAABQhGIKoAtmZmZiZGQkarVaTE1NlR4HAKAvyFDAlszM0kMA9LPV1dU4efJkzM3NxfHjx2Pnzp0xODhYeiwAgJ4mQwEREbXSAwD0uwcPHsTY2FicO3eu9CgAAH1DhgIiPDEF0JHR0dF4+fLlxu9msxl3794tOBEAQO+ToYA2xRRAB96+fRvj4+Nx7dq1uHjxYjQajWg0GqXHAgDoaTIU0Obl5wAdaDQasba2FhMTEzE0NBTNZjN2794dFy5cKD0aAEDPkqGANsUUQAeePHkSERFHjhyJiIjr1697DB0A4G/IUECbYgqgA61WK0ZHR2P79u0REXH69OnYsWNH4akAAHqbDAW0KaYAOtBqteLo0aOlxwAA6CsyFNCmmALoQKvVimPHjpUeAwCgr8hQQJtiCuBf+vbtWzx9+tS/fQAA/4AMBfysVnoAgH61devWWF9fLz0GAEBfkaGAn23JzCw9BMD/xZkzZ2J1dTXW19djz549cf/+/RgfHy89FgBAT5Oh4M+lmAIAAACgCO+YAgAAAKAIxRQAAAAARSimAAAAAChCMQUAAABAEYopAAAAAIpQTAEAAABQhGIKAAAAgCIUUwAAAAAUoZgCAAAAoAjFFAAAAABFKKYAAAAAKEIxBQAAAEAR3wGN20o7aMX08wAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "![image.png](attachment:image.png)"
+    "plot_pairs_2d(\n",
+    "    (\"NSGA-II (original)\", res_nsga2.F),\n",
+    "    (\"NSGA-II (pruning)\", res_nsga2_p.F),\n",
+    "    figsize=[12, 5],\n",
+    "    dpi=100,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -143,15 +156,28 @@
    ]
   },
   {
-   "attachments": {
-    "image.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "![image.png](attachment:image.png)"
+    "plot_pairs_3d(\n",
+    "    (\"NSGA-II (original)\", res_nsga2.F),\n",
+    "    (\"NSGA-II (mnn)\", res_nsga2_mnn.F),\n",
+    "    figsize=[12, 5],\n",
+    "    dpi=100,\n",
+    ")"
    ]
   },
   {
@@ -187,54 +213,60 @@
    "metadata": {},
    "source": [
     "```python\n",
-    "fig, ax = plt.subplots(1, 2, figsize=[12, 5], dpi=100)\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "ax[0].scatter(\n",
-    "    res_nsga2.F[:, 0], res_nsga2.F[:, 1],\n",
-    "    color=\"indigo\", label=\"NSGA-II (original)\", marker=\"o\",\n",
-    ")\n",
-    "ax[0].set_ylabel(\"$f_2$\")\n",
-    "ax[0].set_xlabel(\"$f_1$\")\n",
-    "ax[0].legend()\n",
     "\n",
-    "ax[1].scatter(\n",
-    "    res_nsga2_p.F[:, 0], res_nsga2_p.F[:, 1],\n",
-    "    color=\"firebrick\", label=\"NSGA-II (pcd)\", marker=\"o\",\n",
-    ")\n",
-    "ax[1].set_ylabel(\"$f_2$\")\n",
-    "ax[1].set_xlabel(\"$f_1$\")\n",
-    "ax[1].legend()\n",
+    "def plot_pairs_3d(first, second, colors=(\"indigo\", \"firebrick\"), **kwargs):\n",
+    "    \n",
+    "    fig, ax = plt.subplots(1, 2, subplot_kw={'projection':'3d'}, **kwargs)\n",
     "\n",
-    "fig.tight_layout()\n",
-    "plt.show()\n",
-    "```\n",
+    "    ax[0].scatter(\n",
+    "        *first[1].T,\n",
+    "        color=colors[0], label=first[0], marker=\"o\",\n",
+    "    )\n",
+    "    ax[0].set_ylabel(\"$f_2$\")\n",
+    "    ax[0].set_xlabel(\"$f_1$\")\n",
+    "    ax[0].set_zlabel(\"$f_3$\")\n",
+    "    ax[0].legend()\n",
     "\n",
-    "```python\n",
-    "fig, ax = plt.subplots(1, 2, figsize=[12, 5], dpi=100, subplot_kw={'projection':'3d'})\n",
+    "    ax[1].scatter(\n",
+    "        *second[1].T,\n",
+    "        color=colors[1], label=second[0], marker=\"o\",\n",
+    "    )\n",
+    "    ax[1].set_ylabel(\"$f_2$\")\n",
+    "    ax[1].set_xlabel(\"$f_1$\")\n",
+    "    ax[1].set_zlabel(\"$f_3$\")\n",
+    "    ax[1].legend()\n",
     "\n",
-    "ax[0].scatter(\n",
-    "    res_nsga2.F[:, 0], res_nsga2.F[:, 1], res_nsga2.F[:, 2],\n",
-    "    color=\"indigo\", label=\"NSGA-II (original)\", marker=\"o\",\n",
-    ")\n",
-    "ax[0].set_ylabel(\"$f_2$\")\n",
-    "ax[0].set_xlabel(\"$f_1$\")\n",
-    "ax[0].set_zlabel(\"$f_3$\")\n",
-    "ax[0].legend()\n",
+    "    ax[0].view_init(elev=30, azim=30)\n",
+    "    ax[1].view_init(elev=30, azim=30)\n",
     "\n",
-    "ax[1].scatter(\n",
-    "    res_nsga2_mnn.F[:, 0], res_nsga2_mnn.F[:, 1], res_nsga2_mnn.F[:, 2],\n",
-    "    color=\"firebrick\", label=\"NSGA-II (mnn)\", marker=\"o\",\n",
-    ")\n",
-    "ax[1].set_ylabel(\"$f_2$\")\n",
-    "ax[1].set_xlabel(\"$f_1$\")\n",
-    "ax[1].set_zlabel(\"$f_3$\")\n",
-    "ax[1].legend()\n",
+    "    fig.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "def plot_pairs_2d(first, second, colors=(\"indigo\", \"firebrick\"), **kwargs):\n",
+    "    \n",
+    "    fig, ax = plt.subplots(1, 2, **kwargs)\n",
+    "\n",
+    "    ax[0].scatter(\n",
+    "        *first[1].T,\n",
+    "        color=colors[0], label=first[0], marker=\"o\",\n",
+    "    )\n",
+    "    ax[0].set_ylabel(\"$f_2$\")\n",
+    "    ax[0].set_xlabel(\"$f_1$\")\n",
+    "    ax[0].legend()\n",
     "\n",
-    "ax[0].view_init(elev=30, azim=30)\n",
-    "ax[1].view_init(elev=30, azim=30)\n",
+    "    ax[1].scatter(\n",
+    "        *second[1].T,\n",
+    "        color=colors[1], label=second[0], marker=\"o\",\n",
+    "    )\n",
+    "    ax[1].set_ylabel(\"$f_2$\")\n",
+    "    ax[1].set_xlabel(\"$f_1$\")\n",
+    "    ax[1].legend()\n",
     "\n",
-    "fig.tight_layout()\n",
-    "plt.show()\n",
+    "    fig.tight_layout()\n",
+    "    plt.show()\n",
     "```"
    ]
   },
@@ -262,7 +294,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7 (tags/v3.9.7:1016ef3, Aug 30 2021, 20:19:38) [MSC v.1929 64 bit (AMD64)]"
+   "version": "3.9.7"
   },
   "orig_nbformat": 4,
   "vscode": {