From 65871dc8b2866d080c576ee3a10c332f64aaf57e Mon Sep 17 00:00:00 2001
From: Anders Pride <apride@hawk.illinoistech.edu>
Date: Wed, 22 Apr 2026 01:18:18 -0500
Subject: [PATCH 01/29] Created kronecker folder and search method

---
 .../kronecker/__init__.py                     |  1 +
 .../{ => kronecker}/kronecker.py              |  4 +-
 .../kronecker/kronecker_search_methods.py     | 88 +++++++++++++++++++
 3 files changed, 91 insertions(+), 2 deletions(-)
 create mode 100644 qmcpy/discrete_distribution/kronecker/__init__.py
 rename qmcpy/discrete_distribution/{ => kronecker}/kronecker.py (99%)
 create mode 100644 qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py

diff --git a/qmcpy/discrete_distribution/kronecker/__init__.py b/qmcpy/discrete_distribution/kronecker/__init__.py
new file mode 100644
index 000000000..6d0653bb3
--- /dev/null
+++ b/qmcpy/discrete_distribution/kronecker/__init__.py
@@ -0,0 +1 @@
+from .kronecker import Kronecker
\ No newline at end of file
diff --git a/qmcpy/discrete_distribution/kronecker.py b/qmcpy/discrete_distribution/kronecker/kronecker.py
similarity index 99%
rename from qmcpy/discrete_distribution/kronecker.py
rename to qmcpy/discrete_distribution/kronecker/kronecker.py
index 1b9c2de5b..98e7653a2 100644
--- a/qmcpy/discrete_distribution/kronecker.py
+++ b/qmcpy/discrete_distribution/kronecker/kronecker.py
@@ -1,5 +1,5 @@
-from .abstract_discrete_distribution import AbstractLDDiscreteDistribution
-from ..util import ParameterError
+from ..abstract_discrete_distribution import AbstractLDDiscreteDistribution
+from ...util import ParameterError
 import numpy as np
 import warnings
 
diff --git a/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
new file mode 100644
index 000000000..3fdb298ec
--- /dev/null
+++ b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
@@ -0,0 +1,88 @@
+import numpy as np
+from sympy import gcdex, primerange, prime
+#https://github.com/sympy/sympy/releases
+
+# I can't find where Jimmy's code for the kronecker search from SURE 2025 is, so I've temporarily put my method here
+# Currently, this produces results that are very similar but not identical to my matlab code, which is a bit concerning.
+# The wssd of the two methods are typically the same to 2-3 decimal places, depending on N and d
+
+def KTildeEx(t):
+    b = t * (t - 1) + 1/6
+    step = 1 + b * (1 / (np.arange(1, len(t) + 1) ** 2))
+    k = np.prod(step)
+    return k
+
+def kronecker_search_march_2026(N, dMax, searchsize):
+    if searchsize < 2:
+        raise ValueError("searchsize must be at least 2.")
+    if N < 2:
+        raise ValueError("N must be at least 2.")
+    if dMax < 1:
+        raise ValueError("dMax must be at least 1.")
+    
+    # search over the first n primes, n = searchsize
+    searchspace = np.array(list(primerange(1, prime(searchsize)+1)))
+
+    alpha = np.zeros(dMax)
+    # we pick the golden ratio as the first alpha
+    alpha[0] = (np.sqrt(5) - 1) / 2
+    alpha[0] = (np.sqrt(5) - 1) / 2
+    
+    diff = np.cumsum(1.0 / np.arange(N, 1, -1))
+    freq = np.cumsum(diff)
+    freq = np.flip(freq)
+    
+    # Compute Bezout coefficients for all pairs in the search space
+    bezoutCoeffs = np.zeros((searchsize, searchsize))
+    for i in range(searchsize - 1):
+        a = searchspace[i]
+        for j in range(i + 1, searchsize):
+            c = searchspace[j]
+            # Use sympy.gcdex to get Bezout coefficients
+            d_coeff, b_coeff, _ = gcdex(int(a), int(c))
+            bezoutCoeffs[i, j] = b_coeff
+            bezoutCoeffs[j, i] = d_coeff
+    bezoutCoeffs = np.abs(bezoutCoeffs)
+    
+    # setting up some useful variables for the search
+    coeff = np.zeros((dMax - 1, 4))
+    num = N * (N + 1) / 2
+    t = np.mod(alpha[0] * np.arange(1,N), 1)
+    kPrev = 1 + (t * (t - 1) + 1/6)
+    
+    # the main search loop
+    for dim in range(1, dMax):
+        best = np.array([0, 0, 0, 0, np.inf])
+        nK0 = N * KTildeEx(np.zeros(dim+1))
+        for i in range(searchsize):
+            p1 = searchspace[i]
+            for j in range(searchsize):
+                if j == i:
+                    continue
+                p2 = searchspace[j]
+                b = bezoutCoeffs[i, j]
+                d = bezoutCoeffs[j, i]
+                alpha_dim = (p1 * alpha[dim - 1] + b) / (p2 * alpha[dim - 1] + d)
+                t = (alpha_dim * np.arange(1, N)) - np.floor(alpha_dim * np.arange(1, N))
+                k_vector = kPrev * (1 + (t * (t - 1) + 1/6) / ((dim+1) ** 2))
+                
+                wssd = nK0 - num + 2 * np.dot(freq, k_vector)
+                
+                if wssd < best[4]:
+                    best[0] = p1
+                    best[1] = b
+                    best[2] = p2
+                    best[3] = d
+                    best[4] = wssd
+        
+        alpha_d = (best[0] * alpha[dim - 1] + best[1]) / (best[2] * alpha[dim - 1] + best[3])
+        alpha[dim] = np.mod(alpha_d, 1)
+        t = np.mod(alpha[dim] * np.arange(1, N), 1)
+        kPrev = kPrev * (1 + (t * (t - 1) + 1/6) / ((dim+1) ** 2))
+        coeff[dim - 1, :] = [best[0], best[1], best[2], best[3]]
+        #print(coeff[dim - 1, :], best[4]) #debugging line to check the coefficients and wssd at each dimension
+    
+    return alpha
+
+# quick and dirty test
+# print(kronecker_search_march_2026(10000, 20, 50))
\ No newline at end of file

From 4350e46f2105f8c3ea079828a6d2e639164bc8f9 Mon Sep 17 00:00:00 2001
From: Anders Pride <apride@hawk.illinoistech.edu>
Date: Mon, 18 May 2026 09:50:52 -0500
Subject: [PATCH 02/29] Clarified kronecker_search_methods.py

---
 .../kronecker/kronecker_search_methods.py     | 164 +++++++++++++-----
 1 file changed, 116 insertions(+), 48 deletions(-)

diff --git a/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
index 3fdb298ec..45bd39d35 100644
--- a/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
+++ b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
@@ -1,38 +1,72 @@
 import numpy as np
 from sympy import gcdex, primerange, prime
+np.set_printoptions(precision=17)
 #https://github.com/sympy/sympy/releases
 
 # I can't find where Jimmy's code for the kronecker search from SURE 2025 is, so I've temporarily put my method here
-# Currently, this produces results that are very similar but not identical to my matlab code, which is a bit concerning.
-# The wssd of the two methods are typically the same to 2-3 decimal places, depending on N and d
 
-def KTildeEx(t):
-    b = t * (t - 1) + 1/6
-    step = 1 + b * (1 / (np.arange(1, len(t) + 1) ** 2))
-    k = np.prod(step)
-    return k
+def kronecker_search_march_2026(N, dMax, searchsize, coord_weights=None, alpha_0=None, return_coeffs=False):
+    """
+    Args:
+        N (int): The maximum sample size to be searched over.
+        dMax (int): The maximum dimension for which to find the generating vector.
+        searchsize (int): The number of primes to search over for each component of the generating vector.
+        coord_weights (array-like, optional): An array of coordinate weights to use in the search. If None, weights are set to j^(-2).
+        alpha_0 (float, optional): The value for the first component of the generating vector. If None, the golden ratio is used. Note that alpha_0 is taken mod 1.
+        return_coeffs (bool, optional): Whether to return the coefficients of the linear transformation. Default is False.
+    Returns:
+        alpha, coeff (tuple):
+        - alpha (numpy array): The generating vector found by the search.
+        - coeff (numpy array, optional): The coefficients of the linear transformation used in the search, returned only if return_coeffs is True. A description of the coeff array is found below.
+    Complexity:
+        The time complexity of the search is O(searchsize^2 * dMax * N).
+    Approach:
+        Uses the quadratic Bernoulli polynomial kernel to conduct a CBC search for a generating vector, minimizing the weighted sum of squared discrepancies (wssd) with weights w_n = n.
+    Details on coeff array:
+        The coeff array, if returned, is a (dMax-1) x 4 array where each row corresponds to a dimension from 2 to dMax. The columns correspond to the coefficients of the linear transformation used to compute the alpha component for that dimension. Specifically,
+        - alpha[dim+1] = (coeff[dim, 0] * alpha[dim] + coeff[dim, 1]) / (coeff[dim, 2] * alpha[dim] + coeff[dim, 3])
+    """
 
-def kronecker_search_march_2026(N, dMax, searchsize):
     if searchsize < 2:
         raise ValueError("searchsize must be at least 2.")
     if N < 2:
         raise ValueError("N must be at least 2.")
     if dMax < 1:
         raise ValueError("dMax must be at least 1.")
+    if coord_weights is not None and len(coord_weights) < dMax:
+        raise ValueError("Length of coord_weights must be greater than or equal to dMax.")
     
-    # search over the first n primes, n = searchsize
-    searchspace = np.array(list(primerange(1, prime(searchsize)+1)))
 
-    alpha = np.zeros(dMax)
-    # we pick the golden ratio as the first alpha
-    alpha[0] = (np.sqrt(5) - 1) / 2
-    alpha[0] = (np.sqrt(5) - 1) / 2
+    # the quadratic Bernoulli polynomial
+    bernoulli2 = lambda t: t * (t - 1) + 1/6
     
-    diff = np.cumsum(1.0 / np.arange(N, 1, -1))
+    # define coordinate weights if not provided, default to j^(-2)
+    if coord_weights is None:
+        coord_weights = np.array([j**(-2) for j in range(1, dMax + 1)], dtype=np.float64)
+
+    # search over the first n primes, n = searchsize
+    searchspace = np.array(list(primerange(1, prime(searchsize)+1)), dtype=np.float64)
+
+    # alpha is our generating vector, will be found cbc
+    alpha = np.zeros(dMax, dtype=np.float64)
+
+    # we pick the golden ratio as the first component of alpha, or let the user specify
+    if alpha_0 is None:
+        alpha[0] = np.float64((np.sqrt(5) - 1) / 2)
+    else:
+        alpha[0] = np.mod(alpha_0, 1,dtype=np.float64)
+
+    # precompute several constants for the wssd calculation
+    diff = np.cumsum(1.0 / np.arange(N, 1, -1,dtype=np.float64))
     freq = np.cumsum(diff)
     freq = np.flip(freq)
-    
-    # Compute Bezout coefficients for all pairs in the search space
+
+    num = N * (N + 1) / 2
+
+    nK0 = (1 + coord_weights/6)
+    nK0 = N * np.cumprod(nK0)
+
+    # precompute Bezout coefficients for all pairs of primes in the search space
     bezoutCoeffs = np.zeros((searchsize, searchsize))
     for i in range(searchsize - 1):
         a = searchspace[i]
@@ -40,49 +74,83 @@ def kronecker_search_march_2026(N, dMax, searchsize):
             c = searchspace[j]
             # Use sympy.gcdex to get Bezout coefficients
             d_coeff, b_coeff, _ = gcdex(int(a), int(c))
-            bezoutCoeffs[i, j] = b_coeff
-            bezoutCoeffs[j, i] = d_coeff
-    bezoutCoeffs = np.abs(bezoutCoeffs)
-    
+            bezoutCoeffs[i, j] = np.float64(b_coeff)
+            bezoutCoeffs[j, i] = np.float64(d_coeff)
+
+
     # setting up some useful variables for the search
-    coeff = np.zeros((dMax - 1, 4))
-    num = N * (N + 1) / 2
-    t = np.mod(alpha[0] * np.arange(1,N), 1)
-    kPrev = 1 + (t * (t - 1) + 1/6)
+    coeff = np.zeros((dMax - 1, 4)) # stores the coefficients of the linear transformation at each dimension
+    t = alpha[0] * np.arange(1, N) % 1 # t vector is the vector of coordinates generated for the first dimension
+    kPrev = 1 + coord_weights[0] * bernoulli2(t) # gets the k vector for the first dimension, which is used in the wssd calculation and updated each dimension of the search.
+    # The k vector is Ktilde(x_i) for i = 1,...,N-1, where Ktilde is the kernel and x_i are the points generated by the alpha vector, up to the current dimension.
     
     # the main search loop
     for dim in range(1, dMax):
-        best = np.array([0, 0, 0, 0, np.inf])
-        nK0 = N * KTildeEx(np.zeros(dim+1))
+        best_wssd = np.inf # stores the current wssd found for each dimension, initialized to infinity
+        best_alpha = 0 # stores the current best alpha component found for this dimension, initialized to 0
+        best_k = None # stores the k vector for the current best alpha, used to update the k vector for the next dimension after the search is done for this dimension
         for i in range(searchsize):
-            p1 = searchspace[i]
+            p1 = searchspace[i] 
             for j in range(searchsize):
-                if j == i:
+                if j == i: # the two primes have to be distinct, so we skip this case
                     continue
+
                 p2 = searchspace[j]
+
                 b = bezoutCoeffs[i, j]
                 d = bezoutCoeffs[j, i]
-                alpha_dim = (p1 * alpha[dim - 1] + b) / (p2 * alpha[dim - 1] + d)
-                t = (alpha_dim * np.arange(1, N)) - np.floor(alpha_dim * np.arange(1, N))
-                k_vector = kPrev * (1 + (t * (t - 1) + 1/6) / ((dim+1) ** 2))
+
+                if b < 0: # we search over both minimal Bezout coefficients
+                    b1 = -b
+                    d1 = d
+                    b2 = np.abs(b + p1)
+                    d2 = np.abs(d -p2)
+                else:
+                    d1 = -d
+                    b1 = b
+                    d2 = np.abs(d + p2)
+                    b2 = np.abs(b - p1)
                 
-                wssd = nK0 - num + 2 * np.dot(freq, k_vector)
+                alpha_dim1 = (p1 * alpha[dim - 1] + b1) / (p2 * alpha[dim - 1] + d1) # the linear transformation to get the next alpha_dim candidate to test
+                alpha_dim2 = (p1 * alpha[dim - 1] + b2) / (p2 * alpha[dim - 1] + d2) # the other candidate from the linear transformation
+                t1 = (alpha_dim1 * np.arange(1, N)) - np.floor(alpha_dim1 * np.arange(1, N)) # vector of coordinates generated by this candidate component
+                t2 = (alpha_dim2 * np.arange(1, N)) - np.floor(alpha_dim2 * np.arange(1, N)) 
+                k_vector1 = kPrev * (1 + bernoulli2(t1) * coord_weights[dim]) # get the k vector for this candidate component, used in the wssd calculation
+                k_vector2 = kPrev * (1 + bernoulli2(t2) * coord_weights[dim]) 
+
+                wssd1 = np.dot(freq, k_vector1)
+                wssd2 = np.dot(freq, k_vector2)
+
+                if wssd1 < wssd2:
+                    b = b1
+                    d = d1
+                    wssd = wssd1
+                    k_vector = k_vector1
+                    alpha_dim = alpha_dim1
+                else:
+                    b = b2
+                    d = d2
+                    wssd = wssd2
+                    k_vector = k_vector2
+                    alpha_dim = alpha_dim2
                 
-                if wssd < best[4]:
-                    best[0] = p1
-                    best[1] = b
-                    best[2] = p2
-                    best[3] = d
-                    best[4] = wssd
-        
-        alpha_d = (best[0] * alpha[dim - 1] + best[1]) / (best[2] * alpha[dim - 1] + best[3])
-        alpha[dim] = np.mod(alpha_d, 1)
-        t = np.mod(alpha[dim] * np.arange(1, N), 1)
-        kPrev = kPrev * (1 + (t * (t - 1) + 1/6) / ((dim+1) ** 2))
-        coeff[dim - 1, :] = [best[0], best[1], best[2], best[3]]
-        #print(coeff[dim - 1, :], best[4]) #debugging line to check the coefficients and wssd at each dimension
-    
+                if wssd < best_wssd: # if this candidate has a better wssd than the best found so far, we update the best coefficients and wssd
+                    coeff[dim-1, 0] = p1
+                    coeff[dim-1, 1] = b
+                    coeff[dim-1, 2] = p2
+                    coeff[dim-1, 3] = d
+                    best_wssd = wssd
+                    best_alpha = alpha_dim % 1
+                    best_k = k_vector
+        alpha[dim] = best_alpha # update the alpha vector with the best candidate found for this dimension
+
+        kPrev = best_k # update the k vector for the next dimension with the k vector of the best candidate found for this dimension
+
+        # print(coeff[dim - 1, :], (nK0[dim] - num + 2 * best_wssd)) # debugging line to check the coefficients and wssd at each dimension
+    if return_coeffs:
+        return alpha, coeff
     return alpha
 
 # quick and dirty test
-# print(kronecker_search_march_2026(10000, 20, 50))
\ No newline at end of file
+# print(kronecker_search_march_2026(2**15, 3, 50))
+

From 1e969b5a2d412a033bacc87d2c6cf7fa38353ffb Mon Sep 17 00:00:00 2001
From: Anders Pride <apride@hawk.illinoistech.edu>
Date: Tue, 26 May 2026 13:58:32 -0500
Subject: [PATCH 03/29] Kron search method returns wssd and discrepancies

---
 .../kronecker/kronecker_search_methods.py     | 87 ++++++++++++-------
 1 file changed, 54 insertions(+), 33 deletions(-)

diff --git a/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
index 45bd39d35..e4ed6ad2e 100644
--- a/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
+++ b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
@@ -1,30 +1,33 @@
 import numpy as np
 from sympy import gcdex, primerange, prime
+import time
 np.set_printoptions(precision=17)
 #https://github.com/sympy/sympy/releases
 
+
 # I can't find where Jimmy's code for the kronecker search from SURE 2025 is, so I've temporarily put my method here
 
-def kronecker_search_march_2026(N, dMax, searchsize, coord_weights=None, alpha_0=None, return_coeffs=False):
+def kronecker_search_march_2026(N, dMax, searchsize, coord_weights=None, gen_vec_init=None):
     """
     Args:
         N (int): The maximum sample size to be searched over.
         dMax (int): The maximum dimension for which to find the generating vector.
         searchsize (int): The number of primes to search over for each component of the generating vector.
         coord_weights (array-like, optional): An array of coordinate weights to use in the search. If None, weights are set to j^(-2).
-        alpha_0 (float, optional): The value for the first component of the generating vector. If None, the golden ratio is used. Note that alpha_0 is taken mod 1.
-        return_coeffs (bool, optional): Whether to return the coefficients of the linear transformation. Default is False.
+        gen_vec_init (array-like, optional): The initial value for the generating vector. If None, the golden ratio is used for the first component. Note that gen_vec_init is taken mod 1.
     Returns:
-        alpha, coeff (tuple):
-        - alpha (numpy array): The generating vector found by the search.
-        - coeff (numpy array, optional): The coefficients of the linear transformation used in the search, returned only if return_coeffs is True. A description of the coeff array is found below.
-    Complexity:
-        The time complexity of the search is O(searchsize^2 * dMax * N).
+        generating_vector, wssd, discrepancies, coeff (tuple):
+        - generating_vector (numpy array): The generating vector found by the search.
+        - wssd (float): The weighted sum of squared discrepancies for n = 1,...,N, for the generating vector found.
+        - discrepancies (numpy array): The discrepancies for n = 1,...,N.
+        - coeff (numpy array): The coefficients of the linear transformation used in the search. A description of the coeff array is found below.
+    Time cost:
+        The time cost of the search is O(searchsize^2 * dMax * N).
     Approach:
         Uses the quadratic Bernoulli polynomial kernel to conduct a CBC search for a generating vector, minimizing the weighted sum of squared discrepancies (wssd) with weights w_n = n.
     Details on coeff array:
-        The coeff array, if returned, is a (dMax-1) x 4 array where each row corresponds to a dimension from 2 to dMax. The columns correspond to the coefficients of the linear transformation used to compute the alpha component for that dimension. Specifically,
-        - alpha[dim+1] = (coeff[dim, 0] * alpha[dim] + coeff[dim, 1]) / (coeff[dim, 2] * alpha[dim] + coeff[dim, 3])
+        The coeff array is a (dMax-1) x 4 array where each row corresponds to a dimension from 2 to dMax. The columns correspond to the coefficients of the linear transformation used to compute the gen_vec component for that dimension. Specifically,
+        - gen_vec[dim+1] = (coeff[dim, 0] * gen_vec[dim] + coeff[dim, 1]) / (coeff[dim, 2] * gen_vec[dim] + coeff[dim, 3])
     """
 
     if searchsize < 2:
@@ -47,14 +50,14 @@ def kronecker_search_march_2026(N, dMax, searchsize, coord_weights=None, alpha_0
     # search over the first n primes, n = searchsize
     searchspace = np.array(list(primerange(1, prime(searchsize)+1)), dtype=np.float64)
 
-    # alpha is our generating vector, will be found cbc
-    alpha = np.zeros(dMax, dtype=np.float64)
+    # gen_vec is our generating vector, will be found cbc
+    gen_vec = np.zeros(dMax, dtype=np.float64)
 
-    # we pick the golden ratio as the first component of alpha, or let the user specify
-    if alpha_0 is None:
-        alpha[0] = np.float64((np.sqrt(5) - 1) / 2)
+    # we pick the golden ratio as the first component of gen_vec, or let the user specify
+    if gen_vec_init is None:
+        gen_vec[0] = np.float64((np.sqrt(5) - 1) / 2)
     else:
-        alpha[0] = np.mod(alpha_0, 1,dtype=np.float64)
+        gen_vec[0] = np.mod(gen_vec_init, 1,dtype=np.float64)
 
     # precompute several constants for the wssd calculation
     diff = np.cumsum(1.0 / np.arange(N, 1, -1,dtype=np.float64))
@@ -80,15 +83,15 @@ def kronecker_search_march_2026(N, dMax, searchsize, coord_weights=None, alpha_0
 
     # setting up some useful variables for the search
     coeff = np.zeros((dMax - 1, 4)) # stores the coefficients of the linear transformation at each dimension
-    t = alpha[0] * np.arange(1, N) % 1 # t vector is the vector of coordinates generated for the first dimension
+    t = gen_vec[0] * np.arange(1, N) % 1 # t vector is the vector of coordinates generated for the first dimension
     kPrev = 1 + coord_weights[0] * bernoulli2(t) # gets the k vector for the first dimension, which is used in the wssd calculation and updated each dimension of the search.
-    # The k vector is Ktilde(x_i) for i = 1,...,N-1, where Ktilde is the kernel and x_i are the points generated by the alpha vector, up to the current dimension.
+    # The k vector is Ktilde(x_i) for i = 1,...,N-1, where Ktilde is the kernel and x_i are the points generated by the gen_vec vector, up to the current dimension.
     
     # the main search loop
     for dim in range(1, dMax):
         best_wssd = np.inf # stores the current wssd found for each dimension, initialized to infinity
-        best_alpha = 0 # stores the current best alpha component found for this dimension, initialized to 0
-        best_k = None # stores the k vector for the current best alpha, used to update the k vector for the next dimension after the search is done for this dimension
+        best_gen_vec = 0 # stores the current best gen_vec component found for this dimension, initialized to 0
+        best_k = None # stores the k vector for the current best gen_vec, used to update the k vector for the next dimension after the search is done for this dimension
         for i in range(searchsize):
             p1 = searchspace[i] 
             for j in range(searchsize):
@@ -111,10 +114,10 @@ def kronecker_search_march_2026(N, dMax, searchsize, coord_weights=None, alpha_0
                     d2 = np.abs(d + p2)
                     b2 = np.abs(b - p1)
                 
-                alpha_dim1 = (p1 * alpha[dim - 1] + b1) / (p2 * alpha[dim - 1] + d1) # the linear transformation to get the next alpha_dim candidate to test
-                alpha_dim2 = (p1 * alpha[dim - 1] + b2) / (p2 * alpha[dim - 1] + d2) # the other candidate from the linear transformation
-                t1 = (alpha_dim1 * np.arange(1, N)) - np.floor(alpha_dim1 * np.arange(1, N)) # vector of coordinates generated by this candidate component
-                t2 = (alpha_dim2 * np.arange(1, N)) - np.floor(alpha_dim2 * np.arange(1, N)) 
+                gen_vec_dim1 = (p1 * gen_vec[dim - 1] + b1) /  (p2 * gen_vec[dim - 1] + d1) # the linear transformation to get the next gen_vec_dim candidate to test
+                gen_vec_dim2 = (p1 * gen_vec[dim - 1] + b2) / (p2 * gen_vec[dim - 1] + d2) # the other candidate from the linear transformation
+                t1 = (gen_vec_dim1 * np.arange(1, N)) - np.floor(gen_vec_dim1 * np.arange(1, N)) # vector of coordinates generated by this candidate component
+                t2 = (gen_vec_dim2 * np.arange(1, N)) - np.floor(gen_vec_dim2 * np.arange(1, N)) 
                 k_vector1 = kPrev * (1 + bernoulli2(t1) * coord_weights[dim]) # get the k vector for this candidate component, used in the wssd calculation
                 k_vector2 = kPrev * (1 + bernoulli2(t2) * coord_weights[dim]) 
 
@@ -126,13 +129,13 @@ def kronecker_search_march_2026(N, dMax, searchsize, coord_weights=None, alpha_0
                     d = d1
                     wssd = wssd1
                     k_vector = k_vector1
-                    alpha_dim = alpha_dim1
+                    gen_vec_dim = gen_vec_dim1
                 else:
                     b = b2
                     d = d2
                     wssd = wssd2
                     k_vector = k_vector2
-                    alpha_dim = alpha_dim2
+                    gen_vec_dim = gen_vec_dim2
                 
                 if wssd < best_wssd: # if this candidate has a better wssd than the best found so far, we update the best coefficients and wssd
                     coeff[dim-1, 0] = p1
@@ -140,17 +143,35 @@ def kronecker_search_march_2026(N, dMax, searchsize, coord_weights=None, alpha_0
                     coeff[dim-1, 2] = p2
                     coeff[dim-1, 3] = d
                     best_wssd = wssd
-                    best_alpha = alpha_dim % 1
+                    best_gen_vec = gen_vec_dim % 1
                     best_k = k_vector
-        alpha[dim] = best_alpha # update the alpha vector with the best candidate found for this dimension
+        gen_vec[dim] = best_gen_vec # update the gen_vec vector with the best candidate found for this dimension
 
         kPrev = best_k # update the k vector for the next dimension with the k vector of the best candidate found for this dimension
+        best_wssd = nK0[dim] - num + 2 * best_wssd # calculate the best wssd for this dimension using the formula from the paper, which involves the nK0 constants precomputed at the beginning of the function. This is used for debugging and to check the wssd at each dimension of the search.
 
         # print(coeff[dim - 1, :], (nK0[dim] - num + 2 * best_wssd)) # debugging line to check the coefficients and wssd at each dimension
-    if return_coeffs:
-        return alpha, coeff
-    return alpha
+    
+    # Adapted from Jimmy's code for calculating the discrepancies for n = 1,...,N from SURE 2025
+    n_array = np.arange(1, N + 1)
+    k_tilde = lambda x, coord_weight: np.prod(1 + bernoulli2(x) * coord_weight, axis=1)
+    k_tilde_terms = k_tilde(gen_vec * np.arange(N).reshape((N, 1)) - np.floor(gen_vec * np.arange(N).reshape((N, 1))), coord_weights)
 
-# quick and dirty test
-# print(kronecker_search_march_2026(2**15, 3, 50))
+    left_sum = np.cumsum(k_tilde_terms[1:]) * n_array[1:]
+    right_sum = np.cumsum(n_array[:-1] * k_tilde_terms[1:])
+        
+    k_tilde_zero_terms = k_tilde_terms[0] * n_array
+    summation = np.zeros(N)
+    summation[1:] = left_sum - right_sum
+    discrepancies = (k_tilde_zero_terms + 2 * summation) / (n_array ** 2) - 1
 
+    return gen_vec, best_wssd, discrepancies, coeff
+
+
+# quick and dirty test
+start = time.time()
+a, wssd, _, _ = kronecker_search_march_2026(2**10, 20, 50)
+print(a)
+print(wssd)
+end = time.time()
+print("Run time (seconds): ", end - start)
\ No newline at end of file

From 1ff125dfd79f9a2374795377b0bef1ccaada5bd4 Mon Sep 17 00:00:00 2001
From: Anders Pride <apride@hawk.illinoistech.edu>
Date: Mon, 8 Jun 2026 19:31:58 -0500
Subject: [PATCH 04/29] Added lattice discrepancy computation for any sample
 size, and lattice rule search method

---
 .../discrete_distribution/lattice/lattice.py  |  80 +++++++++++
 .../lattice/lattice_vector_wssd_search.py     | 128 ++++++++++++++++++
 2 files changed, 208 insertions(+)
 create mode 100644 qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py

diff --git a/qmcpy/discrete_distribution/lattice/lattice.py b/qmcpy/discrete_distribution/lattice/lattice.py
index 248692c32..1ad05069c 100644
--- a/qmcpy/discrete_distribution/lattice/lattice.py
+++ b/qmcpy/discrete_distribution/lattice/lattice.py
@@ -385,3 +385,83 @@ def _spawn(self, child_seed, dimension):
             order=self.order,
             m_max=self.input_m_max,
         )
+
+    def expected_squared_periodic_discrepancies(self, n_max, coord_weights=None, kernel=None):
+        """Returns the expected squared periodic discrepancies for each of the first n_max points of the lattice sequence.
+        Args:
+            n_max (int): Maximum number of points to calculate the squared periodic discrepancies for.
+            coord_weights (Union[None, np.ndarray]): Coordinate weights for the discrepancy calculation. If None, uses weights gamma_j = j^(-2).
+            kernel (Union[None, Callable]): Kernel function for the discrepancy calculation. If None, uses the second bernoulli polynomial.
+        Returns:
+            discs (np.ndarray): The expected squared periodic discrepancies for the first n_max points.
+        """
+
+        if coord_weights is not None and len(coord_weights) < self.d:
+            raise ValueError("Length of coord_weights must be greater than or equal to the dimension of the lattice")
+        if coord_weights is None:
+            coord_weights = np.array([j**(-2) for j in range(1, self.d + 1)], dtype=np.float64)
+        if self.order == "LINEAR":
+            raise NotImplementedError("expected_squared_periodic_discrepancies not implemented for linear order")
+
+        if kernel is None:
+            kernel = lambda x: x * (x - 1) + 1/6
+
+        k_tilde = lambda x: np.prod(1 + coord_weights * kernel(x), axis=-1)
+        
+        # generate the vdc points without any random shift
+        r_x = np.uint64(self.gen_vec.shape[0])
+        n = np.uint64(2**(np.ceil(np.log2(n_max))))
+        d = np.uint64(self.d)
+        n_start = np.uint64(0)
+        x = np.empty((r_x, n, d), dtype=np.float64)
+        _ = qmctoolscl.lat_gen_natural(r_x, n, d, n_start, self.gen_vec, x, backend="c")
+        s = x
+
+        # evaluate the kernel on the sample points
+        k_vector = k_tilde(s)
+        k_vector = k_vector.reshape(-1)
+
+        # get the constant vector term of the summation
+        k_const = -1 + k_vector[0]*np.array([j**(-1) for j in range(1, n_max + 1)], dtype=np.float64)
+
+        # group the kernel evaluations by powers of 2
+        k_sum = np.zeros(np.ceil(np.log2(n_max)).astype(int), dtype=np.float64)
+        for i in range(k_sum.size):
+            k_sum[i] = np.sum(k_vector[2**i:(2**(i+1))])
+
+        # get the frequency matrix for how often each kernel evaluation appears (this is always the same and can be precomputed, not done here to avoid adding >1GB txt file to git)
+        freq_mtx = np.zeros((k_sum.size, n_max), dtype=np.float64)
+        for i in range(1,n_max):
+            for j in range(k_sum.size):
+                if np.floor(i / 2**j) % 2 == 1:
+                    freq_mtx[j, i] = freq_mtx[j, i-1] + 2
+                else:
+                    freq_mtx[j, i] = freq_mtx[j, i-1]         
+        for i in range(n_max):
+            freq_mtx[:,i] = freq_mtx[:,i] * (i + 1)**(-2)
+
+        # multiply by the precomputed frequency matrix and add the constant vector
+        discs = k_const + np.vecmat(k_sum, freq_mtx)
+        return discs
+
+
+    def wssd(self, n_max, coord_weights=None, sample_weights=None):
+        """Returns the weighted sum of the expected squared periodic discrepancies for the first n points of the lattice sequence.
+        Args:
+            n (int): Number of points to calculate the weighted squared periodic discrepancy for.
+            coord_weights (Union[None, np.ndarray]): Coordinate weights for the discrepancy calculation. If None, uses weights gamma_j = j^(-2).
+            sample_weights (Union[None, np.ndarray]): Sample weights for the weighted squared periodic discrepancy calculation. If None, uses weights w_n = n. Note that the time cost may be higher for other sample weights.
+        Returns:
+            wssd (float): The weighted squared periodic discrepancy.
+        """
+        if coord_weights is not None and len(coord_weights) < self.d:
+            raise ValueError("Length of coord_weights must be greater than or equal to the dimension of the lattice")
+        if sample_weights is not None and len(sample_weights) < n_max:
+            raise ValueError("Length of sample_weights must be equal to n_max")
+        if sample_weights is None:
+            sample_weights = np.arange(1, n_max + 1, dtype=np.float64)
+
+        discs = self.expected_squared_periodic_discrepancies(n_max, coord_weights=coord_weights)
+        wssd = np.dot(sample_weights, discs)
+        
+        return wssd
\ No newline at end of file
diff --git a/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
new file mode 100644
index 000000000..5c7012622
--- /dev/null
+++ b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
@@ -0,0 +1,128 @@
+import numpy as np
+
+# I am not sure where the best place to put this is, will ask Aleksi
+
+def lattice_search(N, d):
+    m = np.ceil(np.log2(N)).astype(int)
+
+    # ----------------------------------------------------------------------
+    # Set up rhovector
+    # ----------------------------------------------------------------------
+    bits = np.zeros((N, m), dtype=int)
+    for i in range(N):
+        # 2*bitget(i,1:m) in MATLAB
+        bits[i, :] = 2 * np.array([((i >> j) & 1) for j in range(m)], dtype=int)
+
+    cumsumbits = np.cumsum(bits, axis=0)        # N x m
+    rhovector = np.dot((1.0 / np.arange(1, N + 1)), cumsumbits)  # 1 x m
+
+    rhovectorNx1 = np.zeros((2**m - 1, 1))
+    rIdx1 = 0
+    for r in range(m, 0, -1):
+        rIdx2 = rIdx1 + 2**(r - 1) - 1
+        rhovectorNx1[rIdx1:rIdx2 + 1, 0] = rhovector[r - 1]
+        rIdx1 = rIdx2 + 1
+
+    # ----------------------------------------------------------------------
+    # Get ordering of the search space
+    # ----------------------------------------------------------------------
+    gR = np.ones(2**(m - 2), dtype=int)
+    intMod = 2**m
+    for idx in range(1, 2**(m - 2)):
+        temp = (gR[idx - 1] * 5) % intMod
+        gR[idx] = min(intMod - temp, temp)
+
+    gRows = np.ones(2**(m - 1), dtype=int)
+    gRows[-1] = 0
+    rowVects = np.ones(2**m - 1, dtype=int)
+    gStrtIdx = 0
+    vStrtIdx = 0
+
+    for l in range(m, 1, -1):
+        gEndIdx = gStrtIdx + 2**(l - 2) - 1
+        vEndIdx = vStrtIdx + 2**(l - 1) - 1
+
+        gRow = np.ones(2**(l - 2), dtype=int)
+        intMod = 2**l
+        for idx in range(1, 2**(l - 2)):
+            temp = (gRow[idx - 1] * 5) % intMod
+            gRow[idx] = min(intMod - temp, temp)
+
+        gRows[gStrtIdx:gEndIdx + 1] = gRow
+        rowV = np.concatenate(([1], np.flip(gRow[1:])))
+        doubled = np.concatenate((rowV, rowV))
+        rowVects[vStrtIdx:vEndIdx + 1] = 2**(m - l) * doubled
+
+        gStrtIdx = gEndIdx + 1
+        vStrtIdx = vEndIdx + 1
+
+    rowVects[-1] = 2**(m - 1)
+
+    # ----------------------------------------------------------------------
+    # Set up prodV
+    # ----------------------------------------------------------------------
+    prodV = np.ones((2**m - 1, 1))
+    prodV = prodV * rhovectorNx1
+
+    # Initial 1D case
+    rowV = rowVects / 2**m
+    rowV = 1 + (rowV * (rowV - 1) + 1 / 6)
+    prodV = prodV * rowV[:, None]
+
+    # Set up k0
+    k0 = 7 / 6
+
+    # ----------------------------------------------------------------------
+    # Begin search
+    # ----------------------------------------------------------------------
+    h = np.ones(d, dtype=int)
+
+    for hComp in range(2, d + 1):
+        WSSD = np.zeros(2**(m - 2))
+
+        gamma = 1 / (hComp**2)
+        omega = lambda x: 1 + gamma * (x * (x - 1) + 1 / 6)
+
+        k0 = k0 * (1 + gamma / 6)
+
+        curIdx2 = 0
+        prodIdx1 = 0
+        for l in range(m, 1, -1):
+            nextIdx2 = curIdx2 + 2**(l - 2) - 1
+            prodIdx2 = prodIdx1 + 2**(l - 2) - 1
+
+            curRow = gRows[curIdx2:nextIdx2 + 1]
+            col = curRow / 2**l
+            fftCol = omega(col)
+
+            pCol = prodV[prodIdx1:prodIdx2 + 1, 0]
+
+            wVector = 2 * np.fft.ifft(np.fft.fft(fftCol) * np.fft.fft(pCol)).real
+            numrep = 2**(m - l)
+            WSSD = WSSD + np.tile(wVector, numrep)
+
+            curIdx2 = nextIdx2 + 1
+            prodIdx1 = prodIdx2 + 2**(l - 2) + 1
+
+        WSSD = WSSD + omega(1 / 2) * prodV[-1, 0]
+        WSSD = WSSD + N * k0 - N * (N + 1) / 2
+
+        bestIdx = int(np.argmin(WSSD))
+        bestWSSD = float(WSSD[bestIdx])
+        newH = int(gR[bestIdx])
+
+        # Avoid duplicates
+        while newH in h:
+            WSSD[bestIdx] = np.inf
+            bestIdx = int(np.argmin(WSSD))
+            bestWSSD = float(WSSD[bestIdx])
+            newH = int(gR[bestIdx])
+
+        h[hComp - 1] = newH
+
+        rowV = (newH * rowVects) % 2**m
+        rowV = rowV / 2**m
+        rowV = omega(rowV)
+        prodV = prodV * rowV[:, None]
+
+    return h
\ No newline at end of file

From cef4e703f4a070facdefbf1429a09349f2a706df Mon Sep 17 00:00:00 2001
From: Anders Pride <apride@hawk.illinoistech.edu>
Date: Tue, 9 Jun 2026 16:43:20 -0500
Subject: [PATCH 05/29] Added demo for lattice and Kronecker methods

---
 demos/lattice_kronecker_methods.ipynb         |  240 ++
 .../kronecker/kronecker_search_methods.py     |   26 +-
 .../kuo.lattice-39102-1024-1048576.3600.txt   | 3600 +++++++++++++++++
 .../lattice/lattice_vector_wssd_search.py     |   15 +-
 4 files changed, 3864 insertions(+), 17 deletions(-)
 create mode 100644 demos/lattice_kronecker_methods.ipynb
 create mode 100644 qmcpy/discrete_distribution/lattice/generating_vectors/kuo.lattice-39102-1024-1048576.3600.txt

diff --git a/demos/lattice_kronecker_methods.ipynb b/demos/lattice_kronecker_methods.ipynb
new file mode 100644
index 000000000..85be78d1a
--- /dev/null
+++ b/demos/lattice_kronecker_methods.ipynb
@@ -0,0 +1,240 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "492a51ed",
+   "metadata": {},
+   "source": [
+    "# Lattice and Kronecker Methods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2e06ea48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qmcpy import *\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot\n",
+    "from time import time\n",
+    "np.set_printoptions(legacy='1.25')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39b265e4",
+   "metadata": {},
+   "source": [
+    "## Discrepancy Values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c1e69d8",
+   "metadata": {},
+   "source": [
+    "#### Lattice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "958e16e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim = 50\n",
+    "n = 2**20\n",
+    "lat = Lattice(dimension=dim, order=\"RADICAL_INVERSE\", seed=12) # initialize a lattice as usual\n",
+    "coord_weights = np.array([j**(-2) for j in range(1, dim + 1)]) # define some coordinate weights\n",
+    "\n",
+    "lat_discs = lat.expected_squared_periodic_discrepancies(n_max=n, coord_weights=coord_weights) # compute the expected squared periodic discrepancies for n = 1, 2, ...\n",
+    "\n",
+    "sample_weights = np.arange(1, n+1) # define some sample weights\n",
+    "lat_wssd = lat.wssd(n_max=n, coord_weights=coord_weights, sample_weights=sample_weights) # compute the wssd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f174209f",
+   "metadata": {},
+   "source": [
+    "#### Kronecker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c8eec507",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\headw\\QMCSoftware\\qmcpy\\discrete_distribution\\kronecker\\kronecker.py:276: RuntimeWarning: CBC generating vector only supports dimension <= 13; falling back to Richtmyer.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "kron = Kronecker(dimension=dim, seed=12) # initialize a Kronecker sequence as usual\n",
+    "kron_k_tilde = (lambda x, gamma: np.prod(1 + (x * (x - 1) + 1/6) * gamma, axis=-1), 1) # define the kernel function (in this case, the second Bernoulli polynomial)\n",
+    "\n",
+    "kron_discs = kron._square_periodic_discrepancies(n = n, k_tilde = kron_k_tilde, gamma = coord_weights) # compute the expected squared periodic discrepancies for n = 1, 2, ...\n",
+    "\n",
+    "kron_wssd = kron.wssd_discrepancy(n = n, weights = sample_weights, k_tilde = kron_k_tilde, gamma = coord_weights) # compute the wssd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1c20186",
+   "metadata": {},
+   "source": [
+    "## Searches"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01664bbb",
+   "metadata": {},
+   "source": [
+    "#### Lattice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "524e3b99",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time taken for lattice vector wssd search:  8.519208669662476\n",
+      "Searched lattice vector:  [     1 444567 406809  53917 411513 111013  57773  23363 278133 179399\n",
+      " 145725 480145 365723 134199 361515 297163 315729 250703 322429 236947\n",
+      " 508553 455183 148433  37975  46187 474063 490317  14811 417263 342641\n",
+      " 474417 269197 309749  29993 366775 433399 240621 375377  84847 232327\n",
+      " 214987 375079  32109 388283 153487 140919 390453 362317 413527 405689]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qmcpy.discrete_distribution.lattice.lattice_vector_wssd_search import lattice_vector_wssd_search\n",
+    "\n",
+    "# note that the search requires that the sample weights be w_n = n, so they are not customizable\n",
+    "\n",
+    "time_start = time()\n",
+    "searched_lattice_vector = lattice_vector_wssd_search(N = n, d = dim, kernel = lambda x: x * (x - 1) + 1 / 6, coord_weights = coord_weights) # search for a lattice vector with low wssd\n",
+    "time_end = time()\n",
+    "print(\"Time taken for lattice vector wssd search: \", time_end - time_start)\n",
+    "print(\"Searched lattice vector: \", searched_lattice_vector)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9efeb0fa",
+   "metadata": {},
+   "source": [
+    "#### Kronecker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "09388fbc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qmcpy.discrete_distribution.kronecker.kronecker_search_methods import kronecker_search_march_2026\n",
+    "\n",
+    "searchsize = 25 # the time cost is  O(dim * N * searchsize^2), so searchsize should be chosen with care. The largest I have run was in MATLAB with searchsize = 300, N = 2^20, d = 100, which took about 24 hours \n",
+    "\n",
+    "# note that the search requires that the sample weights be w_n = n, so they are not customizable\n",
+    "\n",
+    "time_start = time()\n",
+    "searched_kron_vector = kronecker_search_march_2026(N = n, dMax = dim, kernel = lambda x: x * (x - 1) + 1 / 6, searchsize = searchsize, coord_weights = coord_weights) # search for a Kronecker vector with low wssd\n",
+    "time_end = time()\n",
+    "\n",
+    "print(\"Time taken for Kronecker vector wssd search: \", time_end - time_start)\n",
+    "print(\"Searched Kronecker vector: \", searched_kron_vector)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26a634f7",
+   "metadata": {},
+   "source": [
+    "## Plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f66d72b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "qmcpy\\discrete_distribution\\lattice\\generating_vectors\\kuo.lattice-39102-1024-1048576.3600.txt not found.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m lat1 = Lattice(dimension=dim, order=\u001b[33m\"\u001b[39m\u001b[33mRADICAL_INVERSE\u001b[39m\u001b[33m\"\u001b[39m, seed=\u001b[32m12\u001b[39m, generating_vector=\u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mloadtxt\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mqmcpy\u001b[39;49m\u001b[33;43m\\\u001b[39;49m\u001b[33;43mdiscrete_distribution\u001b[39;49m\u001b[33;43m\\\u001b[39;49m\u001b[33;43mlattice\u001b[39;49m\u001b[33;43m\\\u001b[39;49m\u001b[33;43mgenerating_vectors\u001b[39;49m\u001b[33;43m\\\u001b[39;49m\u001b[33;43mkuo.lattice-39102-1024-1048576.3600.txt\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m, m_max=\u001b[32m20\u001b[39m) \u001b[38;5;66;03m# initialize a lattice with the generating vector of all 1s\u001b[39;00m\n\u001b[32m      2\u001b[39m lat_discs1 = lat1.expected_squared_periodic_discrepancies(n_max=n, coord_weights=coord_weights)\n\u001b[32m      5\u001b[39m lat2 = Lattice(dimension=dim, order=\u001b[33m\"\u001b[39m\u001b[33mRADICAL_INVERSE\u001b[39m\u001b[33m\"\u001b[39m, seed=\u001b[32m12\u001b[39m, generating_vector=np.uint64(searched_lattice_vector), m_max=\u001b[32m20\u001b[39m) \u001b[38;5;66;03m# initialize a lattice with the searched lattice vector\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\headw\\miniconda3\\envs\\qmcpy\\Lib\\site-packages\\numpy\\lib\\_npyio_impl.py:1397\u001b[39m, in \u001b[36mloadtxt\u001b[39m\u001b[34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin, encoding, max_rows, quotechar, like)\u001b[39m\n\u001b[32m   1394\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(delimiter, \u001b[38;5;28mbytes\u001b[39m):\n\u001b[32m   1395\u001b[39m     delimiter = delimiter.decode(\u001b[33m'\u001b[39m\u001b[33mlatin1\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m1397\u001b[39m arr = \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcomment\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcomment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdelimiter\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdelimiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1398\u001b[39m \u001b[43m            \u001b[49m\u001b[43mconverters\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconverters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskiplines\u001b[49m\u001b[43m=\u001b[49m\u001b[43mskiprows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[43m=\u001b[49m\u001b[43musecols\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1399\u001b[39m \u001b[43m            \u001b[49m\u001b[43munpack\u001b[49m\u001b[43m=\u001b[49m\u001b[43munpack\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mndmin\u001b[49m\u001b[43m=\u001b[49m\u001b[43mndmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1400\u001b[39m \u001b[43m            \u001b[49m\u001b[43mmax_rows\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmax_rows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquote\u001b[49m\u001b[43m=\u001b[49m\u001b[43mquotechar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1402\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m arr\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\headw\\miniconda3\\envs\\qmcpy\\Lib\\site-packages\\numpy\\lib\\_npyio_impl.py:1024\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(fname, delimiter, comment, quote, imaginary_unit, usecols, skiplines, max_rows, converters, ndmin, unpack, dtype, encoding)\u001b[39m\n\u001b[32m   1022\u001b[39m     fname = os.fspath(fname)\n\u001b[32m   1023\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fname, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m-> \u001b[39m\u001b[32m1024\u001b[39m     fh = \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_datasource\u001b[49m\u001b[43m.\u001b[49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mrt\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1025\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m encoding \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m   1026\u001b[39m         encoding = \u001b[38;5;28mgetattr\u001b[39m(fh, \u001b[33m'\u001b[39m\u001b[33mencoding\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mlatin1\u001b[39m\u001b[33m'\u001b[39m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\headw\\miniconda3\\envs\\qmcpy\\Lib\\site-packages\\numpy\\lib\\_datasource.py:192\u001b[39m, in \u001b[36mopen\u001b[39m\u001b[34m(path, mode, destpath, encoding, newline)\u001b[39m\n\u001b[32m    155\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    156\u001b[39m \u001b[33;03mOpen `path` with `mode` and return the file object.\u001b[39;00m\n\u001b[32m    157\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m    188\u001b[39m \n\u001b[32m    189\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    191\u001b[39m ds = DataSource(destpath)\n\u001b[32m--> \u001b[39m\u001b[32m192\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mds\u001b[49m\u001b[43m.\u001b[49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\headw\\miniconda3\\envs\\qmcpy\\Lib\\site-packages\\numpy\\lib\\_datasource.py:529\u001b[39m, in \u001b[36mDataSource.open\u001b[39m\u001b[34m(self, path, mode, encoding, newline)\u001b[39m\n\u001b[32m    526\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m _file_openers[ext](found, mode=mode,\n\u001b[32m    527\u001b[39m                               encoding=encoding, newline=newline)\n\u001b[32m    528\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m529\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m not found.\u001b[39m\u001b[33m\"\u001b[39m)\n",
+      "\u001b[31mFileNotFoundError\u001b[39m: qmcpy\\discrete_distribution\\lattice\\generating_vectors\\kuo.lattice-39102-1024-1048576.3600.txt not found."
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# lat1 = Lattice(dimension=dim, order=\"RADICAL_INVERSE\", seed=12, generating_vector=np.loadtxt(\"qmcpy\\discrete_distribution\\lattice\\generating_vectors\\kuo.lattice-39102-1024-1048576.3600.txt\"), m_max=20) # initialize a lattice with the generating vector of all 1s\n",
+    "# lat_discs1 = lat1.expected_squared_periodic_discrepancies(n_max=n, coord_weights=coord_weights)\n",
+    "\n",
+    "\n",
+    "lat2 = Lattice(dimension=dim, order=\"RADICAL_INVERSE\", seed=12, generating_vector=np.uint64(searched_lattice_vector), m_max=20) # initialize a lattice with the searched lattice vector\n",
+    "lat_discs2 = lat2.expected_squared_periodic_discrepancies(n_max=n, coord_weights=coord_weights)\n",
+    "\n",
+    "\n",
+    "fig, ax = pyplot.subplots(nrows=1, ncols=1, figsize=(12,10))\n",
+    "ax.plot(np.arange(1, n+1), np.sqrt(lat_discs), label=\"Old Lattice Discrepancy\")\n",
+    "ax.plot(np.arange(1, n+1), np.sqrt(lat_discs2), label=\"Searched Lattice Discrepancy\")\n",
+    "# ax.plot(np.arange(1, n+1), np.sqrt(kron_discs), label=\"Kronecker Discrepancy\")\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_yscale(\"log\")\n",
+    "ax.legend()\n",
+    "ax.set_xlabel(\"Sample Size\")\n",
+    "ax.set_ylabel(\"Periodic Discrepancy\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "qmcpy",
+   "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": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
index e4ed6ad2e..bd6108b95 100644
--- a/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
+++ b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
@@ -7,11 +7,12 @@
 
 # I can't find where Jimmy's code for the kronecker search from SURE 2025 is, so I've temporarily put my method here
 
-def kronecker_search_march_2026(N, dMax, searchsize, coord_weights=None, gen_vec_init=None):
+def kronecker_search_march_2026(N, dMax, searchsize, kernel=None, coord_weights=None, gen_vec_init=None):
     """
     Args:
         N (int): The maximum sample size to be searched over.
         dMax (int): The maximum dimension for which to find the generating vector.
+        kernel (function): The kernel function to use in the search.
         searchsize (int): The number of primes to search over for each component of the generating vector.
         coord_weights (array-like, optional): An array of coordinate weights to use in the search. If None, weights are set to j^(-2).
         gen_vec_init (array-like, optional): The initial value for the generating vector. If None, the golden ratio is used for the first component. Note that gen_vec_init is taken mod 1.
@@ -41,7 +42,8 @@ def kronecker_search_march_2026(N, dMax, searchsize, coord_weights=None, gen_vec
     
 
     # the quadratic Bernoulli polynomial
-    bernoulli2 = lambda t: t * (t - 1) + 1/6
+    if kernel is None:
+        kernel = lambda t: t * (t - 1) + 1/6
     
     # define coordinate weights if not provided, default to j^(-2)
     if coord_weights is None:
@@ -84,7 +86,7 @@ def kronecker_search_march_2026(N, dMax, searchsize, coord_weights=None, gen_vec
     # setting up some useful variables for the search
     coeff = np.zeros((dMax - 1, 4)) # stores the coefficients of the linear transformation at each dimension
     t = gen_vec[0] * np.arange(1, N) % 1 # t vector is the vector of coordinates generated for the first dimension
-    kPrev = 1 + coord_weights[0] * bernoulli2(t) # gets the k vector for the first dimension, which is used in the wssd calculation and updated each dimension of the search.
+    kPrev = 1 + coord_weights[0] * kernel(t) # gets the k vector for the first dimension, which is used in the wssd calculation and updated each dimension of the search.
     # The k vector is Ktilde(x_i) for i = 1,...,N-1, where Ktilde is the kernel and x_i are the points generated by the gen_vec vector, up to the current dimension.
     
     # the main search loop
@@ -118,8 +120,8 @@ def kronecker_search_march_2026(N, dMax, searchsize, coord_weights=None, gen_vec
                 gen_vec_dim2 = (p1 * gen_vec[dim - 1] + b2) / (p2 * gen_vec[dim - 1] + d2) # the other candidate from the linear transformation
                 t1 = (gen_vec_dim1 * np.arange(1, N)) - np.floor(gen_vec_dim1 * np.arange(1, N)) # vector of coordinates generated by this candidate component
                 t2 = (gen_vec_dim2 * np.arange(1, N)) - np.floor(gen_vec_dim2 * np.arange(1, N)) 
-                k_vector1 = kPrev * (1 + bernoulli2(t1) * coord_weights[dim]) # get the k vector for this candidate component, used in the wssd calculation
-                k_vector2 = kPrev * (1 + bernoulli2(t2) * coord_weights[dim]) 
+                k_vector1 = kPrev * (1 + kernel(t1) * coord_weights[dim]) # get the k vector for this candidate component, used in the wssd calculation
+                k_vector2 = kPrev * (1 + kernel(t2) * coord_weights[dim]) 
 
                 wssd1 = np.dot(freq, k_vector1)
                 wssd2 = np.dot(freq, k_vector2)
@@ -154,7 +156,7 @@ def kronecker_search_march_2026(N, dMax, searchsize, coord_weights=None, gen_vec
     
     # Adapted from Jimmy's code for calculating the discrepancies for n = 1,...,N from SURE 2025
     n_array = np.arange(1, N + 1)
-    k_tilde = lambda x, coord_weight: np.prod(1 + bernoulli2(x) * coord_weight, axis=1)
+    k_tilde = lambda x, coord_weight: np.prod(1 + kernel(x) * coord_weight, axis=1)
     k_tilde_terms = k_tilde(gen_vec * np.arange(N).reshape((N, 1)) - np.floor(gen_vec * np.arange(N).reshape((N, 1))), coord_weights)
 
     left_sum = np.cumsum(k_tilde_terms[1:]) * n_array[1:]
@@ -169,9 +171,9 @@ def kronecker_search_march_2026(N, dMax, searchsize, coord_weights=None, gen_vec
 
 
 # quick and dirty test
-start = time.time()
-a, wssd, _, _ = kronecker_search_march_2026(2**10, 20, 50)
-print(a)
-print(wssd)
-end = time.time()
-print("Run time (seconds): ", end - start)
\ No newline at end of file
+# start = time.time()
+# a, wssd, _, _ = kronecker_search_march_2026(2**10, 20, 50)
+# print(a)
+# print(wssd)
+# end = time.time()
+# print("Run time (seconds): ", end - start)
\ No newline at end of file
diff --git a/qmcpy/discrete_distribution/lattice/generating_vectors/kuo.lattice-39102-1024-1048576.3600.txt b/qmcpy/discrete_distribution/lattice/generating_vectors/kuo.lattice-39102-1024-1048576.3600.txt
new file mode 100644
index 000000000..e147de362
--- /dev/null
+++ b/qmcpy/discrete_distribution/lattice/generating_vectors/kuo.lattice-39102-1024-1048576.3600.txt
@@ -0,0 +1,3600 @@
+    1         1
+    2    433461
+    3    472323
+    4    440637
+    5    231645
+    6    275007
+    7    113895
+    8    331051
+    9    283181
+   10    384579
+   11    288619
+   12    306439
+   13    309943
+   14    452525
+   15    319841
+   16    217509
+   17     84615
+   18    111067
+   19    374949
+   20    315005
+   21    369473
+   22     95709
+   23    155273
+   24    215539
+   25    486377
+   26    107399
+   27    203705
+   28    168683
+   29    154561
+   30    254753
+   31    148129
+   32    117715
+   33    455263
+   34     64579
+   35    450769
+   36    169605
+   37    135865
+   38    239917
+   39     62503
+   40    381173
+   41    417239
+   42    354955
+   43    389279
+   44    457757
+   45    224125
+   46    137945
+   47    140507
+   48    402845
+   49     50989
+   50     42635
+   51    399209
+   52    455121
+   53    487667
+   54    474267
+   55    372965
+   56    429179
+   57     81069
+   58     30905
+   59    509195
+   60    160335
+   61    323865
+   62    153271
+   63    236745
+   64    270649
+   65    465083
+   66    317775
+   67    247885
+   68     99677
+   69    437389
+   70    211881
+   71    289405
+   72    215751
+   73    517253
+   74    240467
+   75    492367
+   76    512859
+   77     37555
+   78    427223
+   79    202991
+   80    329235
+   81    257587
+   82    508363
+   83     99177
+   84    164915
+   85    302617
+   86    216133
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+   88    242089
+   89     51161
+   90     76347
+   91    456301
+   92    198763
+   93     32097
+   94    495921
+   95    205443
+   96    144227
+   97     73919
+   98    468177
+   99    222321
+  100    126651
+  101    257177
+  102    507247
+  103    185133
+  104    358989
+  105    246357
+  106     40767
+  107    515589
+  108    269667
+  109     76563
+  110    365147
+  111    451759
+  112    194315
+  113    118517
+  114    373699
+  115    148541
+  116    343011
+  117    456827
+  118     71289
+  119    277797
+  120     48513
+  121    197421
+  122    362433
+  123    195259
+  124    207401
+  125    170191
+  126    338789
+  127    482571
+  128    266525
+  129     69419
+  130    241555
+  131    416591
+  132    360849
+  133    295805
+  134    152147
+  135    346543
+  136    316837
+  137    210915
+  138    469327
+  139    487023
+  140    319673
+  141     89575
+  142    360997
+  143    331655
+  144     44915
+  145    476083
+  146    422325
+  147     34159
+  148     52753
+  149    205401
+  150    276589
+  151     93013
+  152    124439
+  153    496919
+  154    119415
+  155    511763
+  156    294163
+  157    444073
+  158    235361
+  159    402073
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diff --git a/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
index 5c7012622..c1b02c2ad 100644
--- a/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
+++ b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
@@ -2,7 +2,12 @@
 
 # I am not sure where the best place to put this is, will ask Aleksi
 
-def lattice_search(N, d):
+def lattice_vector_wssd_search(N, d, kernel,coord_weights):
+    if kernel == None:
+        kernel = lambda x: x * (x - 1) + 1 / 6 # default kernel is the second Bernoulli polynomial
+    if coord_weights is None:
+        coord_weights = np.array([j**(-2) for j in range(1, d + 1)], dtype=np.float64) # default coordinate weights are j^(-2)
+
     m = np.ceil(np.log2(N)).astype(int)
 
     # ----------------------------------------------------------------------
@@ -66,11 +71,11 @@ def lattice_search(N, d):
 
     # Initial 1D case
     rowV = rowVects / 2**m
-    rowV = 1 + (rowV * (rowV - 1) + 1 / 6)
+    rowV = 1 + kernel(rowV)
     prodV = prodV * rowV[:, None]
 
     # Set up k0
-    k0 = 7 / 6
+    k0 = 1 + coord_weights[0] * kernel(0)
 
     # ----------------------------------------------------------------------
     # Begin search
@@ -80,10 +85,10 @@ def lattice_search(N, d):
     for hComp in range(2, d + 1):
         WSSD = np.zeros(2**(m - 2))
 
-        gamma = 1 / (hComp**2)
+        gamma = coord_weights[hComp - 1]
         omega = lambda x: 1 + gamma * (x * (x - 1) + 1 / 6)
 
-        k0 = k0 * (1 + gamma / 6)
+        k0 = k0 * (1 + gamma * kernel(0))
 
         curIdx2 = 0
         prodIdx1 = 0

From c33611e28564c8444f151fff31179c2059040f6e Mon Sep 17 00:00:00 2001
From: Anders Pride <apride@hawk.illinoistech.edu>
Date: Tue, 9 Jun 2026 16:54:31 -0500
Subject: [PATCH 06/29] Updated demo to correct coord. weight Kuo vector

---
 .../kuo.lattice-39102-1024-1048576.3600.txt   |  0
 demos/lattice_kronecker_methods.ipynb         | 41 +++++++++++--------
 2 files changed, 23 insertions(+), 18 deletions(-)
 rename {qmcpy/discrete_distribution/lattice/generating_vectors => demos}/kuo.lattice-39102-1024-1048576.3600.txt (100%)

diff --git a/qmcpy/discrete_distribution/lattice/generating_vectors/kuo.lattice-39102-1024-1048576.3600.txt b/demos/kuo.lattice-39102-1024-1048576.3600.txt
similarity index 100%
rename from qmcpy/discrete_distribution/lattice/generating_vectors/kuo.lattice-39102-1024-1048576.3600.txt
rename to demos/kuo.lattice-39102-1024-1048576.3600.txt
diff --git a/demos/lattice_kronecker_methods.ipynb b/demos/lattice_kronecker_methods.ipynb
index 85be78d1a..5bfde07ea 100644
--- a/demos/lattice_kronecker_methods.ipynb
+++ b/demos/lattice_kronecker_methods.ipynb
@@ -179,25 +179,30 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "FileNotFoundError",
-     "evalue": "qmcpy\\discrete_distribution\\lattice\\generating_vectors\\kuo.lattice-39102-1024-1048576.3600.txt not found.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m lat1 = Lattice(dimension=dim, order=\u001b[33m\"\u001b[39m\u001b[33mRADICAL_INVERSE\u001b[39m\u001b[33m\"\u001b[39m, seed=\u001b[32m12\u001b[39m, generating_vector=\u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mloadtxt\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mqmcpy\u001b[39;49m\u001b[33;43m\\\u001b[39;49m\u001b[33;43mdiscrete_distribution\u001b[39;49m\u001b[33;43m\\\u001b[39;49m\u001b[33;43mlattice\u001b[39;49m\u001b[33;43m\\\u001b[39;49m\u001b[33;43mgenerating_vectors\u001b[39;49m\u001b[33;43m\\\u001b[39;49m\u001b[33;43mkuo.lattice-39102-1024-1048576.3600.txt\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m, m_max=\u001b[32m20\u001b[39m) \u001b[38;5;66;03m# initialize a lattice with the generating vector of all 1s\u001b[39;00m\n\u001b[32m      2\u001b[39m lat_discs1 = lat1.expected_squared_periodic_discrepancies(n_max=n, coord_weights=coord_weights)\n\u001b[32m      5\u001b[39m lat2 = Lattice(dimension=dim, order=\u001b[33m\"\u001b[39m\u001b[33mRADICAL_INVERSE\u001b[39m\u001b[33m\"\u001b[39m, seed=\u001b[32m12\u001b[39m, generating_vector=np.uint64(searched_lattice_vector), m_max=\u001b[32m20\u001b[39m) \u001b[38;5;66;03m# initialize a lattice with the searched lattice vector\u001b[39;00m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\headw\\miniconda3\\envs\\qmcpy\\Lib\\site-packages\\numpy\\lib\\_npyio_impl.py:1397\u001b[39m, in \u001b[36mloadtxt\u001b[39m\u001b[34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin, encoding, max_rows, quotechar, like)\u001b[39m\n\u001b[32m   1394\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(delimiter, \u001b[38;5;28mbytes\u001b[39m):\n\u001b[32m   1395\u001b[39m     delimiter = delimiter.decode(\u001b[33m'\u001b[39m\u001b[33mlatin1\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m1397\u001b[39m arr = \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcomment\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcomment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdelimiter\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdelimiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1398\u001b[39m \u001b[43m            \u001b[49m\u001b[43mconverters\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconverters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskiplines\u001b[49m\u001b[43m=\u001b[49m\u001b[43mskiprows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[43m=\u001b[49m\u001b[43musecols\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1399\u001b[39m \u001b[43m            \u001b[49m\u001b[43munpack\u001b[49m\u001b[43m=\u001b[49m\u001b[43munpack\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mndmin\u001b[49m\u001b[43m=\u001b[49m\u001b[43mndmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1400\u001b[39m \u001b[43m            \u001b[49m\u001b[43mmax_rows\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmax_rows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquote\u001b[49m\u001b[43m=\u001b[49m\u001b[43mquotechar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1402\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m arr\n",
-      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\headw\\miniconda3\\envs\\qmcpy\\Lib\\site-packages\\numpy\\lib\\_npyio_impl.py:1024\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(fname, delimiter, comment, quote, imaginary_unit, usecols, skiplines, max_rows, converters, ndmin, unpack, dtype, encoding)\u001b[39m\n\u001b[32m   1022\u001b[39m     fname = os.fspath(fname)\n\u001b[32m   1023\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fname, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m-> \u001b[39m\u001b[32m1024\u001b[39m     fh = \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_datasource\u001b[49m\u001b[43m.\u001b[49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mrt\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1025\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m encoding \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m   1026\u001b[39m         encoding = \u001b[38;5;28mgetattr\u001b[39m(fh, \u001b[33m'\u001b[39m\u001b[33mencoding\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mlatin1\u001b[39m\u001b[33m'\u001b[39m)\n",
-      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\headw\\miniconda3\\envs\\qmcpy\\Lib\\site-packages\\numpy\\lib\\_datasource.py:192\u001b[39m, in \u001b[36mopen\u001b[39m\u001b[34m(path, mode, destpath, encoding, newline)\u001b[39m\n\u001b[32m    155\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    156\u001b[39m \u001b[33;03mOpen `path` with `mode` and return the file object.\u001b[39;00m\n\u001b[32m    157\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m    188\u001b[39m \n\u001b[32m    189\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    191\u001b[39m ds = DataSource(destpath)\n\u001b[32m--> \u001b[39m\u001b[32m192\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mds\u001b[49m\u001b[43m.\u001b[49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\headw\\miniconda3\\envs\\qmcpy\\Lib\\site-packages\\numpy\\lib\\_datasource.py:529\u001b[39m, in \u001b[36mDataSource.open\u001b[39m\u001b[34m(self, path, mode, encoding, newline)\u001b[39m\n\u001b[32m    526\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m _file_openers[ext](found, mode=mode,\n\u001b[32m    527\u001b[39m                               encoding=encoding, newline=newline)\n\u001b[32m    528\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m529\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m not found.\u001b[39m\u001b[33m\"\u001b[39m)\n",
-      "\u001b[31mFileNotFoundError\u001b[39m: qmcpy\\discrete_distribution\\lattice\\generating_vectors\\kuo.lattice-39102-1024-1048576.3600.txt not found."
-     ]
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Periodic Discrepancy')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "\n",
-    "# lat1 = Lattice(dimension=dim, order=\"RADICAL_INVERSE\", seed=12, generating_vector=np.loadtxt(\"qmcpy\\discrete_distribution\\lattice\\generating_vectors\\kuo.lattice-39102-1024-1048576.3600.txt\"), m_max=20) # initialize a lattice with the generating vector of all 1s\n",
-    "# lat_discs1 = lat1.expected_squared_periodic_discrepancies(n_max=n, coord_weights=coord_weights)\n",
+    "gen_vec = np.loadtxt(\"kuo.lattice-39102-1024-1048576.3600.txt\",dtype=np.uint64) \n",
+    "lat1 = Lattice(dimension=dim, order=\"RADICAL_INVERSE\", seed=12, generating_vector=gen_vec[:,1], m_max=20) # initialize a lattice with the generating vector of all 1s\n",
+    "lat_discs1 = lat1.expected_squared_periodic_discrepancies(n_max=n, coord_weights=coord_weights)\n",
     "\n",
     "\n",
     "lat2 = Lattice(dimension=dim, order=\"RADICAL_INVERSE\", seed=12, generating_vector=np.uint64(searched_lattice_vector), m_max=20) # initialize a lattice with the searched lattice vector\n",
@@ -205,8 +210,8 @@
     "\n",
     "\n",
     "fig, ax = pyplot.subplots(nrows=1, ncols=1, figsize=(12,10))\n",
-    "ax.plot(np.arange(1, n+1), np.sqrt(lat_discs), label=\"Old Lattice Discrepancy\")\n",
-    "ax.plot(np.arange(1, n+1), np.sqrt(lat_discs2), label=\"Searched Lattice Discrepancy\")\n",
+    "ax.plot(np.arange(1, n+1), np.sqrt(lat_discs1), label=\"Kuo Lattice Discrepancy\")\n",
+    "ax.plot(np.arange(1, n+1), np.sqrt(lat_discs2), label=\"New Lattice Discrepancy\")\n",
     "# ax.plot(np.arange(1, n+1), np.sqrt(kron_discs), label=\"Kronecker Discrepancy\")\n",
     "ax.set_xscale(\"log\")\n",
     "ax.set_yscale(\"log\")\n",

From 88648606c48494c1bd6b4c72953ab92a69d67023 Mon Sep 17 00:00:00 2001
From: Anders Pride <apride@hawk.illinoistech.edu>
Date: Tue, 9 Jun 2026 17:11:24 -0500
Subject: [PATCH 07/29] Added new generating vector option for Kronecker

---
 .../kron_vector_d-100_N-2exp20_2026_06_01.txt | 100 ++++++++++++++++
 .../kronecker/kronecker.py                    | 113 +++++++++++++++++-
 2 files changed, 212 insertions(+), 1 deletion(-)
 create mode 100644 qmcpy/discrete_distribution/kronecker/generating_vectors/kron_vector_d-100_N-2exp20_2026_06_01.txt

diff --git a/qmcpy/discrete_distribution/kronecker/generating_vectors/kron_vector_d-100_N-2exp20_2026_06_01.txt b/qmcpy/discrete_distribution/kronecker/generating_vectors/kron_vector_d-100_N-2exp20_2026_06_01.txt
new file mode 100644
index 000000000..098e8d858
--- /dev/null
+++ b/qmcpy/discrete_distribution/kronecker/generating_vectors/kron_vector_d-100_N-2exp20_2026_06_01.txt
@@ -0,0 +1,100 @@
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+0.752129792595509
+0.337431120990153
+0.542476014178907
+0.307279789725491
diff --git a/qmcpy/discrete_distribution/kronecker/kronecker.py b/qmcpy/discrete_distribution/kronecker/kronecker.py
index 3310daeba..b78c75b04 100644
--- a/qmcpy/discrete_distribution/kronecker/kronecker.py
+++ b/qmcpy/discrete_distribution/kronecker/kronecker.py
@@ -284,7 +284,118 @@ def __init__(self,
             gen_vec = _richtmyer_generating_vector(self.dvec.max()+1)
         elif isinstance(generating_vector, str) and generating_vector.lower() == "suzuki":
             self.gen_vec_source = "SUZUKI"
-            gen_vec = _suzuki_generating_vector(self.dvec.max()+1)        
+            gen_vec = _suzuki_generating_vector(self.dvec.max()+1)       
+        elif isinstance(generating_vector, str) and generating_vector.lower() == "anders_cbc":
+            self.gen_vec_source = "ANDERS_CBC"
+            ANDERS_CBC = np.array([0.618033988749895,
+                0.3173225474723,
+                0.59332263014446,
+                0.20776441643926,
+                0.27373719258623,
+                0.649734278361753,
+                0.478954018631769,
+                0.86866022435182,
+                0.22845082022244,
+                0.581365429377986,
+                0.282365231829842,
+                0.0822850909119904,
+                0.223849641007295,
+                0.5770772201756,
+                0.51769659336634,
+                0.568025390904592,
+                0.156782234569368,
+                0.82246227056154,
+                0.805675312097409,
+                0.63877102813393,
+                0.358300563495856,
+                0.241741343018598,
+                0.705003192174204,
+                0.1931911954956,
+                0.261022001488623,
+                0.897938992038015,
+                0.46839743115877,
+                0.884022067965329,
+                0.752352896871505,
+                0.1601583600427,
+                0.10727599509739,
+                0.151478435512877,
+                0.163863657127101,
+                0.948303450359399,
+                0.80350943597439,
+                0.426371623468333,
+                0.435930910910882,
+                0.21329852459791,
+                0.661698149534002,
+                0.900679822160453,
+                0.122436710671457,
+                0.483663584095611,
+                0.928181067731583,
+                0.443143014606576,
+                0.74491332336194,
+                0.87948409225588,
+                0.0428242449803,
+                0.534576896789579,
+                0.24340042100879,
+                0.30424418245585,
+                0.574003104342617,
+                0.897289023268963,
+                0.541424476559586,
+                0.356895660350464,
+                0.507567280910795,
+                0.513983550428507,
+                0.0610821922457415,
+                0.183871471606587,
+                0.446015178033969,
+                0.455684287415085,
+                0.280817534817491,
+                0.115220095666085,
+                0.433740673279323,
+                0.515605957977756,
+                0.113076735656464,
+                0.733928297688305,
+                0.0597515651584137,
+                0.422268695684775,
+                0.0979181139173599,
+                0.213699261322352,
+                0.866811679881922,
+                0.0878569329036737,
+                0.678412735893121,
+                0.181093969536107,
+                0.128913741473518,
+                0.109341703717108,
+                0.289067270578427,
+                0.352218331663839,
+                0.303605902333137,
+                0.0613899204730832,
+                0.959535877660851,
+                0.475508309069064,
+                0.688698902674194,
+                0.657037932118495,
+                0.645555897563869,
+                0.720658665263604,
+                0.914423387894897,
+                0.425763295044487,
+                0.328825255006553,
+                0.892452975558004,
+                0.16973367306396,
+                0.912292406867098,
+                0.0923260018966512,
+                0.216301713289429,
+                0.147861410064151,
+                0.8600781655845,
+                0.752129792595509,
+                0.337431120990153,
+                0.542476014178907,
+                0.307279789725491], dtype=np.float64)
+            gen_vec = ANDERS_CBC
+            if not (self.dvec.max() < len(gen_vec)):
+                if warn:
+                    warnings.warn(
+                        f"CBC generating vector only supports dimension <= {len(CBC)}; falling back to Richtmyer.",
+                        RuntimeWarning,
+                    )
+                self.gen_vec_source = "RICHTMYER"
+                gen_vec = _richtmyer_generating_vector(self.dvec.max()+1)
         else:
             self.gen_vec_source = "CUSTOM"
             gen_vec = np.asarray(generating_vector, dtype=float)

From bc3d3b674494d1a87416804562f40104da54ca2e Mon Sep 17 00:00:00 2001
From: Anders Pride <apride@hawk.illinoistech.edu>
Date: Wed, 10 Jun 2026 09:29:49 -0500
Subject: [PATCH 08/29] Corrected Kronecker discrepancy calculations

---
 demos/lattice_kronecker_methods.ipynb         | 23 ++++++++++++++-----
 .../kronecker/kronecker.py                    |  5 +++-
 .../discrete_distribution/lattice/lattice.py  |  2 +-
 3 files changed, 22 insertions(+), 8 deletions(-)

diff --git a/demos/lattice_kronecker_methods.ipynb b/demos/lattice_kronecker_methods.ipynb
index 5bfde07ea..52a450e0d 100644
--- a/demos/lattice_kronecker_methods.ipynb
+++ b/demos/lattice_kronecker_methods.ipynb
@@ -66,26 +66,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "id": "c8eec507",
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\headw\\QMCSoftware\\qmcpy\\discrete_distribution\\kronecker\\kronecker.py:276: RuntimeWarning: CBC generating vector only supports dimension <= 13; falling back to Richtmyer.\n",
-      "  warnings.warn(\n"
+      "191.36069122430635\n"
      ]
     }
    ],
    "source": [
-    "kron = Kronecker(dimension=dim, seed=12) # initialize a Kronecker sequence as usual\n",
+    "kron = Kronecker(dimension=dim, seed=12, generating_vector=\"ANDERS_CBC\") # initialize a Kronecker sequence as usual\n",
     "kron_k_tilde = (lambda x, gamma: np.prod(1 + (x * (x - 1) + 1/6) * gamma, axis=-1), 1) # define the kernel function (in this case, the second Bernoulli polynomial)\n",
     "\n",
     "kron_discs = kron._square_periodic_discrepancies(n = n, k_tilde = kron_k_tilde, gamma = coord_weights) # compute the expected squared periodic discrepancies for n = 1, 2, ...\n",
     "\n",
-    "kron_wssd = kron.wssd_discrepancy(n = n, weights = sample_weights, k_tilde = kron_k_tilde, gamma = coord_weights) # compute the wssd"
+    "kron_wssd = kron.wssd_discrepancy(n = n, weights = sample_weights, k_tilde = kron_k_tilde, gamma = coord_weights) # compute the wssd\n",
+    "print(kron_wssd[-1])"
    ]
   },
   {
@@ -197,6 +197,17 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
+      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
+      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
     }
    ],
    "source": [
diff --git a/qmcpy/discrete_distribution/kronecker/kronecker.py b/qmcpy/discrete_distribution/kronecker/kronecker.py
index b78c75b04..23a40bf31 100644
--- a/qmcpy/discrete_distribution/kronecker/kronecker.py
+++ b/qmcpy/discrete_distribution/kronecker/kronecker.py
@@ -474,7 +474,10 @@ def wssd_discrepancy(self, n, weights, k_tilde = None, gamma = None):
     
     def _square_periodic_discrepancies(self, n, k_tilde, gamma):
         n_array = np.arange(1, n + 1)
-        k_tilde_terms = k_tilde[0](self.gen_samples(n=n), gamma)
+        # we need the points without a random shift for the calculation, so we can't use self._gen_samples
+        i = np.arange(0, n)
+        points = (i[:,None] * self.gen_vec[:,None,:]) % 1
+        k_tilde_terms = k_tilde[0](points, gamma)
 
         left_sum = np.cumsum(k_tilde_terms[...,1:], axis=-1) * n_array[1:]
         right_sum = np.cumsum(n_array[:-1] * k_tilde_terms[...,1:], axis=-1)
diff --git a/qmcpy/discrete_distribution/lattice/lattice.py b/qmcpy/discrete_distribution/lattice/lattice.py
index 1ad05069c..3018a06bf 100644
--- a/qmcpy/discrete_distribution/lattice/lattice.py
+++ b/qmcpy/discrete_distribution/lattice/lattice.py
@@ -440,7 +440,7 @@ def expected_squared_periodic_discrepancies(self, n_max, coord_weights=None, ker
         for i in range(n_max):
             freq_mtx[:,i] = freq_mtx[:,i] * (i + 1)**(-2)
 
-        # multiply by the precomputed frequency matrix and add the constant vector
+        # multiply by the frequency matrix and add the constant vector
         discs = k_const + np.vecmat(k_sum, freq_mtx)
         return discs
 

From 82d8df4cb219985a7dfee54c61b5f69358512d70 Mon Sep 17 00:00:00 2001
From: Anders Pride <apride@hawk.illinoistech.edu>
Date: Thu, 9 Jul 2026 09:17:55 -0500
Subject: [PATCH 09/29] Final version before pull request

---
 demos/lattice_kronecker_methods.ipynb | 52 ++++++++++++++-------------
 1 file changed, 27 insertions(+), 25 deletions(-)

diff --git a/demos/lattice_kronecker_methods.ipynb b/demos/lattice_kronecker_methods.ipynb
index 52a450e0d..384db68ee 100644
--- a/demos/lattice_kronecker_methods.ipynb
+++ b/demos/lattice_kronecker_methods.ipynb
@@ -40,12 +40,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 17,
    "id": "958e16e1",
    "metadata": {},
    "outputs": [],
    "source": [
-    "dim = 50\n",
+    "dim = 64\n",
     "n = 2**20\n",
     "lat = Lattice(dimension=dim, order=\"RADICAL_INVERSE\", seed=12) # initialize a lattice as usual\n",
     "coord_weights = np.array([j**(-2) for j in range(1, dim + 1)]) # define some coordinate weights\n",
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 21,
    "id": "c8eec507",
    "metadata": {},
    "outputs": [
@@ -74,18 +74,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "191.36069122430635\n"
+      "193.7028792871213\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\headw\\AppData\\Local\\Temp\\ipykernel_26420\\204085947.py:9: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  kron_wssd = np.float64(kron.wssd_discrepancy(n = n, weights = sample_weights, k_tilde = kron_k_tilde, gamma = coord_weights)) # compute the wssd\n"
      ]
     }
    ],
    "source": [
+    "dim = 64\n",
+    "coord_weights = np.array([j**(-2) for j in range(1, dim + 1)])\n",
+    "sample_weights = np.arange(1, n+1) # define some sample weights\n",
     "kron = Kronecker(dimension=dim, seed=12, generating_vector=\"ANDERS_CBC\") # initialize a Kronecker sequence as usual\n",
     "kron_k_tilde = (lambda x, gamma: np.prod(1 + (x * (x - 1) + 1/6) * gamma, axis=-1), 1) # define the kernel function (in this case, the second Bernoulli polynomial)\n",
     "\n",
-    "kron_discs = kron._square_periodic_discrepancies(n = n, k_tilde = kron_k_tilde, gamma = coord_weights) # compute the expected squared periodic discrepancies for n = 1, 2, ...\n",
+    "kron_discs = kron._square_periodic_discrepancies(n = n, k_tilde = kron_k_tilde, gamma = coord_weights).reshape(-1) # compute the expected squared periodic discrepancies for n = 1, 2, ...\n",
     "\n",
-    "kron_wssd = kron.wssd_discrepancy(n = n, weights = sample_weights, k_tilde = kron_k_tilde, gamma = coord_weights) # compute the wssd\n",
-    "print(kron_wssd[-1])"
+    "kron_wssd = np.float64(kron.wssd_discrepancy(n = n, weights = sample_weights, k_tilde = kron_k_tilde, gamma = coord_weights)) # compute the wssd\n",
+    "print(kron_wssd)"
    ]
   },
   {
@@ -106,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 19,
    "id": "524e3b99",
    "metadata": {},
    "outputs": [
@@ -114,12 +125,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken for lattice vector wssd search:  8.519208669662476\n",
+      "Time taken for lattice vector wssd search:  9.876056909561157\n",
       "Searched lattice vector:  [     1 444567 406809  53917 411513 111013  57773  23363 278133 179399\n",
       " 145725 480145 365723 134199 361515 297163 315729 250703 322429 236947\n",
       " 508553 455183 148433  37975  46187 474063 490317  14811 417263 342641\n",
       " 474417 269197 309749  29993 366775 433399 240621 375377  84847 232327\n",
-      " 214987 375079  32109 388283 153487 140919 390453 362317 413527 405689]\n"
+      " 214987 375079  32109 388283 153487 140919 390453 362317 413527 405689\n",
+      " 307801  95733 176459 498361 147739 178349 300557 427387 162217 127697\n",
+      " 183267 336879 314911 122203]\n"
      ]
     }
    ],
@@ -174,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "9f66d72b",
    "metadata": {},
    "outputs": [
@@ -184,30 +197,19 @@
        "Text(0, 0.5, 'Periodic Discrepancy')"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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I7JE2IwHMAwST0JqeYzM64OTJk87PJSQk6OGHH7YSTTN6wAyXv/g7pBeb8cILL1ixmUT1tttuU3R0dLZ+bhl9X/N9du/erfvvv9/ZMz5//nzdcsstVo94Ylvid7m4N998pzvuuMO6fkBAgBo3bqxffvnFeXzBggW65JJLVKxYMYWEhOiee+7R2bNn043Z3M/8/CpXrqyGDRtaPweT2J85c8b6+aY15P6tt95S3bp1rThMPNdee63L7+bFF19UnTp1rNEdZpTHc8895zLK4ZtvvlHnzp2tz3/xxRfWsffff9+KwbQ1aNDAukeixM99/fXX6tChg/P7//nnn85z4uPjrfhr1qxp/Qzq16+v119/XckNGzZMffv21UsvvWR957Jly1rLmSd/qHDhwgWNHTvW+hma+M33+OCDD2S3261t89nkzIgNE9u2bduUG0joC5HgshVVc8xsrQjsIj9bvFqvHKdFnzwie0KCp0MDAACAO+x2KeZs3r/Mfd1kklHzmj59upXcpGXgwIE6cuSIfv31V61YsUItW7ZU9+7ddeKEo5CzSQp79+6tuXPnWr39V1xxhfr06aM9e/a4XMckSM2aNbPOefzxx60EyVwnLCxMixYtshJV8zmTsCUyIwdKlCihJUuWWMmjGSY+e/Zst2P73//+ZyXOzz//vJYvX24ld8kTyKzI6PuaYevVqlWzYjW94uZlklOTtCf2lJvXgw8+mOLaJknu1auX9aDj888/18aNG60HEmaofOLDBHM/M7Ji7dq1VsJsfm533XVXpr9HhQoVdOONN1qjMpL/zBOZn5d5WGC+x+bNmzVr1ixdeumlzuPmwY+JzfwuTZxffvmllfQnN27cON17773atGmT9bDGJPVPPPGElfibNvN7MZ//5JNPXD730EMP6YEHHrB+vu3bt7d+vomjRszPyPx8v/32W+u+5nqPPPKI9btObt68edbPy7yb65upB+aVyDxc+uqrrzRlyhQrlnfeecf6/8Ek7bfeeqs++ugjl+uZffP9TbKfK+xIV2RkpPnTzXovKOLj4uyL3rrDbn8yyHotnnKzPTbmgqfDAgAAQDLnz5+3b9y40Xp3unDG+W+4PH2Z+2bCd999Zy9durQ9ICDA3qFDB/v48ePta9ascR7/+++/7UFBQfbo6GiXz9WuXdv+zjvvpHndRo0a2d944w3nfo0aNex9+/Z1Oef666+3d+zYMc1rdO7c2d6pUyeXtvDwcPvYsWPdjq19+/b2UaNGuRxv27atvVmzZmned+fOnVbesGrVKru7Uvu+r776qss5H330kT04ODjFZ5Of+9tvv9m9vLzsmzdvTvU+t912m/322293aTM/B/MZl//+3Liv8fbbb1vf9fDhw86f+b333mttf//999bPNyoqKsXnTJu/v7/9vffeS/dn+Nprr6X43Xz55Zcubc8884z1e0r+uRdeeMF5PDY21l6tWjX7pEmT7GkZPXq0fcCAAc79oUOHWj/XuLg4Z9vAgQPtgwcPtrbNz9fcZ/bs2aleb//+/XZvb2/7kiVLrP2YmBh7uXLl7B9//LH7fwZkMg+lhz4NERER1lM/MxSmoPHy9la7kVO1uP5YJdhtanv8R2145SqdPX3K06EBAACgEDA9vWYOtemlNT2/Zmi46eVO7Mk0w9dNj7QZspzYo29eO3futHo/DXPc9DabYdSm6Js5bno8L+6hb926tct+Yg99epo2beqyb3rYTY+8u7GZOEwBuORMj292uPt9s8L8TEzvc7169VI9br6z+d0k/76m59v0WpvvnVlmeLlheqUvdtlll1nTMGrVqqWbb77Z6l0/d+6cdcx8XzOqI6PfX/LfuZkWYH4vZrh88vifffZZ5+8rtd+Rj4+PdR1zz+Q5XqtWraypFuYa7777boqff6NGjZwjGy7+b8f8nM0xMx0gNVWqVNGVV16pDz/80Nr/+eefre9rRoTkFp9cu3IBZ+ZKmFdUVJSCg4NVELW7/hGt/C1EYf/cr2bnl2jra91VesR0lasU4unQAAAAkBrf4tIjBzxz30wy85RN8mZeZvizmZP+5JNPWvOQTfJqEiGT6F8ssWK7SW7NMHgzpN4MRzbzms1c64sL35mh88mZ8zL8Or6+Lvsm8TTJq+FObLnB3e+bFRn9TMx3NvPrzVD4i5k57JllkmQzDcA8FLmYqTmwcuVK6+f7+++/W0PbzfQFU5Xfnd/dxb9zE7vx3nvvpXjI4p0s8c6ImV9vfgcvv/yylfibOCdPnmxNy3D3vx134jf/H5gHGa+++qo13H7w4MG5WryQhL6Qa9nzZv1bprIqzhimuvHbdOCdbtp93beqUT//rKEJAACA/5geTz/XBLagMKNbzbx6w/TWHzp0yOolNcXbUmPme5vkv1+/fs7EzRQ3y4jpfTfz0J966qksxelObKYX3SR6Zr50osWLFys73Pm+fn5+Kealp9aW2s/EVKI3Kw6k1ktvvrOZN54T87hNb7WZ926Kx3l5pT7g2/xse/ToYb3MQx7zoOSPP/6wagiYpNj8/kzi6w4zv970fO/YscOau5+exYsXO+frx8XFWfUREusEmJ+/qUkwatQo5/kX9/BnpEmTJlZyb4rtme+WGvMdzQOJt99+26of8Ndffyk3kdAXAQ3Ce2hv6V919otrVc1+SJFf9damXh+pYdueng4NAAAABYwpMmaGEJsCYCaRND2dphCaKT5nqrgbJtkxvaAm6TPtJsk0Q/RnzJhhJbRmKLSpgm4KwZnCZaYX1PTyJ/aEpscUVTOJlUnM7rzzTivhNQXMTEzlypXL8PPuxGYKspnk22x37NjRGja+YcMGaxh5RkwhuIuZYdzufF/zgMEkgNddd51VQd18H9Nmkn+TBJvigKa39+IeXzME3CSyZirEK6+8YiXu//77r3UfMyXCVGVv166dldyaRNoknCbBNyMG3nzzzXSH1puHH+bdVNE3RQhNQTozgtkUtkuNqaxvkm8Tj1mBYObMmdb3NFXlzagOE4upkG9+b+Zne/ToUetna4bUp8U8vDGjC8x9zfcxw9jNf3MnT57UmDFjXIbUm5+zeSBjesjNcfPfqWHazZJ7v/32m1Xp/rPPPrNGDZhtd5nfxdChQ61rmqJ45vdhViYwDzkGDRrkHDVg/tsx/52ae2Z3qkZGmENfRITUaaLiI//QZp/6CtZZ1Zp5o1bMdK3ACAAAAGTEzD02Q59NwmSSNrM8mElOR4wY4UwOTSJpEjlz3Cy7ZpJmk6Sa5CexorlJPE3CZ3pNTZJr5nSbnuSMmGuZodxmXrhZLs8kTD/++KPVK+wOd2Izw6TNdzKJp5lzbY6NHDnSreuba7Vo0cLldfjwYbe+r6kMb3rta9eubc3zNsz55sGFicm0mYcQqfn++++t+l/XX3+9NVrCxJ7Ys28evJheZdODb5auMzGZofCm5zs9ZvqxmZ5QtWpV6+dsKrqbhNZUkTftqTG98ebBhVkK0CTWU6dOtarCm4cahvm5mkr05v7muPleiXPU02IeQphl68wQdvMwxzzAMDUBal6UjJuHDOZlEm1Txd/UeEh8yGOmHPTv39+6n/nv1zyYSt5b7y7T826mSpjPmuXzzH/3Fy//Zx5OmKkU5r+v3GYzlfFy/S4FWOIcerP2o5knUtCdP3ta/0YMUotz/1gF85bWu19tr39ctjSGywAAACB3mHXNTUEyk5SYnksAWbNr1y7r/yPzoKF5c89PLf7777+twn979+5NsSSfu38GuJuHksUVMcVKlFTTMT9rSbn+8rLZ1W7rK1ry9u2Kj4vzdGgAAAAAUGBduHDBqmVgigCaKSDpJfM5hYS+CPL28VGbUR9ocZ37rP12R7/V2levUfQ5RwVJAAAAAEDmmKkFZsk+U28grakROY0h90VsyP3FVsx4X02WjpWfLU7/+jRUxTt+UOnyqc+HAQAAQM5hyD1QtEUz5B7Z1erK4dp2xeeKUgk1iNukM2910/4dGzwdFgAAAAAgAyT0UFj7Xjp53S86qPIKsR9QsU+v0JaV8z0dFgAAQJHAgFmgaLLnwP/7JPSw1GjQUr63z9U279oqoyiF/DhQq2d/6emwAAAACi1fX1/r/dy5c54OBYAHJP6/n/hnQVa4t1gjioRyVWqo2H1/aG3EtWoavUxNFozSkhN71HbwOE+HBgAAUOh4e3tba3YnrsFdvHhxa410AIW/Z/7cuXPW//vmzwDzZ0FWURQvDREREdYrPj5eW7ZsKbRF8VITG3NBq96+VW1O/mLtL6p8k9oOnyKvbPyHBgAAgJTMP8UPHTpkVcUGULSUKlVKlSpVSvVBnrtF8Ujoi3iV+7TYExK0+JPxar97qrW/omQ3NR79hfwDins6NAAAgELHdCLFxsZ6OgwAecQMs0+vZ56EPocU1YQ+0bLpEWq+6nH52uK1wa+Jqt35g4LLlPd0WAAAAABQaLFsHXJEeN/R+rf7RzpjL6ZGMet06s2uOrh7s6fDAgAAAIAij4QeGWpy6TU6MvBHHVEZ1UjYK9+PLte2NQs9HRYAAAAAFGkk9HBLrcZtZR8+Rzu9aqicTqnytP5aO+87T4cFAAAAAEUWCT3cVrFabZW9Z57W+zdXCVu0wuaP0NLvX/N0WAAAAABQJJHQI1OCSpVVvTG/aVnw5fKxJajNuie16IMHrKr4AAAAAIC8Q0KPTPPzD1Dre7/Roqq3WPvt976v5a9fr5gL0Z4ODQAAAACKDBJ6ZInNy0vtR7ymJY2eUJzdS+GRs7T5lSt0OvKEp0MDAAAAgCKBhB7Z0nbgA9rQ+R2ds/uryYVVOvp6Vx3Zv9PTYQEAAABAoUdCj2xr1m2Q9vf7XsdUSrUSdsn+Xnft3LDE02EBAAAAQKFGQo8cUbf5JYoZ+pv2eFVVRR1Xuf9do/ULfvJ0WAAAAABQaJHQI8dUqdlAwaPnaZNvI5W0nVe92cO0/Ke3PR0WAAAAABRKJPTIUcFlK6rmmNlaEdhFfrZ4tV45Tos/foRl7QAAAAAgh5HQI8cFFCuhFvdP0+KK11v77XZFaOmbQxUXG+Pp0AAAAACg0CChT0NERITCwsIUHh7u6VAKJC9vb7UbOVWL649Vgt2mtid+0oZXrtLZ06c8HRoAAAAAFAo2u91u93QQ+VlUVJSCg4MVGRmpoKAgT4dTIK387TOF/XO/Amyx2updR6VH/KBylap7OiwAAAAAKNB5KD30yHUte96sXVd9rZMqqbrx2xTzTncryU+Ij/d0aAAAAABQYJHQI080CO+hMzf9qn22SqpiP6KWi+7Snueaa/kv7yo+Ls7T4QEAAABAgUNCjzwTUqeJSt69QIuq3qLT9mIKTdij1ssf0oHnmmjpD28oNuaCp0MEAAAAgAKDOfQZYA597og8eUwbp09Ww92fq5TOWG0HbBW0N+wONe8zSv4BxT0dIgAAAADk6zyUhD4DJPS560zUSa2f/orq7vhEZRVptR1RGe2oP1zNrr5HxUqU9HSIAAAAAJCnSOhzCAl93jh/9rTW/DRFtTa/rwo6YbUdV7C21hqqxn3HKDCotKdDBAAAAIA8QUKfQ0jo89aF6HNa/fNbCtn4jlU8zzilQG2qcZPC+j6k4NLlPB0iAAAAAOQqEvocQkLvGaZA3qoZ76ry2rcUYj9gtZlCeuurXacGfceqdPnKng4RAAAAAHIFCX0OIaH3LLOk3apZH6rcyjesqvjGObu/1lbqrzr9HlG5StU9HSIAAAAA5CgS+hxCQp8/JMTHa/WcLxW09FXVid9utV2w+2p1+atV45pHVCmkjqdDBAAAAIAcQUKfQ0jo8xd7QoLWzv9OAf+8rPpx/1ptMXZvrS7bW1WvelRVazX0dIgAAAAAkC0k9DmEhD7/JvYbFv4s298vqVHMWqstzu6lVaUuU4Xej6hG/eaeDhEAAAAAsoSEPoeQ0Od/m5b8pth5L6pp9HJrP8Fu0/LSvdRy9Cfy8fXzdHgAAAAAkCt5qFfmLgvkPw3b9lTTcXO15eqftKp4B3nZ7GpzaqaWfTLW06EBAAAAQK4hoUehUa9lZ7V4+FctbznJ2m+79yOt++sHT4cFAAAAALmChB6FTuur79TSMn2snvqqf9yjYwd2ezokAAAAAMhxJPQolJoOn6qdXqEqoygd/vhmaz17AAAAAChMSOhRKAUUD5T3dZ/onN1fjWLWaOkn4zwdEgAAAADkKBJ6FFrV6zXXxlZPWdtt97yv9X//6OmQAAAAACDHkNCjUGt99UgtLX2lNZ++0tx7dOzQHk+HBAAAAAA5goQehV6T4e9op1cNldMpHfqI+fQAAAAACgcSehR6xUqUlNegj6359I0vrNbSTx/xdEgAAAAAkG0k9GmIiIhQWFiYwsPDPR0KckCNBi21ocWT1nab3e9qw8IZng4JAAAAALLFZrfb7dm7ROEWFRWl4OBgRUZGKigoyNPhIJuWvXadwk/9qqMqLdvIBSpXsZqnQwIAAACALOWh9NCjSGk0/B3t8gpReZ3UwQ9vVkJ8vKdDAgAAAIAsIaFHkVI8MFga+LHO2/3U5MJKLfnsUU+HBAAAAABZQkKPIie0YWuta/64td1m51RtXPSrp0MCAAAAgEwjoUeRFH7NXVoW3FPeNrvK/TZKJ47s93RIAAAAAJApJPQokmxeXgob/q52e1VTBZ3Qvg+HMJ8eAAAAQIFCQo8iq0TJUkoY8KGi7b5qGr1cSz5/wtMhAQAAAIDbSOhRpNVs1FZrmz5mbYfveEublvzm6ZAAAAAAwC0k9Cjywvvdo+VBPeRjS1DZX+/UyaMHPR0SAAAAAGSIhB5FnplP33DEB9rjVdWaT7+H+fQAAAAACgASeuC/+fRx/T+y5tM3O79US798ytMhAQAAAEC6SOiB/9Rq3FZrGo+3tltve0P/Lp2duQvERktnjkgx53InQAAAAABIxif5DlDUtRlwv5bvWajWp+eq1Mw7teHEePnFn5Vv7Gn5xkZZ7z4xUfKOjZJPzGl5m+3/Xl7xF6xr2P0CZRv0iVSnh6e/DgAAAIBCzGa32+2eDiI/i4qKUnBwsCIjIxUUFOTpcJAHzkSd1MlXOyjEfiDL14j3DpD30B+l6u1yNDYAAAAAhV+Um3koPfTARQKDSuv4wE+16ccHFBNv1xlbCZ1RCZ22ldBpFddps61iirKb9xKKshfXKXtx6/1MvLdeSZisLlqj+M8HyvuWX6TKzTz9lQAAAAAUQiT0QCpqhIVLYX9l+nPRsfG69b0SKn5onNrEbFbCp/3lddssqVzdXIkTAAAAQNFFUTwgBwX4euvNoZ00IfAJrUsIldf5Y7J/crV0ao+nQwMAAABQyJDQAzmsTAk/vXlLF93t9Zi2JVSR7fQB2T+9xlEBHwAAAAByCAk9kAtqlQ/UpCHdNCz+Ue1NKC/biR3SZ/2k8yc9HRoAAACAQoKEHsglbWuV1ZgBXXRT7HgdsZeSDq+XvhgoXTjj6dAAAAAAFAIk9EAu6t+ymvp2u0Q3xYzXKXsJad8y6esbpNhoT4cGAAAAoIAjoQdy2X096qpR83YaGjNWZ+0B0s4/pe9vk+LjPB0aAAAAgAKMhB7IZTabTS8MaCL/Gm00PPYBXZCv9O8v0o+jpYQET4cHAAAAoIAioQfygL+Pt965uZUOlWmjUTH3KE7e0tqvpV8fJqkHAAAAkCUk9EAeKV3CTx8NC9fKgHYaEzNSCbJJy96TItpIKz+T4mI8HSIAAACAAoSEHshDoeVK6N0hrTXL1kkPxtyh894lpeNbpZ/ukl5vJi2KoAo+AAAAALeQ0AN5LDy0jCYPbKppCZeq9dnX9HWp2xVbvIJ0+oD02yPSa42leROls8c9HSoAAACAfMxmt9vtng4iP4uKilJwcLAiIyMVFBTk6XBQiLz/9w698Ou/ikuwy98Wq6dqrNeA6O/le2qH4wTf4lLLoVKHu6Tgap4OFwAAAEA+y0NJ6DNAQo/ctOvYWb30+2b9svagtR/gLT1Tf4f6nflGPkfWOU7y8pF6Pi+1vcOzwQIAAADIEyT0OYSEHnlh3b5ITZr1rxZsO2btB/p769kmR9Un6mt571kgeftL96ySgqt6OlQAAAAA+SQPZQ49kA80qRasz4e31ee3tVWTqsE6cyFe9y0vo7b779Oh0q2k+AvSn5M8HSYAAACAfISEHshHOtUtpx9Hd9SbN7RQaNniOnY2RqMO9XEcXPW5dGyrp0MEAAAAkE+Q0AP5jJeXTVc1raLZYzrr6WsaaZXqaXZ8S8keL/3xrKfDAwAAAJBPkNAD+ZSvt5eGtA/V5WEV9VLcICXIJm2cLh1Y5enQAAAAAOQDRSKh79evn0qXLq1rr73W06EAmXZH59rabK+unxI6OhrmPu3pkAAAAADkA0Uiob/33nv16aefejoMIEtaVi+tNjXL6OXYAYq3+Ujb/5B2/uXpsAAAAAB4WJFI6Lt06aKSJUt6Ogwgy0Z2rq299or6JqG7o2HOUxIrTgIAAABFmscT+r/++kt9+vRRlSpVZLPZNH369BTnREREKDQ0VAEBAWrbtq2WLl3qkVgBT+lSv7zqVyypVy9co1ivAGn/cmnzTE+HBQAAAKAoJ/Rnz55Vs2bNrKQ9Nd98843GjBmjJ598UitXrrTO7dmzp44cOeI8p3nz5mrcuHGK14EDBzIdz4ULFxQVFeXyAjzNPOy6o3MtHVUpfWbvnTSXPiHe06EBAAAA8BAfeVivXr2sV1peeeUVjRgxQrfccou1P3XqVM2YMUMffvihxo0bZ7WtXr06x+KZOHGinnrqqRy7HpBT+jSropd/36LXTvXSjSXnyP/ov9La/0nNr0/7QzHnHMvd+TPlBAAAAChsPN5Dn56YmBitWLFCPXr0cLZ5eXlZ+4sWLcqVe44fP16RkZHO1969e3PlPkBWlrG7rVNNRamEPlJfR+O856W4CylPPn/KceyletJrTaXDG/M8XgAAAABFOKE/duyY4uPjVbFiRZd2s3/o0CG3r2MeAAwcOFAzZ85UtWrV0n0Y4O/vr6CgIJcXkF8MDg9RcDFfvXa6q6IDKkiRe6QVHyedcOG09Ndk6fWm0p+TpJjT0vkT0hfXSpH7PBk6AAAAgKKU0OeUOXPm6OjRozp37pz27dun9u3bezokIEtK+PtoaPsaipa/PvAe5Gj880XpzFFp4euO3vg/npWiI5VQroF+rj1BRwJCpaj90ufXSudPevorAAAAACgKCX25cuXk7e2tw4cPu7Sb/UqVKnksLsCThnYIVYCvl1493kbnS9aQzh2TXg2TZj/h6I0vW0dnr3pHN/i8rLs31FPfU2N0wrusdHST9PWNUmy0p78CAAAAgMKe0Pv5+alVq1aaO3eusy0hIcHap5cdRVXZQH8Nah2iOPnoQ78bHY3xMVLpUKnv29p93R/qM7+SFu+KVKC/j076VtQN5x7SOVtxafdC6YfbqY4PAAAAFAIeT+jPnDljValPrFS/c+dOa3vPnj3Wvlmy7r333tMnn3yiTZs2aeTIkdZSd4lV73OLWUYvLCxM4eHhuXofICuGd6olL5s0eX8jHej0nJXI667lWl7qCvWbulQ7jp1V1VLF9P3IDvrk1jba41tTt124X7HylTb+KM0aL9ntnv4aAAAAALLBZrd79l/18+fPV9euXVO0Dx06VB9/7Cj29eabb2ry5MlWITyz5vyUKVPUtm3bPInPrEMfHBxsVbynQB7yk7u/WqWf1xzQ1c2qaMr1LfTj6v166Lu1iolLUNNqwXp/SGtVCAqwzl2+64SGfrhUXeMW6E2/NxwX6PGU1Ok+z34JAAAAAFnOQz2e0Od3JPTIr9bvj9RVbyyweurNvPqPFu6y2i8Pq6jXrmuu4n4+Luev2G2S+mUaFPeznvD9zNFY/0op7Bqp/hVSQLAnvgYAAACAi5DQ5xASeuRnN3+wRH9vPebcH3FJTY3r1VDeJstPxco9JzX0g6UaGf+5Rvn85Gy3e/nKVrvrf8l9b6l4mTyJHwAAAEBKJPQ5hIQe+dk/247phveXWAn8U1c30k3tamT4mdV7T1kPAqpc2KHe3kvVy2uJ6nntdx6P8QmU1y0z5VO1WS5HDwAAACA1JPQ5hIQe+d38zUdULtBfjau6P2R+w4FITZm7VVsOn9Hu42dVU/vVy2upBnj/pZpeh7W7fFfVGD09V+MGAAAAkDoS+hxCQo/C7kJcvHYdO6ftR89o45qlGrN1qLxsdl24Y5H8K4d5OjwAAACgyIlyMw/1+LJ1+RXL1qGo8PfxVv1KJdW7SWXdc10f/e3dxmrf/dNET4cGAAAAIB0k9GkYPXq0Nm7cqGXLlnk6FCDP+Pl46UK7e6ztWgdnKPrYbk+HBAAAACANJPQAXHTt3lsrvBrLR/Ha+uMkT4cDAAAAIA0k9ABc+Hp76Uzru63tOnu/07lThz0dEgAAAIBUkNADSKHD5QO1xVZLxXRBG6e/5OlwAAAAAKSChB5ACr4+3jre8i5ru86uL3Xm9ClPhwQAAADgIiT0AFIVfsUQ7bNVVimd0arpU6y2hAS7Fq3fou/eGKt5T12mhf/87ekwAQAAgCLLx9MBAMiffHx9dbTpHaq2ZoLqbv9Y039sqGLrPleX2IVqb4u1ztn8+12KbLpYwYHFPB0uAAAAUOTQQ58G1qEHpKZXjtQxW2lV0nH1XTVcPePmy98WqwPF6ilKgaqvXVrw1QueDhMAAAAokkjo08A69IDk7RegE81HWdsX5KcdVa/R+SG/qcrDS3W07Tir/ZJ972jD5s0ejhQAAAAoemx2u93u6SDys6ioKAUHBysyMlJBQUGeDgfIe3a74nYvlneF+rIVL5PUnpCg3S92UI3oTZrn10WXjpsuby+bJyMFAAAAilQeSg89gPTZbPIJbe+azBteXgq6dooSZFPXmPmaM/NbT0UIAAAAFEkk9ACyrHSdNtoaMsjarrNsgo6cjPJ0SAAAAECRQUIPIFvqXDdJp2zBqm3brwWfPaXo2HhPhwQAAAAUCST0ALLFu0Rpnb70SWv7muMfaOakm/TX2q2eDgsAAAAo9EjoAWRbSJdbta/mQHnb7OofN1MNv++m9998TnuOnfV0aAAAAEChRUIPIPtsNlUb+r7OX/+DjgXUUHlblIYfe1H73+ipfUdPeDo6AAAAoFAioU9DRESEwsLCFB4e7ulQgAKjWP1uKvfgch1rN17R8ld72zpt/uMLT4cFAAAAFEok9GkYPXq0Nm7cqGXLlnk6FKBg8fFTuSvGaWvNm6zdgB2/ezoiAAAAoFAioQeQKyqF97Pem0Qv1/HIM54OBwAAACh0SOgB5IryDTpYy9kF2c5p/eLfUhzfd/KcIs/HeiQ2AAAAoDAgoQeQO7y8tb98J2szZuOvLoc2HYxSt5fn65JJf+j7Fftkt9udx6KiYzVr/SGdjibZBwAAANLjk+5RAMiGwCZ9pLkzVPfUAkXHxivA19tq/3LOEs3welC74ytozLej9Ov6mrqrW139vOaAvlm2V2cuxKlL/fL6+JY2nv4KAAAAQL5FDz2AXFM9/ErFykehtoNas3qZc6h93S3vqq7XfvXwXqX/+T+jNZs2q2/EQn2wYKeVzBvzNx/Vkh3HPfwNAAAAgPyLhB5ArrEFBGlXYAtr+8Sqn6z37+Yt1WCvPxwn+AergW2Pfi7+tGrb9uuOkL1a0Ox3/VXmWV3mtVyTf9vsHI5v3pfvOsFQfAAAAOA/DLkHkKvs9a6QVi5TxYPzFXkuVuXXvCV/W5xOlW+tUte/L33WT5VO7tRc/4eko3K8JE3wPaJLd7fQvM1H1KVeBT350wZ9tni36lYI1E93dVIxP8fwfQAAAKCoooceQK6q0b6/9d40YZPe+OYnXau51n5wr8elMjWl236XKjVxnFy8rNT8Ruu9qu2YLrd66bfo0enrrWTe2HrkjJ6budFzXwgAAADIJ0jo0xAREaGwsDCFh4d7OhSgQPMvX0v7fUPlY0vQTTvHW73zx8q0lK1mZ8cJgRWk4XOlOxdKD26V+r4ltb7NOnS77yyrIv5XS/fIyyaNbFPG9Pnr88V79PuGQ579YgAAAICHkdCnYfTo0dq4caOWLXMU8gKQdVHVu1vvoV6HrffgXk9INlvSCT7+UqXG1lJ3lvDbJC9ftbBtVjPbNiuZ/7bDXo1d30ezK71lJfVjv1+rFbtPKi4+wSPfCQAAAPA0EnoAua5Km37O7YPBzeVbp0v6HyhZSWpyrbX5YrV/9ONVNrVa/biUEKu6pxbqgTL/6OS5WA14+x81f3q27vhsubV+PQAAAFCUkNADyHXBdTvotG+51Hvn09L2Tuut/vE5avL3SCk+RiodarWNjv1YN9STggJ8rGXufttwWP9btjd3vwQAAACQz5DQA8h9Xt4qOfwn6abvVbyBY/h9hqo0l2p0lBLipOhTUtVWjnn21dvLK/asnvd5V6sev0xjr2hgnT599f7c/Q4AAABAPkNCDyBvVGwk1emRuc90uMfxHlxduv5ryT9QuiZC8gmQdsyX98dXaNi5j9TVe63W74/UtiNnrNOPnr6g71fs0/mY+Fz4IgAAAED+YLPb7XZPB5GfRUVFKTg4WJGRkQoKCvJ0OEDRs2uhVL6+VMIxZN+y7H1pxgMupw2JGatmXQbonu51ddWUBdp8+LQ61Smn94e2VoAva9YDAACg8OWhPnkaFQBkVmjHlG3hwyWz7N2exdLqL6Q9i3SV12K9ubq9Av19rGTeWLDtmEZ9sdIalr/1yGlVDi6mVjVK5/13AAAAAHIBPfQZoIceyOd2/i19cpVO2kuq9YW3JC8fxSfYdXO7Gvrf8r26EJe0rJ2Pl01/PNBF1csW92jIAAAAQE7kocyhB1CwVW8vFSuj0rbTauP1r5XMtwkto6eubqR3h7RWcT9vlfDzVqnivopLsOvzJbs9HTEAAACQI0joARRs3j5Sg97WZk+vZVYv/JSGG+X1fjd1LnVcq5+4XOsm9NRL1zazzjG99tGxFMsDAABAwUdCD6Dga9DHeru2xGp9fGUJVfprnHRgpfTjaPl52eXlZVPXBhVUrXQxnToXq59WH9Cni3Zp8DuLtPmQY749AAAAUNCQ0AMo+Gp1kfwCFXjhiDotvlOKj3G0718uLf/Q2vT2summdjWs7Sd/2qAnftygJTtP6LHp60QpEQAAABREJPRpiIiIUFhYmMLDwz0dCoCM+AZIdS9zbJ8+IAWUki550LE/92np9CFrc1DrEPn5eOl8bLy8bJKft5eW7TqpuZuOeDB4AAAAIGuocp8BqtwDBcT676XvbnVs939fatxfer+7dGCV5FtCKh0q1emmd/2HadqqA3qkd0P9s/24pv65XfUqBurXey+1evEBAACAgpKHktBngIQeKCBiz0vfDpPK1pEuf1ay2aRD66TP+ktnk/XA3/GXVNlRIC/yXKwuefEPRUXHqX+Lqhrbq4EqBgV47jsAAAAAYtk6AEWNbzHphm+kns85knmjUhPp/vXS6GVSzUsdbVtnOz8SXNxXD1/RwNqetmq/Ok+epxW7T3gkfAAAACCzSOgBFG4+/lL5elKjfikSesMUyvv2zvZqUjVY0bEJ+mjhLs/ECQAAAGQSCT2AoqHOf0Xz9i2Vzrn2woeHltGTfcKs7QXbjik+wa7FO47ryR/Xa/vRM9p9/Kzu+nKl3pq/zRORAwAAAKnySb0ZAAqZUiFShTDpyEZp+x9Sg6sca9WHtJO8vNQspJRK+vtY69Sv2XdK93+zWgcjo/XV0r3y9bbpbEy8fll7UN0bVFRxP29tOBCpno0qyZY4vB8AAADIY/TQAyg6Epe22zhd+qSP9FEvaYVjnXpfby91qFPW2n5h5r9WMm9y9Zj4BCuZD/B1/HE5ada/Gjh1ke78fKXe/3un574LAAAAijwSegBFb9j9pp8dQ++NZR9K/y32cUnd8tb70l2OIflD24fq/SGtNWlAE31ze3ur7Y9/j+hQVLS1/dLvm7XtyBkPfBEAAACAhB5AUVK9neRX0rHtFyh5+0tHNjjWqpd06X8JfaLB4SHqEVZRg8OrW0PyO9dzHDe99c1DSulCXIKufnOBrolYqPX7I/P++wAAAKBII6EHUHR4+0pthkslKkjXfy2FXe1oX/WZ9Va9bHGFli1ubZsEvmFl1zU/H72yoVpWL6XXBjfX2ze1VEiZYjoXE681e0/pnb925P33AQAAQJFGQg+gaOkxQXpwi1TzEqnFTY62dd9JMeeszb4tqlrvIy6pmeKj9SqW1LRRHXVF48qqHFxMc8d00XtDWlvH/th0WL+uO6jw5+bo88W78/IbAQAAoIiy2e3/TR5FqqKiohQcHKzIyEgFBbn21gEo4BISpCnNpFN7pK6PSZc+qHi7dPT0BVUKDnDrEuaP0I4v/KEDkdHy8/FSTFyC1d6tQQUdOR2tFwc0U1gV/uwAAABAzueh9NADKLq8vKQ2dzi25z0r/XiXvBNi3E7mDbNsXc/Glaxtk8x7e9mcxfPW74/SlLlbcyd2AAAAFHkk9ACKtvajpcuekWxe0urPpaXvZfoSvZtUdm6P79XAet3Ytrq1P2fTYX2xZLdenPWvs/ceAAAAyAk+OXIVACiozGLzHe+R4mOkP56R9i6WdFfK8zbPkoKqSJWbpjjUqnppdahdVudj43VTuxoK8PW22jcciNLqvaf06A/rrf0KJf01rGPKufkAAABAVtBDDwBGNUdxOx3e4Hg/tk1a/qGjWN6Kj6WvBkuf9ZXiLkg7/pR+f0y64FiD3svLpi9HtNMPozo6k/nEZe+Se2v+dkXHxufhlwIAAEBhRg99GiIiIqxXfDz/+AaKhIqNHe8ndjoS9Z/ulvb8I/3zhhS5z3Hs3HFpyyxp5sPSmUNSXIzU+8U0L9m3eVUt2HZMdSsE6tvl+7T/1Hnd89UqPXl1I1UtVSyPvhgAAAAKK6rcZ4Aq90AR8lI96cxhaejP0mf9pYTYpGO+JaTYs1JgJUcyb7FJV74sxUVLLW6WAtL+M+LnNQd091errO0GlUrq13svsQrqGQkJdmdPPwAAABDlZh5KDz0AJKoQ5kjoV37mSOYDK0r1rpDOn5TajJA+6ZOUzPsHSxcipRljHPtnjkiXPZXmpfs0q6LQsiXU/+2F+vfQaWut+p/XHlTbmmX0xZI9alerjF4Z1FznYuJVpoRfHn1hAAAAFGTMoQeARBUbOd43Tne81+ggXT1FGvyZFHqJVLbOfyfapCHTpbJ1pYBgR9P2uY759uZhQHRUqpdvUi1YLUJKW9uP/7hBS3ee0Bt/bNOJszGaue6QGjw+Sx1emKt1+yJz/7sCAACgwCOhB4CL59GbivdG9Q5Jx8zw+KbXObbr95KqtpTuXi7dtdzRdmid9Mt90k93SdNGpHmLDnXKuuz7ersOs4+OTdDwT5dpwdZjzqH4AAAAQGpI6AHg4h76RNXbue53vFfqM0W6+o2ktsAKUoX/Prf2G8e7KZy34DVHdfzYaNdL1CnnMgx/63O99fv9lzrb/H28dDjqgm76YIm1fj0AAACQFubQA0Ci8vUlm7dkj5f8SqZM8H38pFZDU36uVmfpyH/L3SWa86Tj3cy/73S/s7l5SCmV8PPW2Zh49W9R1WqrV7GkXh7YTNFx8apfsaRufH+JLsQl6JlfNunNeds0pH2ormleReVL+svfJ2lZPAAAABRt9NADQCIff6lcXcd2SBvJy83kuWZn1178xKH7xra5Lqf6entpyvUt9GjvhupSv7yzfUCrarqxbQ21Di2juQ84rhcTn2D11k/+bbM6TZqnB79dm62vBwAAgMKFhB4AkqvS0vFe8xL3PxPaUfILlLz9pDZ3SCMXSqOXOY7tXSpF7pcOrnGe3r1hRY24tJZz2bqLVStdXKmtYGeWvnvpt81av5+ieQAAACChBwBX3Z+QrpgktRvl/mf8S0rDZki3zpKCHcPorZ5+s+xd/AXp1TDpnc7SvhVuX/LBnvWt98GtQ6w162uXL2HtmyH4Iz5drpi4hEx+MQAAABQ2JPQAkFxQZandnY7h95lRpblUtVXSvul9N0vdOdmlVZ+5fbnbL6mlj4aF6+m+jdSwcpCGdazpPHYwMlrfLNuTufgAAABQ6JDQA0BuMUPxk9swLUXV+7T4eHupa4MKziJ4A1tV0/BONdXpvyr5Zh37d/7cnvMxAwAAoMAgoQeA3FKvlxQQ7CiaV7KKFB0prfkqS5cK8PXWY1eF6cNh4bosrKLV9uqcLXr2l42atf6g/rd8r6Jj43P4CwAAACA/s9ntdrung8jPoqKiFBwcrMjISAUFBXk6HAAFTVyMZPOS5j8v/f2yo63jfdJlT2X5kiZxbzrhd6sKfnI9GlZU3xZVdGWTymkW3AMAAEDhyUPpoQeA3GTWrvf2kS59WAof7mhbMlVKyHpvuumtbxYSnKJ9zqbDuuvLVfpq6d7sRAwAAIACgoQeAPKCb4DU60XJJ0CKi5Y2/ST9MkY6uUv65w1px/xMXa52+UDn9o+jXefqP/LDOk1buS/HQgcAAED+5OPpAACgyPDylsrXd6xJP+12KT5GWv6B41jxstK9a6SYc1JJxxz59IzuWkcrdp/Uze1rqFlIKb00sJlen7tFe0+ct44/8O0aNaoSrDoVAuWd2qL2AAAAKPCYQ58B5tADyFE/3Jl2YTy/klJCrHT7fKlCwyxd/sTZGLV9fo5i4x1/tLcJLaNn+zVWvYolsxM1AAAA8hBz6AEgP7o4Ua8WnrQdc9oxHH/xW1m+fJkSfro12Zr1S3ed0OWv/qXZGw9n+ZoAAADIn0joASAvVQhL2q7TQxo+R7r8OddzVn4qfTtMOn0oS7doX7tsirYRny7Xn1uOZul6AAAAyJ9I6AHAUz301ds73mt1dh12b2z4QVr6bpZu0alOOQ1tX0NPX9NI34/87x6Shn64VEdOR2cxcAAAAOQ3JPQAkJeCqkoBpRzbNf6rTl+hkaO3vkYnR499ySqO9oNrpQunpUyWOvHx9tJT1zTWkPahahFSWlWCA5zHvlrCknYAAACFBUXxMkBRPAA5bvOv0rGtUoe7JVsqFej3LpU+uOy/HZvUeazUdXzWb3fotCb/9q/mbDoiU/D+g2Hh6lq/QtbjBwAAQL7IQ0noM0BCDyDPXTgjTawmKdkfz48clPyKZ/mScfEJGvLhUv2z/bi137NRRfl6e2lUlzoKq8KfbQAAAPkJVe6zKSIiQmFhYQoPT1aBGgDygn+gFPTfsPtErzR09NxnkRmGf1e3Os793zYc1i9rD6r3lL9191erlJDAs10AAICChoQ+DaNHj9bGjRu1bNkyT4cCoCgyc+eTiz4lzRqXrUs2qRqcavvPaw7otblbxYAtAACAgoWEHgDyoysmOt47jZEaXu3YPrxRSkjI8iVLBvg6t/u3qKqwyknDt6bM3ar/Ld9LTz0AAEABwhz6DDCHHoBHmD+aj22RytZ1zKV/rrIUf0G6Z5VUplaWLztj7UH9sGqfXry2mSLPx+rvrUc1+bfNOh0dZx2vWqqY3rihhVpWL52DXwYAAACZQVG8HEJCDyBfmNpJOrTOsX1NhNTiphy79MJtx3Tj+0tc2mbff6nqViyZY/cAAACA+yiKBwCFSYWwpO0fR2d6bfr0NA8plaLtslf/0uq9p3LsHgAAAMh5JPQAUBCUqu66f2pPjl26hL+PHu3dUNe3qW69J3p+5qYcuwcAAAByHgk9ABQENTu77u91HSJvOX9SOrg2S5cfcWktTezfRL2bVna2Ld15Qit2n8jS9QAAAJD7SOgBoCCoeYl0w/+kRv0c+3sWJR07d0JaFCF9c7P0ziXS5LrS749JJ3ZKcRcydRtTFG/j0z3VuoajKN6Atxfp5d83W7310bHxOfqVAAAAkD0UxcsARfEA5CubfpG+uVHyLSHd+qtUuZn0aV9px7zUzw8oJTUdLHV7VApIfR361Ow4ekbdXv4zRfvUm1rpisaVsvMNAAAAkAGK4gFAYVTvCqlWVyn2rDTnKUcPfFrJvBF9Slr6jrTqi0zdpma5Eqm23/n5Cq3fH5nZqAEAAJALSOgBoCDx9pE6j3Vsb58rPVvB9bjpha/UNOXnfhsvfTHI7er4NpstzWNXvbFA8zYfEQO8AAAAPIuEHgAKmqAqaR+r2FhqfmPSfuglSdtbf0tay94NLw9sprIl/DRtVAfrdX+Pes5jt3y0TB1e+EOx8QmZjx8AAAA5goQeAAqakkmV6C11e0pXvuxo7zVJKp+UeKvfO67nznteSnAvCR/QqppWPH6ZWlYvbb3u6V7H5fjByGgrsY9PoKceAADAE3w8clcAQNb5+EnFy0nnjjn2TeX75tdL4cMd+2YofLfHpHL1pOCqrp/d8qv0+6NSubrSH89JIW2lRn2lpoPcGoZ/VdPK+mXtQWfbgm3HNO/fI+oRVjFnvyMAAAAyRJX7DFDlHkC+NLVT0vD5YTOk0E5pn7t3qeNcU0TvQhoF7cySeHUvN1l7urc9HR2rfSfPa83eUxo3zXH/tjXL6KsR7eTllf5nAQAA4B6q3ANAYRaYbOm4UtXTPzekjRR+m9T/3bTP+XKQtOGHDG9bMsBXDSsHaVDrEI3v1cBqW7LzhGo9MlNdX5qv/y3fqwSG4AMAAOQJEnoAKIi8ks2YKplOkbzkytRM//h3t0hL33Pv9l423dG5tno3SXqwsPPYWT383VpN/Wu7e/EAAAAgW0joAaAgSoh1XcrOHaVqJG3XvFSq3t6xXaVlUvvMB6W137odRqMqwSnaXpy1WbM3Hnb7GgAAAMgaEnoAKIhMwbvM8g1I2vb2lwZ/Id02R+r1out504ZLR7e4dcmKQUnXrJRse8Sny9X62dk6cOp85uMEAACAW6hyDwAFUeeHpfOnpGaDM/e5oKpS1H5HZfwSZR2v6FQK5R1a67r8XRr6NKusv7ceVZuaZXR9eHXN/feIlcwbx87E6OYPlmjWfZfK15vnxwAAADmNKvcZoMo9gELl1F5p7xKpUX8zET6p/aPe0vHtUrXW0r+/ONpu/kGq3S1Tlz9yOlptnpvr0jbmsnq6p3tdnY+Jt47XKFsiR74KAABAUc9DSegzQEIPoEiIj5US4qQlU6U5ExxtwSHS/eszfalXZm/R/pPn9f3Kfc62Z/s21mPTU17ruzvbq1WN0joUFa0LsQkKLUeyDwAAEOVmHsqQewCA5O3reJkh+YnOn8zSpUyPvFG9THG9OscxFz+1ZN64duoilSnhpxNnY5JC8bJp3BUNNPySmrLZWNseAAAgLST0AIAk9XpKpUOlk7ukmDPSniXS2aPS9FFS7Dnp8mek1rdJPn4ZXureHnV18lyMPv5nV7rnJU/mjfgEu56buUmbDkbp723HdPT0BT1zTSO1q1XW6sFnPj4AAIADQ+4zwJB7AEXSp32lHfOkgOCURfNMwn/Dt24VzZu1/qDu/HyltT2gZTXVrRioF379V81DSmnL4dM6FxOf6dCWPtJdMfEJCi7mq5IBvpn+PAAAQH7HkHsAQNZ1fcSR0KdWAd/03keES71fktqMSPcy1cskzYkf37uBygX669K65VWzXAn5eNtU99FfrWNv3tDCen/q541WxfwZaw+mec02z7sW3XttcHP1bZFsqgAAAEARQUIPAEipTK2Mz5n5oBTWVwosn+Yp9SoGWkXvShf3tZJ5I6xK0lPmcb0aaMOBKF0eVkl+Pl66qmkVq/3shaWav/motT26a21FzNue5j3u+2a1vlyyR58Pb2tdAwAAoKhgyH0GGHIPoEhKSJCeLp207x/kGGpv1qdPrmRl6d41ko8jWc8px85csKrl39qxpmqXL6H9p86raqliqjl+ZoafnTOms+pUCMzReAAAAPJjHkpXBgAgpeRr1PsUk+5eIQ37b3365E4flP6dkeO3N735z/drYiXmptJ9tdLFrfcPhra2huT//XBXvXhtU13VtHKKz/Z45U+N/mKleF4NAAAKOxJ6AED6zHJ2gRUcBfJu/F5qdoMUWCnp+MHVeRZK94YV9b872iukTHENah2iN29oqbdubJnivBnrDur2z1bkWVwAAACeQEIPAEhf+fpJ23V7SP3elnq/mNS28HXp2Dbp1F7JA73ivRpX0hfD22rV45epfa2yzvbZGw8rdNwMq6o+AABAYcQc+gwwhx5AkbVjvvT3y9KVr0rl6rgeM3917F8hvd/dtb1SU2noz1KxUvKEmLgEbT96Rr1e/zvFscXju6tScIBH4gIAAMgM5tADALKnVhdHcn5xMm/YbFK11lKz613bTdG8726Vp5gq9w0rB+n6NiEpjrWbOFeR52I9EhcAAEBuIKEHAGRdyWRz6RNtnyttnS1PeuzKMD3Us74+viXcpb3Z079ryIdLrZ58AACAgq7QJ/R79+5Vly5dFBYWpqZNm+rbb7/1dEgAUHiUSGMN+rlPSwnx8pQS/j4a3bWOutSvoF/u7iQfL5vz2F9bjqreY79qw4FIj8UHAACQEwp9Qu/j46PXXntNGzdu1O+//6777rtPZ8+e9XRYAFA4JE/ag6q5Dr2f97zyg8ZVg7X+qZ4p2q+cskAr95z0SEwAAAA5odAn9JUrV1bz5s2t7UqVKqlcuXI6ceKEp8MCgMIhtKPj3ctXumupdN1XScf+fkk6uVv5QYCvt14c0FS3dAx1ae//1j+6+s0FOhcT57HYAAAACmxC/9dff6lPnz6qUqWKbDabpk+fnuKciIgIhYaGKiAgQG3bttXSpUuzdK8VK1YoPj5eISEpiyUBALKgaitp2Azp3tWSXwmp7EUF9Ba/pfxiUHiInuzTSH891FWta5R2tq/dF6mwJ37TpoNRHo0PAACgwCX0Zvh7s2bNrKQ9Nd98843GjBmjJ598UitXrrTO7dmzp44cOeI8x/TAN27cOMXrwIEDznNMr/yQIUP07rvvphvPhQsXrCUCkr8AAOkI7SQF/zfcPriq6zGzNn0+U71scX03skOKdrPU3dp9pzwSEwAAQIFfh9700P/www/q27evs830yIeHh+vNN9+09hMSEqwe9rvvvlvjxo1z67omSb/ssss0YsQI3XzzzemeO2HCBD311FMp2lmHHgDctPZ/0sYfpX9/ceyPWixVaOjYNn/lxMdKpw9IAcFSsaSe8ry2bNcJTVu5X18t3ePS/uDl9ayCeubvJAAAAE8oFOvQx8TEWMPke/To4Wzz8vKy9hctWuTWNczzimHDhqlbt24ZJvPG+PHjrR9a4stUyQcAZELTQdIlDyTtfzvM8b7qC+mpUtKz5aXXm0mTQj3agx8eWkYT+zfRovHd1KhK0l+UL/2+Rde9u9hjcQEAALgrXyf0x44ds+a8V6xY0aXd7B86dMitayxcuNAatm/m5puh+ea1bt26NM/39/e3noAkfwEAMimoStL20X+l49ulH0elPO+1xo4e+x3zpT2eSaIrBxfTjHsucWlbsvOEQsfN8Eg8AAAA7vJRIdepUydrmD4AwEPr0xcrI72fNNIqhdeaOobgG2HXSIM+lSd8OaKt/rdsr6avTqq/Mm3lPvVrUZXh9wAAIF/K1z30Zok5b29vHT582KXd7Jsl6AAA+ZSXt3T/Rsf2+ROOV1oSk3nDzL3//TF5Qofa5fTadS307s2tnG1j/rdGNcfPVGw8D4YBAED+k68Tej8/P7Vq1Upz5851tpnedrPfvn17j8YGAMiAqXhfq2vmP/fPG9Kfk+UplzeqpM9va+vSVvfRX3X2AmvVAwCA/MXjCf2ZM2e0evVq62Xs3LnT2t6zx1F12CxZ99577+mTTz7Rpk2bNHLkSGupu1tuuSVX4zLL6IWFhVkV9gEAWRTimhirUT+p37vSmH+lOxem/bl5z5onuPKU8JqlVbdCoEtbi2dmeyweAACAfLls3fz589W1a8oenKFDh+rjjz+2ts2SdZMnT7YK4ZmidlOmTLGWs8tPywUAAFKx+G1p1n9LjF71mtRqmFmj1LEfHSW9EOLYbnqdtPZr18/2nCi1uV2K2ieVDpUn7D5+Vp0nz3dpW/9UTwX6F/oSNAAAwIPczUM9ntDndyT0AJANq7+Spt/p2L7+a6l+r6Rj5q8fs4yd0f89x5r0Zom7mDOONrNOfXRk0vk3fS/VSae4Xi6Jjo1Xg8dnubStnXC5ggJ88zwWAABQNEQVhnXoAQAFXLFSybZLux4zPfVNBkmlqjsS/bqXSQ9tTzqePJk3Ph8gzX9BeS3A11td6yer2m8GFEz4XTwPBwAAnkZCDwDIPQHJE/oyKY8PeE+6Z43kX9Kx7xsgDfgg7evNnyhFJauKn0deG9xCb97QwqWt06R5Vu89AACAp5DQAwByT2KibhRPJaE3vC76q6jJtY7h9mn5/XHlteDivrqqaRX99VBSzZf9p86nGIoPAACQl0joAQC5JyAo9d76jCQfze5b3PXY+u+kb2+R9i5VXqtetrju6V7XpS103Iw8jwMAAMAgoU8Dy9YBQA4w8+Mvf1bqM0XyzkRleHuyoezj9kq3/uZ6fMM06YPLpOPJ5tznkRGX1FSvxpVc2uZuOpzncQAAAFDlPgNUuQcAD3iushR7zrE9ITJlVfxEXr7SE8fyPj5Jy3ed0LVTFzn337m5lXo2ck30AQAAsoIq9wCAgqvHBMd7+IiktsT165NLiJV2LZQntA4to2f7Nnbu3/HZCsXFJ3gkFgAAUDSR0AMA8p82t0sjF0lXuLFM3ce9pQTPJNK9m1R22W/xzGyPxAEAAIomEnoAQP5jeuMrhqWcdx9U1fHe4mbX9hN5P5feKFPCT5ufvcK5fzo6TkdPX/BILAAAoOghoQcAFBy3/Orote/1omv70nc9FZH8fbz15Yi2zv0PFuz0WCwAAKBoyUTJYQAAPKx0DandSMf2nQukqZ2SEnovH2nxW0nnPn48c5X1s6FD7XJqE1pGS3ed0NQ/t6tSkL+GdayZJ/cGAABFFz30AICCqVIT1/3kybzxv4uG5eeyvi3+mw5gCvP/vFEJCSwiAwAAchcJfRpYhx4ACoBrLkrik9s8U5oQLL3d0bHkXS678qICeb2n/J3r9wQAAEUb69BngHXoASCfMwn74fXpn1OrqzRkep6EEzpuhnP732euUICvd57cFwAAFB6sQw8AKBqCq2V8zo550vZ5eRGNJg1Imgrwz/ZjeXJPAABQNJHQAwAKtuQDzcbtlZ44KfkFpjzvs755Es7g8OrO7Vs/Xq5tR87kyX0BAEDRQ0IPACjY7AlJ2wFBkpdX2nPmL5zOk5Ba1Sjt3O7xyp95ck8AAFD0kNADAAq4VJL3io1SP3Xh69LOv6T4uFyNaOwVDVz2GXoPAAByAwk9AKBgS214ff93pbBrpBHzpNtmJ7X/NVn6pI/0TFkpOjLXQmpTs4z+fKiLc/+G95bk2r0AAEDR5ePpAAAAyJbLnpaObJLajUxqK1NTGvSpYzut4fcvVHfMtzdD9HNB9TLFXfbjE+zy9rLlyr0AAEDRRA89AKBgK11Dumup1PqW1I/bbNLQX1I/tnthroVls9m0dsLlzv2GT8zKtXsBAICiiYQ+DREREQoLC1N4eLinQwEAZFdQldTbP7kqd28b4OvcjolL0Lp9uTfMHwAAFD0k9GkYPXq0Nm7cqGXLlnk6FABAdvkHpX0sNjpXb+3nnfRXbZ83F+TqvQAAQNFCQg8AKPz8SyZtD/jA9dhzFaW9S3Pt1nPGdHbZPxh5PtfuBQAAihYSegBA4efj71ow79HDrsc/uCzXbl29bHE936+Jc//pnzfm2r0AAEDRQkIPACj8TGG8nhOltiOlKi0l34CU5+TiMnaDw0Oc27+uP6TQcTN09PSFXLsfAAAoGkjoAQBFQ/tRUq8XHMl9auY9n7ItIcHxyiazXN2bN7RwaQt/bo6+Xb4329cGAABFFwk9AADGkqmO9/g4afpoR4L/dGnHa8MP2b58nQqBKdoe+m6t1Vtvt9uzfX0AAFD0+Hg6AAAAPKLFzdKqz1zbzp2QXqyZ8txvh0llaktHN0sJsVKTgZJ30pJ07qhQMpVh/v+pOX6mfrm7kxpXDc7UNQEAQNFms9MtkK6oqCgFBwcrMjJSQUHpLHsEAChYzF9/Zt78pBpJbXV6SNvmuPf5CZmfc7905wkV8/VOc/m6sVc00MgutTN9XQAAUDTzUIbcAwCKJjOXvlgpaeQ/SW3uJvPGtDuks8cydcs2NcuoSbVgfTWinRpUSraU3n8mzfqXIfgAAMBtJPRpiIiIUFhYmMLDwz0dCgAgN1VslLXPrf1ampy13vT2tctq1n2X6n93tE9zCD4AAEBGSOjTMHr0aG3cuFHLli3zdCgAAE/q/VL6x1d8nOVLmx77lwc206uDm6U49vHCnVm+LgAAKBpI6AEA6DLedb9s3aTt8OHpf/bne6ULp6UPLpc2TM/0rQe0qqZ+LaqlqII/4eeNOnaGteoBAEDaSOgBAGh+o+v+iD+kBldJ7Ua7rltforwUdk3Kz0+sJu1dIn07VJoQLH13W6ZDMFXulz7a3aWt9bOZmNMPAACKHBJ6AABKVkrabnO7FBAkXfeFdMXzKc8tWyfj663/Tlr2fqZCCPD1tpa2u7d7Xdcqt9GxmboOAAAoOkjoAQBIvqa8t1/K4436Od473id1GiO1vlW69qP0rznjgSyFcv9l9XRn56Rie4OmLsrSdQAAQOFHQg8AQEb6vSONmCe1GyX5B0pXvSo17p/x58zw++cqO9a8z4RxvRo4t/89dFor95zMStQAAKCQy3RCv2PHjtyJBACA/CD5nPlEPv5S1ZaS10V/bfaanPH1Ys9Jb6W+PJ27+r/1T7Y+DwAACqdMJ/R16tRR165d9fnnnys6Ojp3ogIAoCDw8k7aHvpz2ucd3ZTpSz/ZJ8xlP/Icc+kBAEA2E/qVK1eqadOmGjNmjCpVqqQ77rhDS5cuzexlAADIn2zJkvQMJRtKX/NS6bovpbuWSyUqpDz1+WpSQoLbVx7WIVQ3tq3u3H/jj62ZiAsAABQFmU7omzdvrtdff10HDhzQhx9+qIMHD6pTp05q3LixXnnlFR09ejR3IgUAIDe1v0sKrOiYJ++ui+fGN7hSKldXKhWS8tyY09L8VKrmp8Fms+mZaxo7999fsJN16QEAQM4UxfPx8VH//v317bffatKkSdq2bZsefPBBhYSEaMiQIVaiDwBAgdHzOemBzVLJiu5/pn4vx3u5+q7taRXB+8uNOffJeHm5zudnXXoAAJAjCf3y5cs1atQoVa5c2eqZN8n89u3bNXv2bKv3/pprrsnqpQEAyD8F8dITXE16aLt059+u7Q2vcrwHVUv5mbPHpemjpL1LpSMZz61vX6usy749kxXzAQBA4WWzZ/JfBiZ5/+ijj7R582b17t1bw4cPt969klX+3bdvn0JDQxUXF6eCKiIiwnrFx8dry5YtioyMVFBQkKfDAgAUBHEx0sYfpZqXSC9f1Ht/sVpdpSHT0zx8KDJa7SbOde6/e3MrXd6oUqrn7jx2VkeiotWmZhlryD4AACiYoqKiFBwcnGEemumEvm7durr11ls1bNgwq3c+NTExMfrqq680dOhQFZUfJAAAqdq1UPrfEOncsbTPGb9P8i+Z5uHtR8+o+8t/Jl3yhStdjj/x43p9umh3iir5t3SsmZ3IAQBAYUvoixoSegBAjpgQnPE5YzZJQVVSPRQ6boZz+/uR7dWqRhntOHpG3ZIl+hd7tHdDjbi0VprHExLs+mXdQVUtFWBdDwAAFKw81CezFzbD7QMDAzVw4ECXdlMc79y5c4WiVx4AgJxnhsBn8Az9lYbShMgMrzTg7UX6fmQHDXj7n3TPe27mJp2+EKcxl9VzaTfP8u/4bIV+33jYpX3aqA5qWb10hvcHAAAFtCjexIkTVa5cuRTtFSpU0PPPu78cDwAARcqN37p33obU59NfF+66FF5GyXyiKXO3asSny5370bHxqjl+Zopk3uj/1j/WSADTcw8AAAphQr9nzx7VrJlyTl6NGjWsYwAAIBXFkw1pb3i1490rlYFy3w5Nddm7x68Ky/AW93Svq83PXqGqpYq5tM/eeFgLtx1TfIJdDR6fleF1aj0yU5HnYjM8DwAAFLCE3vTEr127NkX7mjVrVLas69I6AADgPz4BSdtXviLdt17qNSn1c1d8nKKphL+PZt13Saqnh5QpptVPXGYNrff38baG4w9u7dqjf+P7S1T7kZkpPjusQ6j6Nk85b7/Z07/r2JkLbnwxAABQYBL666+/Xvfcc4/mzZtnLelmXn/88YfuvfdeXXfddbkTJQAAhYlvMalUSKo98ZZf7ku1uUGllEVxbm5XQ38/3E2livs52yoFB+i5fo1VMcg/3TAWje+mCVc30mvXtdCcMZ1THG/97BzWvQcAoDAl9M8884zatm2r7t27q1ixYtbr8ssvV7du3ZhDDwCAOz30JqE3QtqmXxU/g8r4pkd+bK8Gqd/O20v/jOuuRlVSr4z7z7huqhycNDS/ToVA7ZzYW5fWK+9ynplvDwAA8qcsL1u3ZcsWa5i9SeibNGlizaEvjFi2DgCQI8xftzMekIqVlro/ntS+e5EUXE1a+o70zxupf/axI5KPf4rl6y5ejz41e0+c0yUvznNp++XuTmpcNe2HBZNm/au352937j/fr4luaFs9w3sBAICcwTr0OYSEHgCQZ1Z+Kv10d+rH/lvO7s8tRzXu+7V6YUBTdb6oNz0t5q/6xJ52d5em6/jCH9p/6rxzf92Ey1UywNe97wEAAPJnQm/mzH/88ceaO3eujhw5ooSEBJfjZj59YUJCDwDIM/Gx0jMpl4a1uLE+fXp+XL1fO4+d1b3d68pms2V4/qlzMWr+9GyXNndGBAAAgLzLQ1NZLyd9pvidSeivvPJKNW7c2K1/FAAAADd4p9MDHnNO8iue5Utf07xqps43Rfa+GN7Wqo6f6J0/t+uOzrWzHAMAAMhZme6hL1eunD799FP17t1bRQE99ACAPJVeIbwrJknt7szLaPTQt2v07Yp9zn166QEAyD95aKar3Pv5+alOnTrZjQ8AAGTWrLF5fstn+jZ22f9pzYE8jwEAAORQQv/AAw/o9ddfZ11aAAByU42O0vBU6tKc2JGnYQT4emvl45c59+/5alWe3h8AACjn5tAvWLBA8+bN06+//qpGjRrJ19d1vt+0adMye0kAAJDo6jekvyZLV74iVUhljfl/3pSueiVPQypTws9l3zzUp4YOAAAFsIe+VKlS6tevnzp37mzNpzfj+pO/CouIiAiFhYUpPDzc06EAAIqSlkOk+9alnswbyz+QLpx2bO9bLq37Topo65h7n4u99+8Pae3cXr8/KtfuAwAA3Mc69BmgKB4AwKOeKiPZ413bWt8qdRkvvVQ35fk9JkjNb5LOHZdKlJdKlM2xUELHzXBuUxwPAIACWBQPAADkoUseSNm2/MPUk3ljzgTppTrSW22lybWkyKQK9QAAoIjPoTe+++47/e9//9OePXsUExPjcmzlypU5FRsAAOg8VqrdVfr4qpQ99e54tZFk85aePJHtUDrULqt/th+3tjceiFJYFUauAQDgSZnuoZ8yZYpuueUWVaxYUatWrVKbNm1UtmxZ7dixQ7169cqdKAEAKKq8faQaHRxJfVaZBwH7s//A/cNhSXVl3vs7b6vtAwCAHEjo33rrLb377rt64403rDXpH374Yc2ePVv33HOPNb4fAADkgr5Ts/f597pKCQnZXsIu0Q+r9mcvHgAAkPcJvRlm36FDB2u7WLFiOn3aUWn35ptv1ldffZX9iAAAQEqB5aXRy7J3jU/6OKrhm9eFM9kOKT6BuroAABSohL5SpUo6ccIxD6969epavHixtb1z505rXVoAAJBLgqumfaz7E1KtLkn7Qamcu3tB0vbEqo7EPpMm9m/i3P74n12Z/jwAAPBgQt+tWzf99NNP1raZS3///ffrsssu0+DBg6316QEAQC7xLe66P+ADqeO9UvmGUvhwsxpt0rGhP7t3zdVfZiqEga2qObd/WXsgU58FAAAernJv5s8n/DcHb/To0VZBvH/++UdXX3217rjjjhwODwAAONmSJexGk2sdr8ueTnm8VHWpeFnHevTpmT5SajLIUXzPDT7eSX0Bq/acykTwAADA4wm9l5eX9Up03XXXWS8AAOBhLW6Wtv8hVQuXvH2lMZscvfZfXy9tm5P2574YIA3+QvIPzMtoAQCAJ9ahP3nypD744ANt2mT+oSCFhYVZw+/LlCmT3XgAAEBWNeonla4hlavv2Pfxd7xf+bL0erO0P7djvmNOfbfHpUsfzPA2wzqEOufPz1p/SFc0rpQz8QMAgNydQ//XX3+pZs2a1nr0JrE3L7Nt2swxAADgIWbIfdVWKXvai5VO2u79Utqf/+MZKT42w9s82SfMuf3dir1ZixUAAOR9Qm/mzQ8aNMiqaj9t2jTrtWPHDmvYvTkGAADyGb9kCX5wNalhH8mWtKa8i5/vzfBytmRz9edsOqJNB6NyJEwAAJDLCf22bdv0wAMPyNs76R8CZnvMmDHWMQAAkM94JUvevXylwZ9LTxyXrnwl5bmrv5D+eDZTl+/1+t/ac/xcDgQKAAByNaFv2bKlc+58cqatWbN05ucBAIDs6zTmv/f7M/c5s6xdSFupVmfHvullL98g9XP/mpzh5RpUKumyf+nkeYqOjc9cTAAAIG+L4t1zzz269957rd74du3aWW2LFy9WRESEXnjhBa1du9Z5btOmTbMXHQAAcGUK1zUdlFT4zl2mMF5mxJyV/EqkebhPsyr699Bml7YGj8/SrheuzNx9JC3cdkw3vr/EuV8xyF+LxnWXl9dFy/QBAAAXNrvdblcmJF+yLq15deaS5j0+vuA/qY+KilJwcLAiIyMVFBTk6XAAAMg5hzdKb7d3bJeqIZ3anXSsRHnp7NGk/avfkFoOce7GxCXori9X6veNh10u2btJJb11Yyu3Q4hPsKv2IzNTPfbvM1cowDeNuf4AABRi7uahmU7od+9O9pd9BmrUqKGCjoQeAFComeH1JatIC1+Xjrn2uKdwx99S5aTRd+afEKO/XKmZ6w65nJaZXvrQcTPSPZ6VHn8AAAq6XEvoixoSegBAkbBrgfSxm8lzra7SkOnWZlx8ghpP+E3RsQnOw38/3FUhZYrnSEJvhUZSDwAoYqLczEMzXRTP+Oyzz9SxY0dVqVLF2WP/2muv6ccff8x6xAAAwHNCO0n3b3Tv3B3zHHPsTTEeby8teaSHy+HbP1vh1mW+WronReK+c2LvFOcdjDzvXlwAABQxmU7o3377bWuJut69e+vUqVPOefKlSpWyknoAAFBABVeVWg1z79znq0hnjzs+VsxX79ycNG/e3XXpx09b59z+3x2OufymBs/FSX37iX+4FxMAAEVMphP6N954Q++9954effRRl7XoW7durXXrkv5iBgAABVDbke6fO7mWc/PysIqZuk1CguuMvzY1yzi3TVK/4jHXXn8AAJADCf3OnTvVokWLFO3+/v46e9Yx/K4wMMvwhYWFKTw83NOhAACQdyqksTZ9BkwSPvaKpM8eOR2dahKfmMi/NmeLs71KcECKc8sG+qtbgwrO/WNnLmQpLgAACrNMJ/Q1a9bU6tWrU7TPmjVLDRs2VGExevRobdy4UcuWLfN0KAAAeEb7u6TrvpLuW5/2OROCHa+EBAX4Jv2zYsrcrS6nfbZol2o9MtN6mUJ4U/7Y5jy2cFy3VC/9/pDWzu3w5+Zk77sAAFAI+WT2A2b+vEl2o6OjreVqli5dqq+++koTJ07U+++/nztRAgCAvGezSQ1SFqlL1dOlVb73Eufu54v36Nm+TZz7j/+4IZ3b2FJt9/JKamdNHgAAciChHz58uIoVK6bHHntM586d0w033GBVu3/99dd13XXXZfZyAACgIPDykRLipC7jpfkTUz3lqpltdZe+TNH+zTLXavaZ0bhqkNbvd6/IHgAARU2mEvq4uDh9+eWX6tmzp2688UYroT9z5owqVEia4wYAAAqJam2Sth/aJl04LZWqLrW5XXqxZqofaVnFXysPuM53H/t92kVzMyp+Fx5axpnQL915wqV4HgAARV2m5tD7+PjozjvvtIbbG8WLFyeZBwCgsLlrudT/falhn6S2YqUdybxRPO2k+sP4x5zbmw+dtqbnpadkgG+6x8dcVs+5/cGCHW4EDwBA0ZHponht2rTRqlWrcicaAADgeeXqSk0HOubQp6VCWKrNpSI3qZbtgLU9bdU+rdp7yuX4rheu1N8Pd1Wgv496NqooPx8vtxP+3zYcztz3AACgkMv0HPpRo0bpgQce0L59+9SqVSuVKFHC5XjTpk1zMj4AAJAfpdPz/of/g2oc/b7e+XOH3v0rZa96SJniWv9Uzyze1p5mET0AAIoamz2jsXAX8fJK+STd/MWa+BdsfHy8CpOoqCgFBwcrMjJSQUFBng4HAID8IaKddHSTY7tYGen8iRSnhEa7Fshb/cRlKlXcL9O3evaXjXp/wU7n/sx7LlFYFf5OBgAUXu7moZkecr9z584Urx07djjfAQBAEXD1FMnbX7rsGWnsTqlGpww/kpVk3hjfu6HLfu8pf+vTRbuydC0AAIr0kPsaNWrkTiQAAKDgCGkjjd8n+fyXpDe5Vtq9wOWUfl5/64eES6xtmxKkmHPSW22lq9+QanVx+1beydajT/TEjxt0c7saDL8HABRpme6hnzhxoj788MMU7aZt0qRJORUXAADI7xKTecOekOLwq35vW++7Am7QzoCbpOcrS6f2SJ9eI73SKNu3/3b5vjSPnTgbo9BxM6zX3E0U0wMAFE6ZTujfeecdNWjQIEV7o0aNNHXq1JyKCwAAFCipl+QxyXyqovZJE4Idr5fqSQnp1+DZ+HRPPd+viUvbw9+vTfP8ls/Mdm7f9slyNXnyt/TDBwCgKCT0hw4dUuXKlVO0ly9fXgcPHsypuAAAQEFSq2vWP3vmsPRyys6C5Ir7+eiGttWtZe8ycjo6NmXbhTit2xeZ9RgBACgMCX1ISIgWLlyYot20ValSJafiAgAABUnZ2tI9q6UeT2Xt82ePOHrr4y5k6mNnLsRZK+0MemeRNbw+OjZeTSb8nuq5fd50neMPAECRS+hHjBih++67Tx999JF2795tvcz8+fvvv986BgAAiqgyNaWajiJ4WfZshQxPeXVwM+f2ou3HNeyjZVq607FsXoPHZ7mc+9aNLV32/z0Ulb34AAAoyOvQm9PHjRunKVOmKCYmxmoLCAjQ2LFj9cQTT6iwYR16AAAyyRS92zE/659/YItUsmK6p5jeeHeYIfoP/G+Nvl+ZVEDPnWH7AAAUhDw00wl9ojNnzmjTpk0qVqyY6tatK39/fxVGJPQAAGTS2ePS5FppH299m7T8g7SPFy8nBQRLJ7ZLdy6QKjSSvLwyndD/dFdHNa1WSrHxCar76K/OdhJ6AEBhyUMzPeQ+UWBgoMLDw1W9enX9+uuvVnIPAACgEmXTPla2rhR+W9J+x/tSnnPumCOZN6Z2kp4uLW2f53JKMV/vDMNoXCXYevf1dv3nzt4T5zL8LAAABUGmE/pBgwbpzTfftLbPnz+v1q1bW21NmzbV999/nxsxAgCAguzmH6SR/0iNB0g3fON6rLub0/U+6yud2uvcnXB1WIpT7ulWx2Xfy8vm3F7z5OXO7Ud+WJeJ4AEAKEQJ/V9//aVLLnEUvPnhhx+sOfWnTp2y5tQ/++yzuREjAAAoYLaWTlYcr2ZnqWIj6doPHdXwS4cmHfPKuKfd6bXGUux5a7N2+cAUh8dcXt+5PbprbZdjwcV8ndt/bz3m/j0BAChMCb0Zw1+mTBlre9asWRowYICKFy+uK6+8Ulu3bs2NGAEAQAHjXb5e2km7Xwnp4Z3SuP963O9aLvWZ4t6Fn6skLftArUMd/xZJ9Omtbaz3JY9014vXNtXd3epm8xsAAFBI16FftGiRzp49ayX0l1/uGMJ28uRJq9o9AABAzXIl0j+heBkp4L8iP+XqSq2GSo8fd+/iM8ZI50+6NF1St5z1XjEoQINahyjAjTn2AAAUuYTerEF/4403qlq1aqpSpYq6dOniHIrfpEmT3IgRAAAUMDZlYREdbx/3z50U6no/W9J8+bQ8fEXSkPyEhCwt8gMAQMFO6EeNGmX10H/44YdasGCBvP5bRqZWrVrMoQcAADmjbB2pSgvHtm/qvf3f97ardHFffTC0tVuXvKVDTef26n2nciZOAAA8KBOPwpOYyvbmlZyZQw8AAGCp3Dz71xj6s3RwrVS9vWPpuou0+uNGrXzylFu980aAb1I/xsu/b9YXw9tl+Jm4+ATFxttVzI8h/ACAAprQjxkzRs8884xKlChhbafnlVdeyanYAABAQWWWqIs5K1Vzr/c8hUpNJf+SUmjHdE+znT0mBZZ365LJE/+F2zKerx86bobL/q4X6LwAABTAhH7VqlWKjY11bqfF3SfkAACgkDP/JjCF7jLrzgXS6i+lSx9K/XhwdSlyT9L+S3WktiOlJW879rs+JnVO47OmwL63l2LiE1Qigx73Qe8sStH2v2V7NSg8xN1vAgBArrPZzULySFNUVJSCg4Ot5fqCgv6rxgsAAPLWxh+lXQukOpdJXw5M/9yGV0uDP8uw1z29HveLe+fd+QwAAHmdh2a6KJ5hngEcO3ZMx4+7ubwMAABAdoRdI/WeLPm6sUTupp+kCcHS5DopDhXP5lz4Y2cuZOvzAADkpEwl9IcOHdKQIUNUunRpVaxYURUqVLC2b731Vh0+fDhHAwMAAEihRkepTg/3zj17VDp3wqXp7m51ndsxcQmpfmz70TPO7ZFdauv+HvWc+98s25v5mAEA8PSQe9Pl37x5c505c8Zah75BgwZWT/3GjRv11VdfWYn9ypUrFRgYqMKEIfcAAORDT5eTEhz1fTI0bo8UEGxtnr0Qp0ZP/pbuEPrx09bpq6WOefqz779UdSuWdHuoPgAAeZmHur1s3euvvy5vb29t2LBB5cu7VpN97LHH1LFjR02ZMkWPPPJI9iIHAADIyMPbpRequ3euOW9CZKpD7tfvj1Tjqo5kP1FiMm+YZB4AgAI/5H7GjBlWsn5xMm+Yoffjx4/Xzz//nNPxAQAApPRfj7vb4uNSXZHnqjcWuPVxHy9W8gEAFOCEfsuWLerQoUOax82xzZs351RcAAAA7qveQap5adrHJ9eWvrtNij2f4lBCQsazD2/pGOrcjnfjfAAA8lVCb8bwlypVKs3j5pg5J785deqUWrdubc3/b9y4sd577z1PhwQAAHLaLTOlIT+lfTz6lLT+O+m5Sto64KjLoa+TFbpLXlqoa/2kUYl3dK7t3F6552Sqt2gy4Tdrrn1aS94BAOCxhN78BefllfbpZghbflzSvmTJkvrrr7+0evVqLVmyRM8//zzL7QEAUNiYofTmNeADqVw9qWqrNE/1nXGv7u1Yzrn/yA/rrPe9J86p5viZzvZmIUkdGeUC/Z3bY79fm+Kaptje6WjHsH6DpB4AkBfcLopnkvV69eqlmHuW/Hh+ZAr5FS9e3Nq+cOGCFWd+jRUAAGRTk2sdr7PHpIi20rljqZ52/4rL9bq+dGm75MV5Lvt3dU25jr2x4+hZK2FPXu3+4e/WpjqU34u59wCA/JDQf/TRR7kSgOk9nzx5slasWKGDBw/qhx9+UN++fV3OiYiIsM45dOiQmjVrpjfeeENt2rTJ1LD7zp07a+vWrdZ1ypVLeioPAAAKqFt+lWY+JPWenPJYiXKOSvgT0i6ed7XXQv2U0NHaXrE75TB6H+/0BzJ+umiXhrR3zK2fse5giuO1HpnJEncAgPyR0A8dOjRXAjh79qyVpN96663q379/iuPffPONxowZo6lTp6pt27Z67bXX1LNnT6sAn6mub5j58XFxScPcEv3++++qUqWKNb9/zZo1Onz4sHWPa6+9VhUrVsyV7wMAAPJIjQ7SyIXpn3PT99LnA1I9NMUvQtsvVNUGe6gGvbMo07d/4scNui68unYfP5vpzwIAkBNs9nw0/twM57+4h94k8eHh4XrzzTet/YSEBIWEhOjuu+/WuHHjMn2PUaNGqVu3blZSnxozLN+8EplCf+Z+kZGRCgoKytL3AgAAHrJtTpoJfaLQaNeh90b7WmX11e3tXNqW7Diuwe8uztTt6aEHAGSFyUODg4MzzEPdLornCTExMdZQ/B49ejjbTGE+s79okXtP0k2v/OnTp61t88MwQ/zr16+f5vkTJ060fnCJL5PMAwCAAioL3RYbnuqpL0e0TdHetlbZDBP0X+7upFLFfZ370bHxmQ8AAAA35euE/tixY4qPj08xPN7sm/n07ti9e7cuueQSa1i/eTc9+02aNEnz/PHjx1uJf+Jr796kpWwAAEAB45NUnV73rJJGpuwQmOjjuqRtCX+fNIsAG2VL+KV5rG7FQC0e3925/8/21IvyAQCQp3PoCypTPM8sWecuf39/6wUAAAqBGh2lBlc5lrIrUyvVU673mafxcSNUx7ZPc/wflib8d+Cat6QWN6Y4/7GrGur+b9akei1/H2+X/e9W7FO3BtTtAQAUwYTeVKM3y86ZYfPJmf1KlSp5LC4AAFBAeHlJ132R4WlNbDv0s/9jro0/jpLK1HQU30umSz1HUV53/Ln5qPuxAgCQ20PuBwwYoEmTJqVof/HFFzVw4EDlJD8/P7Vq1Upz5851tpmieGa/ffv2OXovAABQRNS5TAp07TVPkcwn+qiXFLlPSlZDuPRFQ+6DAnz04OX1NPeBzik+fjYm9Tn03V6eb61lb14AAORZD70pKjdhQuJYtCS9evXSyy+/nOkAzpw5o23btjn3d+7caQ2RL1OmjKpXr24tWWeWzGvdurU1fN4sW2eWurvlllsyfS8AAADd+K2UEC89U9a9819tlLTd9VGp88Muh6eN6qA6FUq6fftvl+/VjqNJS939vuGQLm/EyEMAQB700JsE3PScX8zX19cqrZ9Zy5cvV4sWLayXYRJ4s/3EE09Y+4MHD9ZLL71k7Zv15k2yP2vWrFxfRz4iIkJhYWHWknkAAKAQMQXvvLM463Dec9KFMy5NoWVLpDitboXANC/x0HdrXfZv/2xF1mIBABR5mU7oTYX4b775JkX7119/bSXAmdWlSxfZ7fYUr48//th5zl133WVVqzfrwy9ZssRamz63jR49Whs3btSyZcty/V4AAMADek3O2ucmVtU7pT937vp4p/zn1PBLamYnMgAA3JLpx9OPP/64+vfvr+3bt6tbt25Wm5nT/tVXX+nbb7/N7OUAAAA8o+3t0q8PZemjPc/PlHST2tYsk+rxkgFJa9FfiItPUf3+YvEJdnl7pb1UHgAAOdJD36dPH02fPt2a9z5q1Cg98MAD2rdvn+bMmaO+fftm9nIAAAAe829CSJY/+/eVJ/TJrW1SPXZpvfLO7f0nzzu3950859y+q2sd5/b0VfuzHAcAoOjKdEJvXHnllVq4cKFVnO7YsWP6448/1LlzysquAAAA+dmyWqNdG66YJFVuJvWcKN25IN3Phsy9SwHxZ6QXqkubf3U5FuifNAiy28t/OrdnrT/k3L6ne13n9u4TSYk+AAC5mtADAAAUBl3atnJuf93ic6ndndIdf0ntR0mVmmR8AZPMR0dKX10nTQiWTh9O9bRF249b78/O2ORs8/NJ+mfYl0t2Z++LAACKJLcSerOEnOmJN0qXLm3tp/UCAAAoKEpUaeDcblurdNondrzXvQu+XC/V5uvfW5zux46diXHv+gAAZLYo3quvvqqSJR3rq5p14IsCs2ydecXHx3s6FAAAkEvMsruJbLZ0CteZYfg3T5emjZDOHk3/op/1k27+QXd2rq2pf253Np+PSflvivoVS2rz4dMp2u/+apV+XnPA2t7+fG8K5gEAUmWzmzXikKaoqCgFBwcrMjJSQUFBng4HAADkoOiYWAU8X87a3nXtbwpt3M71hMMbpH3LpZZDHOvXm382PVUq4ws/eco6P3TcjDRP2fXClS7HVzzWQ2UD/a3tiz9nzgUAFB1RbuahXu5ezN0XAABAQeHrndQrb/crnvKEio2kVkMdybxh3se7UZHeJP0TguWv9IfST70paQ5/q2fnyPSz0NcCAMjRIfelSpWSLfEvsgwwRB0AABQU3t5e+rbsnfKKPqV+ddwogmf4B7p9/c0BwxQa/WWK9s7/LWtXs1wJl/YF246pSqliKc6PiUtwKaIHAIDbCf28efOc27t27dK4ceM0bNgwtW/f3mpbtGiRPvnkE02cOJGfKgAAKFAG3j0p8x+q0Eg6ssGx7VNMiktaa/5ivopT7EX/5JpwdSPrvVTxpDn8xncr9unH1Y6588l9vWyPhrQPzXycAIBCLdNz6Lt3767hw4fr+uuvd2n/8ssv9e6772r+/PkqTJhDDwAAUph6iXRorWN77G7pnynShunSiaQieMkl76Xf8mwvl972J35cr08Xpb9snZ+3l7Y81yunogcAFKU59MmZ3vjWrVunaDdtS5cuzXykAAAABY1Xsor4xUpJ3Z+Q7lmZ5unXeC1wbl88dP7paxpneLuY+ISsRgoAKMQyndCHhITovffeS9H+/vvvW8cKC7NkXVhYmMLDwz0dCgAAyG86j3O8Nx3s1umv+72VpdtcPMc+OVMJ37wuxFG/CACKqkwPuZ85c6YGDBigOnXqqG3btlab6ZnfunWrvv/+e/Xu3VuFCUPuAQBAqqIOSIGVJK9k/SMTgtM8vfuFyZrr/5B01WtSzUulsrWdx2qOn2GtiHcxs/x8gj3l0nUsawcAhVuuDbk3CbtJ3vv06aMTJ05YL7O9ZcuWQpfMAwAApCmoimsyb1Rp4Xivl3K+u5XMG7/cJ73RUjq8Md1h9+N6NVC3BhVzOmoAQFGrcn+xatWq6fnnn8/5aAAAAAqyG76VNkyTmg6SJmVQlf7t9tKESGuzYkl/l0MT+zfRta2qqXlIKc3ZdDjD2x6MPK/KwSmXuwMAFG5ZSuhPnTqlDz74QJs2bbL2GzVqpFtvvdUaEgAAAFBkBZaX2t7h2L75B+mzfumf/98Q/Y4P7nVpvr5Ndet938mk5fBi4xPk6+1lrUl/sb+2HNXg8Oouw/ErBQVo8SPds/mFAAD5WaaH3C9fvly1a9fWq6++6hxy/8orr1htK1emXd0VAACgSKndTQpx1BvKSImXkgoL925SybndoXZZ5/a5GEfxuwOnUq55P/b7ddZ78mT/UFS0MlkqCQBQ2BP6+++/X1dffbV27dqladOmWa+dO3fqqquu0n333Zc7UQIAABREg79w+9R3fV+23jvXK+9sqxwc4NyOOh9rvT87wzFCMjVP/rTBZf+Wj5dlKlwAQBHooR87dqx8fJJG65vthx9+2DoGAACAZEPw3XS59wpdHlZR/VpUc7bZbDbn9pdL91jvc/9Ne079V/+dk2j+5qOZDBgAUKgTelMyf88e178sjL1796pkyZI5FRcAAEChsC+wqdvnvntlafn5pP7Ps+K+3tZ7WqPoExLXtwMAFBmZTugHDx6s2267Td98842VxJvX119/reHDh+v6669XYREREaGwsDCFh4d7OhQAAFCAeYe0TNoZu0saPleqlEaSb5az2zZHio5KceiTRbvTvU+tR2ame9wU1TMF8y5ewx4AUISq3L/00kvW8K8hQ4YoLi7OavP19dXIkSP1wgsvqLAYPXq09YqKiqJ6PwAAyLLKwcWTdoqVlqq1lmzp9Kl8PsDxftscKSSpY+HYmQvWe7lAPx07E6PWNUpr+e6TbsdR99FfndtXvfG3frn7ksx9EQBAwe+h9/Pz0+uvv66TJ09q9erV1stUujdV7/39XddQBQAAKPJSS9793Zim+EEPKda1ov2+k+esZN4wyfzAVknz7TNj/f6UIwAAAEUgoU9UvHhxNWnSxHqZbQAAAKQiteH1V70mla0jXRMhtbg57c8+l7SEnTH2+7Wu+70aZHj7uPiU69YDAIrQkPv+/fvr448/tgrime30mGXsAAAA8J8mA6XoU1JIm6S2cnWku1c4tlvcJK36LM2Pf3tnew2cusjaXrjtuLO9epniKhfor2WP9lD4c3NcPvP6dc1179erre3dJ86pfElGUQJAke2hN3PIE5dNMdvpvQAAAJCMl5fU9g6pSou0zxn0aZqHwpc/lGp785BS1vvFyfptnWqqT9Mqzv3Nh05r04G0h9i/NX+bNaceAFBIe+g/+ugj691ut+upp55S+fLlVaxYsdyODQAAoGgIuybtY+u/066A7xQa/aVL862daqZ6+uNXhbnslynhp00HUyb052LitOvYOb04a7O1b6rf73rhyqzFDwDI/3PoTUJfp04d7du3L/ciAgAAQAr+chTDu7iHPiOr957ShJ83pmhfvOO4ek+hZx4AikxC7+Xlpbp16+r48aT5WwAAAMhBIW1TbR7r87Vz+6e7Orp9uRd+/Ved65VP0f7hgl2pdt4AAApxlXuz1vxDDz2k9evX505EAAAARb2I3qOHUzTf6jPLpNzWdqWgZPPmE+IzvOSfW46maFuw7ViKtu1Hz2Q+XgBA/p5Dn9yQIUN07tw5NWvWzFqT/uK59GZNegAAAGRSjY7S7oVSwz6Sb0Cqp7S0bVUF2ylVeOUGl/Yfwu5Uv42X6omL5s9n1slzsdZ75LlY9Xt7oT4aFq4aZUtk65oAgHyU0L/22msqCiIiIqxXfHzGT70BAACybegvUuw5yT8wzVOm+U9Itb3Fjqna2Ke2ijdvJ8XHSt6+en9Iaw3/dHmmQvh13SGFh5ZRs6d/t/Y7T55PoTwAyMdsdiZLpSsqKspaji8yMlJBQUGeDgcAABQVE7K5HPCESKty/cWWPNJdbZ+fm+pHHundQMM71VKtR2Y62767s71ah5bJXiwAgFzJQzM9h97Yvn27HnvsMV1//fU6cuSI1fbrr79qw4YNWbkcAAAALlbviux9fkKwBoa5Dpdf/lgPVQxKfTi/8dqcrTp+1rWa/kcLUxbPAwDkD5lO6P/88081adJES5Ys0bRp03TmjKN4ypo1a/Tkk0/mRowAAABFz6BPpdv/zNYlJu8w69snDcYsF5ismF4yxXy9HbdsHaLDUdEux5bsPK6YuASrt9+8YuMTshUTAMCDCf24ceP07LPPavbs2VZRvETdunXT4sWLczA0AACAIszHX6rS3DG3Pht2BdyY7vGf7+qkG9pWt7b9fb10KNI1oT92Jkb1HvvVuV/30aRtAEABS+jXrVunfv36pWivUKGCjh1LufwJAAAAsqHmJdm+RDXbUe0KuEH6/NoUx5pUC1bJAEed5NPRcXriR5YmBoBCm9CXKlVKBw8eTNG+atUqVa1aNafiAgAAQKKyddM+Vqx0hh9f4H+vY2PbbGtu/azgF1yOBwX4Wu9R52N14KIeegBAIUror7vuOo0dO1aHDh2SzWZTQkKCFi5cqAcffNBaox4AAAA5bPBnrvvFyyZt37kg05drcGGt5vg96NwP9Hf00P+yNmWnDQCgECX0zz//vBo0aKCQkBCrIF5YWJguvfRSdejQwap8DwAAgBxWoaHr/oPbpNrdpBqdpKCLRkh2e9ytS9bxOqBf7u5kbX+4cGemQ7omYqGzUB4AwDMcj2MzwRTCe++99/TEE09Y8+lNUt+iRQvVrZvOUDAAAADkHC8v6eYfJLtdstlcjwW4v3594/ccxfCOx7wjqaTbn4tPsGvN3lPO/YQEu7y8LooDAJB/eujN0PpJkyapY8eOCg8PV0REhLp27apBgwaRzAMAAHhCYjJfqel/702kFjdLDa6SOo1x+zLL/O7I1G3PxsS57P/x75FMfR4AkMcJ/XPPPadHHnlEgYGBVvG7119/XaNHj86hMAAAAOCOdQEtUzbe8I106UPSDf+TfAOk676QejyZqeuWU6Tb5246EOWyv/fkuUzdCwCQxwn9p59+qrfeeku//fabpk+frp9//llffPGF1XMPAACAvFGuZImUjUFVpG6POd6T2XvFx0k7pvc+HcsDRqq312Lnfv8Waa9etGCb61LFZy+49tgDAPJZQr9nzx717t3bud+jRw+ryv2BAwdUGJkpBabgn5leAAAAkF9UCg5w+9yQao458pbBX2R4/lt+U5zbkwc2U0iZYs79OWM6O7eX7zrp8rldx8/p/b93WAXyNl7Uew8AyAcJfVxcnAICXP8C8fX1VWxsrAojM51g48aNWrZsmadDAQAAkAIrWW+2BkkdLBmyJxtJWbqGNGKedM/qdD8SrDMa1iFU3l42NagU5GyvVjopuT982nWt+kXbj+vZGZus7d5T/nY/PgBA3lS5t9vtGjZsmPz9/Z1t0dHRuvPOO1WiRNLQr2nTpmUvIgAAAKQ0cqG0f6VUp7v7n/Hxc92v+t/8+7qXS1t/T/UjawJul1bKet0QPlVzFCi7vOTvk9QPtOPoWZfPhJYrrv2nzmfm2wAA8jKhHzp0aIq2m266KSdiAAAAQEZKlJPqXZ65z5jq9y1ukoKTDb03SlZ26+Ndl92pnQFSaPSX1lTLtCzcdtxlPzY+Qb7eXixnBwD5JaH/6KOPcjcSAAAA5CyThF8TkbI97Gpp5SdSUFUpan+Gl/mgT5lM3XbJjhO66YMlzv1dL1yp6Nh4Kxx/H+9MXQsAkANz6AEAAFBI1O4u3T5fGrVIaplyFObFus++QpoQrOq2w25dvpifa9K+cs9JNXh8luo/Nktx8ayQBAA5hYQeAACgqDFd5VVaSAHB0lWvOpJ7N/zlf79b55ne+OT6v/WPc/veb1Y76zMBAPJoyD0AAAAKIS9vR3Lvpsu8lmt2Qmtru1b5EikK5Bkv/b45zc/PWHtQM9bOsLZ9vW3a+lwmqvYDAFzQQw8AAAAd6fc/t857z+8V+SvG2v7jgS6pnrNqzym3rhUbTy89AGQHCT0AAABUqv4lbp+7OWBYjt77fIzrEH0AgHsYcg8AAAB5eycVstt32VRVO/CbVC1c+u0Rt6/RtFqw1u6LzNR9Q8c5ht8nVsMHALiPHnoAAADIy8yl/49P+brSwI+lam3SPH9XwA1W5fvk6lcsmasxAgBckdADAABAtmQJfaXKIY4Ne8ZLzP3pd59scpz37Yp92R56b3rsH5u+LlvXAYCigoQeAAAAjqXshs2QbvhWKlnR0VauboYfq+F1RP/658yc+oZPzLLeP1+8h2XtAMANJPQAAABwCO0k1bs8ab94Gem+9dJDO6SeE9P8mL8tTl8Mb5ujofSesoDeegDIAAk9AAAA0lYqRCpRVmo/SrprRZqndaxsy9HbbjoY5eytBwCkjoQeAAAA7ilXJ+1jk2upoW13XkYDAEUeCX0aIiIiFBYWpvDwcE+HAgAAUCD86j/eWSAvJ/WNWGgNv/9mGb31AJAcCX0aRo8erY0bN2rZsmWeDgUAAKDAGOj9Z6rtVUsVy/I1V+89Zb2P/Z759ACQHAk9AAAAcsyLvu+l2t6pTrk8jwUACjsSegAAALjPr6TjvWIT6eo3Uj2lqo5a70vGd9PL1zZR29BgPVpnh65vXdV5zrheDbJ0+wtx8UpIYEk7ADBsdhb5TFdUVJSCg4MVGRmpoKAgT4cDAADgWUe3SEvflTrdLwVXlSYEp3paaPSX2hVwQ4r2qy88o7X22np/SGsN/3R5lsPYObG3bLacrawPAAUtDyWhzwAJPQAAQDre7SodWJnlj5vEPyu+HN5WHRjGD6CI56EMuQcAAEDWZbOX3PTit7RtyfTnnpmxyap8/86f27N1fwAoyEjoAQAAkHXl6mf7EtP8J6iRbVemPrPpYJT1PvHXf63E3rwAoKghoQcAAEDW9XxOajk025eZ4f+IfBSXrWus3edY3g4AigoSegAAAGRd8TLS1VOk+r2zfaltAUM0xPs3dfPK2pz8q99caPXUv/Drv9mOBQAKAoriZYCieAAAAG6I3Ce92ihHL5nVgnnGrheuzNFYACAvURQPAAAAeSe4mnR31qvdp1Uwr7iis/TZZbtOKJ716gEUciT0AAAAyBlla6d9LKSdLpRvkrRfvoFbl9wYcGuWQhk4dZFqPzIzS58FgIKChB4AAAC5o94VSdutb5V/ve5J+6MWu32ZUd4/5nBgAFA4+Hg6AAAAABRSN3wjnT0mHVor1eoqzXnSdf16Lx8pIePK9g/7fqOzCtAn8T0zHULicnbMqQdQGNFDDwAAgBxzxF7KtaFEOal2N0cC3/wmR1uNjo73cXukh3dKjx7K8LpP+X5izanPqujY+Cx/FgDyK3roAQAAkGNM3p6m8vUcCXxAsGPfr4TjlQkVdFJHVDrTcTV4fJb1Tk89gMKEHnoAAADkmAAf74zXrfdK+5yVoSPS/fjSgNHKjgtx9NQDKDxI6AEAAJBjAgOyNwC0buUyUqWm6Z6TnaH3mw+dlt3OcnYACgcSegAAAOQYW8BFc+jdZK/nKHhXst0QacQ8aexuxXabkMPRSVe/uVA1x88kqQdQKJDQAwAAIOcM/NjRw37915n6mO36b6RHDkrB1SRvH6lYKfk0ujrN86/yWpStMD9fsidbnweA/MBm5/FkuqKiohQcHKzIyEgFBQV5OhwAAICiI+qg9EoDt04dE3OnpiVc6valywX6a/ljPbIRHAB4Pg+lhx4AAAD5k3+g26e+4jdV93p/bwbvu3V+qxqldC4mLhvBAYDn0UOfhoiICOsVHx+vLVu20EMPAADgCQtfl+JjpWNbpLXfuP2x0Ogv3TovcRm7TQejVLdCoHy86e8CUHB66EnoM8CQewAAgHxg3kTpzxfcPn1kzL36NaFthudVDg7Qwchol7ZJA5pocHj1LIUJADmBIfcAAAAoPDreK7W+1e3T3/Z7XVd7LczwvIuTeWPs9+v09vztmQ4RAPIaCT0AAADyP7/i0lWvSjb3//k6xS8iy7ebNOtfDXj7Hy3cdizL1wCA3EZCDwAAgIJj7K5MnV5RJ7J8qxW7T+rG95codNwMRczbluXrAEBuIaEHAABAwREQnKnTlwTcpV0BN6iEzmfrtpN/26z9p7J3DQDIaST0AAAAKNjq9szwlA0Bt8lfMdm6TccX/tDSnVnv8QeAnEZCDwAAgAJpe2Br6dFD0g3uLWe3OWBYtu856J1F2b4GAOQUEnoAAAAUSNXLBUq+xSSbze3PZHfovTHi0+XZvgYA5AQSegAAABRIvlWaJu3cNlu64gXpgS1SmzvSHXqfXbM3HlbdR2fqlo+WWoXzAMBTbHa73e6xuxcAUVFRCg4OVmRkpIKCgjwdDgAAAA6sljb9LF0yRvIrkfo5E9IuntcheooOqFyOhdOxTll9cksb+XjTVwYgb/NQ/tQBAABAwVKludT98bSTeUm7AxqkeeyfgHvkrfgcC2fhtuO668tVOXY9AHAXCT0AAAAKnUif9HvgtwfcnKP3m7XhkG79eFmOXhP/b+8+wKOq8j6O/2bShpbQFASBANJCEyGUFSsoYkVAXQsCCrZYEX1BRXBRscuyxnXXim0X3RXYXRWkihQFUbAACkgURUBUEopJSDLvc25MICST3Clk5s58P88z78y995xz/7jnTfKfc+45AKpCQg8AAICo0yo1tdrvuXDDTp3xxPvVfl8AsYuEHgAAAFGn9tmT5W3RVxr0jHTynRWWaaTQ7ym/cedepY57W/vzC0LeNgAcjkXxqsCieAAAAM73y9/OVf0fPyh3PjX3dSuxvzF+lpJd+3VB3HLr/NX5t2tBUfeg7pn10DlB1QcQu3JYFA8AAAAo5j3xVp/70n/kuVHD4ueXJvPG84mP63T3JxoeNzfge760bEvAdQHADkboq8AIPQAAQBT47kPphQEBVz8h9xn9Iv//FmSUHkAgGKEHAAAASriC+7P3E891IQsFAEKFhB4AAADRr2l3qVkvqXbjgJvI8lwmj/L8qmMWyDMTYtf/mGO9A0AoMeW+Cky5BwAAiCLfvC+9fH5QTZiF9AL1x/RmemhIl6DuDyD65TDlHgAAADiMJ/gBmg6ubwOu+89VW/XPld8FHQMAGCT0AAAAiB3HHC/VbxVUE+8mjVdDZevSuAWqqVy/649763MNe/4jpuADCBpT7qvAlHsAAIAos3urNLWTz8tFXS+Ve+0//GpyaN69+tjb3u9QPhzfT41TPH7XAxDdmHIPAAAAVKRus0ovuy54uvSz151gq8l/Jf1JTfWT36H0nrJA+/IK/K4HAAYJPQAAADDwEanlKdLId+VyH/wT2XXDCttNLPPcEtCtO06cG1A9AIgPdwAAAABAdZubcrEGZL9RfDB2k1T7KKnXtQcLnHa3tP8XqWEbv7e2a5f7kvKUGOKIAaA8RugBAAAQcxq07n7wwCTzhzvlTmngQ8WfL50h1W9tu+2vPCPMZH2/4hk1fRWL5AHwGwk9AAAAYo7L5Ufy3O4s6eZPpJ7X2K4yO3GCX/HMX79TLce/owsyl5HYA7CNhB4AAAAxxyWX/5XOftR20a7ub/xvX9LarbutxB4A7CChBwAAQMzpdGzdI36PPyc8FXDdn/fmhTQWANGJhB4AAAAxJ7F2w+AbGfyslFineAG9ClwQtzzgprvfPz+IwADEChJ6AAAAxJ7j+km9rpcO2XPeb10ulsZ9V7yAng8nuj8PuPnTHlsccF0AsYGEHgAAALHH5Spexb7b5f7V63Be8XuPq4vfD9mzviKvJU5RQ2Xr0rgFeiphmga7l9i+1ZZd+3TRM4GP8gOIfi4vy2hWKicnRykpKcrOzlZycnK4wwEAAEA4HfhN+na5lNpXik86eP7fo6XP35Bany5tXlhlM6m5r9u+ZdZD5wQaLYAoz0NjZoR+//79atGihcaOHRvuUAAAAOBUCTWKp+sfmswbg/8ujdsqDXneVjNZnst0tH61VXb8W58FEimAGBAzCf0DDzyg3r17hzsMAAAAROsUfk+yVFRou8pKT4YSVFBluX+s3BpkcACiVUwk9Bs3btSGDRs0cODAcIcCAACAaFajnl/FN3qutFVuf36Bhj3/kVLHva0xb6xRfkGRcnIPBBgkgGgR9oR+yZIlOu+889SkSRO5XC7NmjWrXJnMzEylpqbK4/GoV69eWrlypV/3MNPsp0yZEsKoAQAAgArExUvjv/eryvnuZVWWSbt3rj7YuMv6/NYnP6jtPe+qy6T39NfFmwMOFYDzhT2h37dvn7p27Wol7RWZMWOGxowZo4kTJ+qTTz6xyg4YMEA7d+4sLXP88cerU6dO5V7btm3T7Nmz1bZtW+tlR15enrUAwaEvAAAAwLakOlLPa20Xn5ZY8d/Bdjw8Z4O++WlvwPUBOFtErXJvRuhnzpypQYMGlZ4zI/Lp6el66qmnrOOioiI1a9ZMN910k8aNG1dlm+PHj9err76quLg47d27VwcOHNDtt9+ue++9t8LykyZN0n333VfuPKvcAwAAwLaiIulP9qffd8n9u3JUO+jbfnDnaWpWv2bQ7QAIr6hY5T4/P1+rV69W//79S8+53W7reMWKFbbaMFPtt27dqqysLD322GMaPXq0z2S+5AsA8x+t5GXqAgAAAH6pYn/6w33muUZdXZvkUlFQtz3pkUUqKoqY8ToAR1hEJ/S7du1SYWGhGjVqVOa8Od6+ffsRuWdSUpL1DcihLwAAACAovW+QOl9caZHZSfdqi+cKtXUFN6DU6q539MPu34JqA4AzxCuGjBgxItwhAAAAIEYUeerJnfv7XvNn/b5A854fpawPKq33XtL/6bjcl1UQxJ/qJz60UFkPnRNwfQDOENEj9A0bNrSefd+xY0eZ8+a4cePGYYsLAAAAqIo7PrH8yYtfli7IlHpcXWndTTa3s6vMxNlfBN0GgMgW0Ql9YmKiunfvrgULFpSeM4vimeM+ffqENTYAAADAbzXrS92ukPredsRvNX3Ft/r2531auGGHcg8UHvH7AYjBKfdm5flNmzaVHm/ZskVr1qxR/fr11bx5c2vLuuHDh6tHjx7q2bOnpk6dam11N3LkyLDGDQAAAFTKjMa/dpE04IHy1+o2k+7YLD3a2mf1LM9lSs19PagQTnl0cennzyadqWRPQlDtAYgsYd+2bvHixTrttNPKnTdJ/EsvvWR9NlvWPfroo9ZCeGbP+WnTplnb2R1JmZmZ1sssyvf111+zbR0AAAAC276ukhXvi6a0kDtvt8/r8wpP0OgDY0MWzk2nH6fbz2wXsvYAhHfburAn9NHyHxIAAADw1/6ta1Xz+ZMrLRPsKP3hnri4qwafcGxI2wQQWlGxDz0AAAAQzWrWqVft9xzzxtpqvyeAI4OEHgAAAAgbV5UlerrWlzvnUZ4SdSDgu6aOe1s9H5gvJusCzkZCDwAAAISLO+7g56vnS1fOLlfkjaTJ1nt713fWQnnmtcEzUl97hquWfgv41jv35JVZNA+A85DQAwAAAOFS5xipaQ+pWS/p2B5Sq1MrLOZWkeYkjSt3/kvP1TpKu63R+o6uLYqTf9vTfffL/oBDBxB+LIrnA6vcAwAAoFqU/Dnu+n36/aSUoJpLzX3N1lT+Q22ZcrZcJfcHEHYsihekjIwMrVu3TqtWrQp3KAAAAIhmJpE+NJmu3yqo5rI8l6uu9vhVZ9T0j4O6J4DwIKEHAAAAIsn1K6Qx5RfC88caz7V+lV+wYWdQ9wMQHiT0AAAAQCRJ8EjJTYJuprb8ez5+0YadOmfaB9q5JzfoewOoHiT0AAAAQBT6wjPKWkxvUeJtGhs/o8ryI19apS+35ajnAwusbe1+2B34CvoAqgcJPQAAABCJmnYPuolvPFeopXuHboyfbW13d3XcO7brnvjQwqDvD+DIIqEHAAAAItFV74W8yQkJr8qlItvlzUh9u3veFRtjAZGJhB4AAACIRHHxFZ93x0vdR0rXLgmo2S2eK/wqn1dQpC73hf7LBQDBI6H3wexBn5aWpvT09HCHAgAAABx0z0/SeVOlY7oePNest9T+XNtNdHNtVJwKbZffk1ugqfO/9jdSAEcYCb0P7EMPAACAsLtoetnje3+V3BX8CV+zvnRBpnTSWFvNzkyaqM2eYXow/jnboUydv9F2WQDVg4QeAAAAiFRtzih7XFEybzTqKNWoK/WbIDXvY7v5y+L9W/hu4uwv/CoP4MgioQcAAAAiVULN0o9766WVv379cun0e6STbj94boT9lewNs/q9XdNXfKtBmcv00548v+4B4MggoQcAAAAilctV+rFW3+sqHpk/+Q4pocbBc263tnta+XWbdq7vbJdds3W30h+YrwOF9lfLB3BkkNADAAAADuA6ZLS+KjVaHTLtfuJu6fJ/SS1P9ll+btI4v+Npc/e7ytq1z+96AEKHhB4AAACIMim1apQd5TfP4g//bxW1vOrh2qBk7bU+23HqY4uDCxRAUHxsbgkAAAAglmR5Li9znJ77tH5S3bDFA6BqjNADAAAATtCgtf2ybQYUv3tSyp6PS7TdxCrPDbbKpY57W+t/zLEfG4CQIaH3ITMzU2lpaUpPTw93KAAAAIhloxdJQ1+Ump5gv46ZYn/1POnmNWXPp/Ytfq/dyPYK+OaVpPxKyw388wf2YwMQMi6v12vvAZkYlZOTo5SUFGVnZys5OTnc4QAAAACB2/uT9NEzUrcrpK/nSnP+z3bV1NzXK72+8q5+OjrZE4IgAeTYzEMZoQcAAABiRe2jpH4TpPotpd4VbINXicyEqZVe7/nggiCDA+AvEnoAAAAgVvW/z3bRc+JWVlnmzY+3BhkQAH+wyj0AAAAQq068RZo/0Xbx1q4ftNnb1Of1Z97frLo1EzX65Y+t4+tPba2EOLdu699GLrN9HoCQ4hn6KvAMPQAAAKLapMNWwq9Ch9wX9JuSTCrhV71PJ5yherXsr7IPxLIcnqEHAAAA4JceV0kj3qm0yHrPVdae9Yk64FfT3SbP05fbspVXUBhkkABKkNADAAAAMezTzhMOHpz7pJR6onYed0mV9b72DFec/EvOz5m2VO3umWPtXc9EYSB4JPQAAABADKtbr365c0fXrW2r7vtJtwV830fnfhVwXQDFSOgBAACAGNayYQXJ+3H9i9/jPdLAR3zWPda1K+D7Pr14szVSDyBwJPQAAABALKvbovy5dgOlK2dLt34u9bq2igaCmzo/b92OoOoDsYyE3ofMzEylpaUpPT093KEAAAAAR07zXtI5T0jD/3vwnNlirtWpUu2ji49b9/NZ3SyQd33cfwK+vdnibn9+QcD1gVjGtnVVYNs6AAAAxLz8fdKDTSot0jL3VXlDMF749f0DlRjPuCNiWw7b1gEAAAAIicRaVRa5IYhR+kO1veddXfTM8pC0BUQ7EnoAAAAAtuXW+H0a/mHuSHgjZPdYlfWrxryxJmTtAdGKhB4AAACAbUldhkiXvVnhtbraY71PjJ+uU91rdF3cf5TluUzNXf4vfPfWJz/oQGFR0PEC0Sw+3AEAAAAAcA6XJ0Vqe2aF19Z4Dq6IP1JzSz8vSbpNF+dN0EpvB7/u1ebud/X88B7q16FREBED0YuEHgAAAEDVLnlV+nKm9IcbA6r+RtLkMsepua/bqnf19I/VtG4NLRt3ekD3BaIZU+4BAAAAVK3DedLQF6SkOiFpLtX1o+2yP+z+TT/tyQvJfYFoQkIPAAAAoNotTrrder7ervQH5rNfPXAYEnoAAAAA/jv3yZA0syzpJttl0+49+Fw+ABJ6AAAAAIHocZV09w7phOFBNdPU9bMkr+3yhUX2ywLRzuX1evn/iErk5OQoJSVF2dnZSk5ODnc4AAAAQGQpLJAmNwiqiSKvS25XcVqSmvuaSVN8lu3eop6G9W6hlg1rqWuzukHdF3B6Hsoq9wAAAAACFxd8SlGSzBtZnssrXQV/9be/Wq8SL4zoodPbs60dYhNT7n3IzMxUWlqa0tPTwx0KAAAAENmSm/q+NmxmQE3aXTDvqpc+1q69rICP2ERC70NGRobWrVunVatWhTsUAAAAILJd+Leyx8edcfBzXFKZS9kDnw757XvcP199H16ovILCkLcNRDISegAAAADBaXJ82eNBf5X63yd1vUxq8QcVHZLUp9Stb7vZ++OfV3PXDltlv//1N7W7Z479mIEoQEIPAAAAIDhJdcoe1z5K6nurdOFfJZdL7lpHHbzWuIvtZq+IX6AlSbfpkfjDZgBU4p5Zn9suCzgdCT0AAACAI8sk90bHwVJKU+nG1dLYTbarXxz/vu2yr374nbb+sj+QKAHHIaEHAAAAcGSlj5IyVkqDny0+bnhc8Si+H3q51tsue9Iji/yNEHAkEnoAAAAAIfOzKtgb3uWSjmpXbou7vTd8Vvp5X8dLK213RtJkdXd9ZTuOfXkFLJKHqEdCDwAAACBkaiXZ35fe7Tn47P1v6TdWWf7fSffZbrvjxLnWInl7cg/YrgM4DQk9AAAAgJDxxNtPMWomHkz+G9arJ12zWLp2SaV14uTfqHvnSe/py23ZftUBnIKEHgAAAEDwatQrfm95sv067rhDPsdLTbpJx3TVnpYDfVb5LGmUsjyXlb7Ocq+s8jbnTFtqPybAQUjoAQAAAATPjK73u1c653H7dRJrSSfdLvW5UarTqPR0nXan+qxSy5VX5viZxKlqqKpH4Jdv3mU/LsAhXF6v1xvuICJZTk6OUlJSlJ2dreTk5HCHAwAAAES/D5+R5vyf39XuOTBSrxae4fP6/27qq05NU4IMDoicPJQRegAAAACRpdOQ4vfWp/tV7f6EFyu9fu5flir3ACvfI3qQ0AMAAACILGaP+rt3SFe8JTXq7FdV81y95HsS8sV/WxGCAIHIQEIPAAAAIPIkeIr3r7/sn1LaIL+qLky83ee1z77PVmERTx0jOpDQAwAAAIhcKcdKF0/3q0or9/ZKr7ef8G6QQQGRgYTeh8zMTKWlpSk9PT3coQAAAADwk5l6vyFpeIXXDhR6rWfphz3/kVLHva2vtu+xVsHn+Xo4DavcV4FV7gEAAIAI8OFfpTnj/K62qaiJBuY/pAOKt1X+gztPU7P6NQMIEAgdVrkHAAAAED26DSt7nFBLRWc9XGW149zbtNFzpe3bnPTIImuk/rd8RusR+ex9TQUAAAAA4ZRUu/TjzpOn6OhTr5XbW2R7v3qXiuS1OZ7ZfsKc0s9bppwtl1mcD4hAjNADAAAAcJSj6yVL7jipqMB2nbPcqwK6V8vx74inlBGpSOgBAAAAOIsrrvg9oYbU6zrphOHSqAVSpyE+q/w18c8aGz8j4KR+Z05uoNECRwwJPQAAAADHTr/XwIel86dJx/aQhr5QabUb42crSfkB3bLngwsCqgccSST0AAAAAJzhjMlS2gVSu7MDbuKtxIkB13168aaA6wJHAtvWVYFt6wAAAAAHmZRSZZHU3NeDvs3Ku/rp6GRP0O0AFWHbOgAAAACoQB/3l2VWvzeOde2U5PVrCn6vB+erqIjxUYQPCT0AAACA6DTwkQpP/yPxAes9y3OZtniusN6XJt2qLM/lStZe283vyMlTq7veCVm4gL9I6AEAAABEjzP+VPzeO0Pqda3PYiaJr8hnnmvUQNl+JfZf/JDtf5xACPAMfRV4hh4AAABwEJPe/Jol1UuVXC599LcM9frx1YCb65D7gn5T1c/KP3dlD/VPaxTwfYBD8Qw9AAAAgNjjckn1Wxa/S+rWtFZQza33XGWr3KiXP1bquLeDuhfgLxJ6AAAAAFErMfmooNsw0/MTdcBW2dXf/hr0/QC7SOgBAAAARK/eN0hd/qgDNYJL7L/2DLdVbshfl6uQle9RTUjoAQAAAESvxFrS4L8pocvQoJuyu1Bea1a+RzUhoQcAAAAQ/U6+I+gmzAr4ryfcb03B7+laX2lZ9qdHdSChBwAAABD9ajWQ7t4edDN/iFtnvb+RNNlK7I9Wxc/Mm/3pzYZibCqGIyn+iLYOAAAAAJEioYbPS96EWnId2Od3kys9GTou92UVVJBatRx/cOr9P0b3Vp/WDfxuH6gMI/QAAAAAYp6r93X6Mm3MwRMnjbVdd5PnyirLXPrsh1qV9Uug4QEVIqEHAAAAEJsm7JLu2CwNfUE65f/UqmXrg9f6TfCrKTP9vrF+rrTMRc+s0OXPfaj9+QWBRgyUQUIPAAAAIDbFJUi1GkqdhkjxSapR75igmvvQc5PiVXmyvmzTz0q7dy7P1iMkSOgBAAAAxIzfXDV9X2x9utR3jDTk+eLjYbOk3hl+tW9n+n3J8/WshI9gkdD7kJmZqbS0NKWnp4c7FAAAAAAhsjmxne+LLpfUf6LU+fc961ufJp31oJSx0q97DHIvtVWu/YQ5frULHI6E3oeMjAytW7dOq1atCncoAAAAAELEK5f/lY6q5EuACkxNfNpWufzCIqbeIygk9AAAAABiRosGtRRJ/rxgo7b+sj/cYcChSOgBAAAAxIzkJm2Cql/Q7jzpqveKt7Ubt7XSVe9Pcn+mifHTdVHcYtVRxUn71PkbddIjizQoc1lQcSE2xYc7AAAAAACoNv0mSgX5UtdLAqoeH58gNe9V/KrCK4kPlX5+NOHv6pz7nPao4kX51mzdrdRxbyvroXMCiguxiRF6AAAAALGjRl1pUKbU8uTA6jdsG/CtP/eMUhPtqrTML/vyA24fsYeEHgAAAACqcvU8qe9tUt9bK7y8u9NwW80s99xsTcf35YTJ8wIOEbGHhB4AAAAAqtKsp9R/kpRQo3yif/HLqtu0vV/N9XKt93ntrU++DzRKxBgSegAAAAAIJtFPu0BqO8CvajOSJusY/VzhtTFvrNXrH30XogARzUjoAQAAACBYDVpLt3/lV5UVnpt8Xrtr5uchCArRjoQeAAAAAEKhTmPpmsV+VvL6vLI3ryDokBDdSOgBAAAAIFSadPOr+IakEVqQeLviVFjuWqeJc0MYGKIRCT0AAAAAhInHdUCt3T9qs2eYLoqreHR//FufWXvU3/7GWr2yIks/7P6t2uNEZHJ5vV7fczygnJwcpaSkKDs7W8nJyeEOBwAAAECkm5Ry8PM5T0gpzaTXL7JVtWXuq/LaGHe986x2uuHU44KJElGQhzJCDwAAAABHSvrVUtszbRdfk3SNrXKPzPlKa7fu1k978oIIDk4XH+4AAAAAACAa5SY1lKfkoGl36YfVVdZJce1XE+3SNjWssuwFmctKP799c191bHLIzADEBEboAQAAAOAI8NSsffBg5Bzp5jXSjR9XWW+552bV1R6/7nXOtKWBhAiHI6EHAAAAgFAa+qJUt7l00UsHz8UnSvVbSg3bSBN2VdnEGs+1ft/WLJy3+ttf/a4H5yKhBwAAAIBQ6jRYuvVz31vYxSUcsVsP+evyI9Y2Ig8JPQAAAABEoKFx7wdUr/vkeSGPBZGJhB4AAAAAqtn2E8ZUWeaxhL9Z7z1d65XlucxK8Gcn3qNB7sqfl/95X75u/een1hT8TTv9exYfzkJCDwAAAADVrHH9Q/YW73Ojz3JdXZv0RtLk0gS/q/sbTU18Wme7P6y0/Vlrtlnv/Z9YYiX2v+7LD1XoiCAk9AAAAABQ3dyH7CA+4AH9NnJhhcVmJ91b4fmnE6fpGP2sJxMy9deEJxWvgkpv123yPBUVeYOLGRGHhB4AAAAAqtsJw6WG7aQTby0+TqrjdxMrPDfpwrhlGhi3Sps8V1ZZvtVd7wQSKSIYCT0AAAAAVDdPsnTjSumM+6zDGskNg27SPGd/rGtnpWX+PH+jvF5G6qMFCT0AAAAAhJsnJSTNLE36fcTfhyfnf62W499RfkFRSO6H8CKhBwAAAIBwc8dJ43+Qd8yGoJu6IW52lWXa3vNu0PdB+JHQAwAAAEAkSKotV80GQTdzZ8IMPZbwjDUFf4h7ic9yv7DyveOR0AMAAABApIhPlM5+LOhmhsYVJ/KPJxYn9t1cG8uVOWHyvKDvg/AioQcAAACASNJ9ZMibnJk0UVL5xfBmfvp9yO+F6kNCDwAAAACRJO6QPeorcvm/A2o2y3N5uXO3zVgbUFuIDFX0FAAAAABA2GWskt5/qHj/+lanBNzM7fFv6M8Fg1VwSCqYOu7t0s/D+7TQfRd0CjpcVA9G6AEAAAAgwvza8pyDB6MWSEe1lYa+UC6ZL/zDrSqK99hu96b4WdrkuVLNXDsqvD59xbdlEnxENhJ6AAAAAIgwv7a/7OBBncY+y8Ud1UbuaxZL3Uf41f4HSbdVev2K5z7SvrwCv9pE9SOhBwAAAIAIU1TjkO3raleQ0HcaItU6WupwvnR0B+m8P0vjvvPrHpuTyj9TX2Lppl3qOHGutmfn+tUmqldMPEOfmpqq5ORkud1u1atXT4sWLQp3SAAAAADgm+vQz4ce/G7I85K3SHLHHTznSfHrFnEurxJUoAOVpIW9pyxQ1kOHTP9HRImZEfrly5drzZo1JPMAAAAAIl583CGpmquCtM0k+Ycm8xX4rXnVi+fNSfy/KsvwTH3kipmEHgAAAACcokWDOpWP0NtQ448vSWc+IP3xdZ9lWrt/1DnuD5XluUx93F8qRXsrLFdYVH4Pe4Rf2BP6JUuW6LzzzlOTJk3kcrk0a9ascmUyMzOtafMej0e9evXSypUr/bqHafeUU05Renq6XnvttRBGDwAAAACh5zLPxbc+Xep8sV/18t2HrHhfs770hxul9pVPmc9MnGa9/yPxAa31XKOaKv/cfOu73vErDsTIM/T79u1T165dddVVV2nw4MHlrs+YMUNjxozRM888YyXzU6dO1YABA/TVV1/p6KOPtsocf/zxKigovwLje++9Z31RsHTpUjVt2lQ//vij+vfvr86dO6tLly4VxpOXl2e9SuTk5IT03wsAAAAAVTKj8sNm+l3N3esaacU0HWh9hhICvPU6z1VKzTUDoa5yU+/P7txYT1/ePcCWEWour9cbMXMnzEj6zJkzNWjQoNJzJok3I+tPPfWUdVxUVKRmzZrppptu0rhx4/y+xx133KGOHTtqxIiKt3WYNGmS7rvvvnLns7OzrYX1AAAAACBiFR6QspZKzXpJiTUPnp9UvGDegYYdlLBrve3m2uZOV34FXw2wUN6RZQaWU1JSqsxDwz7lvjL5+flavXq1NapewqxUb45XrFhhewbAnj17rM979+7VwoULrYTel/Hjx1v/0UpeW7duDcG/BAAAAACqQVyC1Pq0ssm80fwP1ltC+ki/mvvaM7zC8zM//T7wGBE9U+4rs2vXLhUWFqpRo0ZlzpvjDRs22Gpjx44duvDCC63Ppq3Ro0dbI/6+JCUlWS8AAAAAiBpX/Fva8aXUtLu2f/2xGm9+w3ZVs2Beam7ZhfVum7FWF3Y79ggEiqhJ6EOhVatWWrt2bbjDAAAAAIDwMSP2zYoHNhsPe1a/vrRP9bKC247uuldW65lhPE8fThE95b5hw4aKi4uzRtkPZY4bN24ctrgAAAAAwMnqXfmKX+W/SLpKUtnl1+Z8uV3Dnv8oxJEhahL6xMREde/eXQsWLCg9ZxbFM8d9+vQJa2wAAAAA4FjuOL+K13blKstzuT5NuqbM+Q827gpxYHBUQm8WqluzZo31MrZs2WJ9/u6776xjs2Xds88+q+nTp2v9+vW6/vrrrYXuRo70bzEHf2VmZiotLa3S5+0BAAAAIBrsiyteBb8q9Vx7rWfqD7U9u/y+9YiRbesWL16s0047rdz54cOH66WXXrI+my3rHn30UW3fvt3ac37atGnWdnaRtF0AAAAAADhJzp9aKLlod/HBvb/Im7dHrodb2Kp7Yd59+tTbpvSYbezCk4eGPaGPdCT0AAAAAKLR5w+drs65q4sPJmVbb4WT6ilORbbqH7ry/cf39FfNxDil3TtXl/ZsrjFntFVhkVeNUzxHJvgol2MzD436Ve4BAAAAAOVVNLJ7oGkvxf2wwlb9U9xr9X5RV+tzj/vnl57/x8rvrJdxdd+WmnBuWogiRsQ9Qw8AAAAAqH57O15hvX/u7lB6znPJi1L6KOmGj7R9ZOUr2E9PfFhLk26utMzzS7foqYUbxcTwI4Mp91Vgyj0AAACAaGRSwc/XfqzU4zoouXbtCst8NPMp9Vp7t+2p91Xp36GRnhvew+9YY02OzTyUEXoAAAAAiEEul0tdjk/3mcwbPc8dVWU7/dy/P4dvw/z1O/T1jj22y6NyJPQ+sG0dAAAAgFjnSqh6UbvnEx/3q80zn1yi1HFvBxEVSrAong8ZGRnWq2SqAwAAAADEso+K2quXe0OF19q5vtM5cR/q5vhZZc53zn1Oe1Szwjq5BwrlSYg7IrHGCkboAQAAAABVOnDqBK0a/k2F1+YmjSuXzBufe0apm2tjhXXaT5gT8hhjDSP0AAAAAACfXi3op5au7UpL76f6dWr4XX9m0sTSzy8WDNB9BcNLj0+YPE+/7Mu3Pmc9dE6IIo4djNADAAAAAHw6965/quXtCwJK5g83Mn6uklScwBslybxhnqv/+5LNQd8jlpDQAwAAAAB8qlszUU3qBp/Ml/jKM0JZnssqvPbgOxv0/a/7Q3avaEdCDwAAAACwbVtSq5C009O1vsLzfR9eFJL2YwEJPQAAAADAtmPGLte+a1YE3c4bSZN//+Qtd63vwwu1Iyc36HtEOxJ6H9iHHgAAAADKcyXUUK0maTotz7/95ytipt5neS633l0qKj3//a+/qdeDC/TInIq3yUMxl9frLf91CEqV7EOfnZ2t5OTkcIcDAAAAABHhs+93q8tzLULebmru62WOY3H1+xybeSgj9AAAAAAAv3U5tq5uyb/B5/VR+bcH1O7hC+aZ1e9RMRJ6AAAAAEBAGvS+oszxpfl3l35+YNydQbRcdiL5da+sDqKt6BUf7gAAAAAAAM6Ucfpx0icHj68dPkL/mverElIa64JkT+n5bd6G2pnYTMcf+NRWu+a5eqNd7kvKU6LmfLk99MFHARJ6AAAAAEBAGtROKv1c4HXr1HZHS+2mlCv3k7uhchr3krbaS+gP3bO+5Jn6bbt/089789W2cW0lxceFIHrnI6EHAAAAAARtboNhOnz5usF5kzQ6/m39r3GGHrl0gOa+4tbL25roNdc9fk6/d+kPDy0sc/bTCWeoXq1ExTKeoQcAAAAABK1WQvlz994wUu+mPaq7Lj9LtWrW1IBrp+iViTdqcWFX2+2a6fcp2lvufLfJ8xTrm7aR0AMAAAAAghZ32EJ2xvHN6mrapd3UtG6N0nNut0vHuH4uPX6z4OQq217ruabC8y3Hv6PLnv1QhUWxmdiT0PuQmZmptLQ0paenhzsUAAAAAIhYHxR2st7XNrS/X/ze9heXfq7de7itOm4VyaWicueXb/5Zre96R7GIZ+h9yMjIsF45OTlKSUkJdzgAAAAAEJGuPDBOtQ7k6tKazW3XKUyoWfp5wNlD9O99e5V8bHudMW+AzzrfeMpukdct9xn9quTS4+c++EajTmqlWMIIPQAAAAAgYF65tVc11SO1vu063zY6w1oV//3CLtYU/CGXjNAZJ/bWR0Xtbbfxqec6xamw9Pj+t9fH3DP1jNADAAAAAAL2/h2nat22HJ2Z1sh2nebHNlPHvBeUpwRlHXI+OdElFdi/92bPMOu9c+5z2qOaOmvqB5p7W9XP5EcLRugBAAAAAAFr0aCWBnY+Ri6Xy3adXq0a6JE/9tT/bjqpzPk3a15ivb9V2NevGD73jLLev9qxR9/9vF+5Bw6O3EczlzfW5iT4qeQZ+uzsbCUnH3w+AwAAAAAQWv9e/b0efHOJ2rduqdd+OMvv+qm5r5d+3vzg2Ypz2/+SwYl5KCP0AAAAAICIMPiEpnr91vP04shempj8J7/rN3PtKP1sVr7fmZOraEZCDwAAAACICGbafrvGdZQY79YdGRk6teZbftX/IOm2Msc9H1ygaEZCDwAAAACIOLWT4jXrxpP0i7e2X/WyPJdZrxLR/JQ5CT0AAAAAICLVrZmoJwouCqhulucyJSlfLce/E7WL5JHQ+5CZmam0tDSlp6eHOxQAAAAAiFnNOxzMyYblj7P2qn+94HRbdb/yjLDe20+Yo2hEQu9DRkaG1q1bp1WrVoU7FAAAAACIWad0bVP6+eZRV+vFNk+rRsuefrez6KudijYk9AAAAACAiJUQdzBt7d6ioZ4Z1l1qWbx//T5vkq2p9/WUo5EvRt9gLQk9AAAAACBi5ddroyWFnTWr8A9y/b6v/N6azfSH3GlKz/urMvJvrrKNTz3XReUoPQk9AAAAACByuVy68sB43XrgRmtbO+Oczsdop/sopbdtpprdhio19/Uqm2nj+j7qRunjwx0AAAAAAAC+tGxYS7US46wV70vUr5WoL/80QIlx7tIkX19W3s68pDuVmvuaogkJPQAAAAAgYiXFx+mTe89QXEnifsj5Eo8M7VJlQm/cHW8S+nMVLZhyDwAAAACIaCZ5jz9kcbzDuQ5L9n0ZHf+ONu3cq2hBQg8AAAAAcLypBYOt98F5k9Q2d7rPcv2feF/Rgin3AAAAAADHm1owVJkFg3SgyjTXq2jBCD0AAAAAICocULw6N03Rhsln+SxzXdx/FS1I6AEAAAAAjnf7GW2t90nnd5QnIU43+NifflT8O4oWTLkHAAAAADjeTf3a6NpTWisxvnjcenH8iUrLPV5rk0YrwVVYWq6hK0fRghF6HzIzM5WWlqb09PRwhwIAAAAAsCHx92Te8Hql/fJoQsFIRSsSeh8yMjK0bt06rVq1KtyhAAAAAAD8NLBTY+v9n4Wna0z+dYpGJPQAAAAAgKgzeVAnPTS4s/V5ZlFfRSMSegAAAABA1KmVFK8/9myum/u1kTdKU9/o/FcBAAAAACDpln5tFK1I6AEAAAAAUSvO7dL/ndVebxUWT7t/ocD3HvVOQ0IPAAAAAIhqo05qqV+8dazPuUpUtCChBwAAAABEtYQ4t4p+T3/d8ipakNADAAAAAKJekVzWu0tFihbx4Q4AAAAAAIAj7cmCofpLwYVKSEzStYoOjNADAAAAAKLesL7ttE81dEmf4xQtSOgBAAAAAFHPVTzjXlH0CD0JPQAAAAAg+rlKM/roQUIPAAAAAIADkdADAAAAAOBArHIPAAAAAIh6o09qpYu6H6u6NRMVLUjoAQAAAABR76g6SdYrmjDl3ofMzEylpaUpPT093KEAAAAAAFCOy+v1RtGi/aGXk5OjlJQUZWdnKzk5OdzhAAAAAACiXI7NPJQRegAAAAAAHIiEHgAAAAAAByKhBwAAAADAgUjoAQAAAABwIBJ6AAAAAAAciIQeAAAAAAAHIqEHAAAAAMCBSOgBAAAAAHAgEnoAAAAAAByIhB4AAAAAAAcioQcAAAAAwIFI6AEAAAAAcCASegAAAAAAHIiEHgAAAAAAByKhBwAAAADAgUjoAQAAAABwIBJ6AAAAAAAciIQeAAAAAAAHIqEHAAAAAMCBSOgBAAAAAHAgEnoAAAAAAByIhB4AAAAAAAciofchMzNTaWlpSk9PD3coAAAAAACU4/J6vd7yp1EiJydHKSkpys7OVnJycrjDAQAAAABEuRybeSgj9AAAAAAAOBAJPQAAAAAADkRCDwAAAACAA5HQAwAAAADgQCT0AAAAAAA4UHy4A4h0JZsAmFUGAQAAAAA40kryz6o2pSOhr8KePXus92bNmoU7FAAAAABAjOWjKSkpPq+zD30VioqK1LZtW61evVoul6vK8unp6Vq1alVQZQK5br7BMV86bN26tdJ9CiOBnf9GkXKPQNrxp47dssH0Gaf3l+roM+HsL/7WO9I/Y3xdc1KfccrPmEjpL3bK8TMmNn4n2S3Pzxh+xvhTNtgyTv8Z45T+Emg7Tvid5NQ+Y9J0k8w3adJEbrfvJ+UZoa+C+Y+XmJhY6bcih4qLi6uyk1RVJpjr5nykd1I7/40i5R6BtONPHbtlg+kTTu8v1dFnwtlf/K13pH/GVFXXCX3GKT9jIqW/2CnHz5jY+J1ktzw/Y/gZ40/ZYMs4/WeMU/pLoO046XeSE/uMnRyURfFsyMjICGnZqsoEez3SVUf8obpHIO2Eur/YKVfZdaf3l+r4N4Szv0Tazxj6S/XdI1L6i51y9JnY+J1ktzz9hZ8x/pQNtozT+4xT+kug7fA7KfT8/Tcw5T5KmGkk5huc7OzsiP/WCeFHf4G/6DPwB/0F/qLPwB/0F/grJ4r7DCP0USIpKUkTJ0603oGq0F/gL/oM/EF/gb/oM/AH/QX+iuY+wwg9AAAAAAAOxAg9AAAAAAAOREIPAAAAAIADkdADAAAAAOBAJPQAAAAAADgQCT0AAAAAAA5EQh8D/ve//6ldu3Zq06aNnnvuuXCHAwe48MILVa9ePQ0dOjTcoSDCbd26VaeeeqrS0tLUpUsXvfnmm+EOCRFu9+7d6tGjh44//nh16tRJzz77bLhDggPs379fLVq00NixY8MdChwgNTXV+p1kfs6cdtpp4Q4HEW7Lli1WPzF/y3Tu3Fn79u2Tk7BtXZQrKCiwOueiRYuUkpKi7t27a/ny5WrQoEG4Q0MEW7x4sfbs2aPp06frX//6V7jDQQT78ccftWPHDuuPpu3bt1s/Y77++mvVqlUr3KEhQhUWFiovL081a9a0/mgySf3HH3/M7yVU6u6779amTZvUrFkzPfbYY+EOBw5I6L/44gvVrl073KHAAU455RTdf//9Oumkk/TLL78oOTlZ8fHxcgpG6KPcypUr1bFjRzVt2tT6oTZw4EC999574Q4LEc6MuNapUyfcYcABjjnmGCuZNxo3bqyGDRtavwwBX+Li4qxk3jCJvRlXYGwBldm4caM2bNhg/Q0DAKH05ZdfKiEhwUrmjfr16zsqmTdI6CPckiVLdN5556lJkyZyuVyaNWtWuTKZmZnWN5Eej0e9evWykvgS27Zts5L5EubzDz/8UG3xw3l9BrEllP1l9erV1uirGUFD9ApFnzHT7rt27apjjz1Wd9xxh/VFEKJTKPqLmWY/ZcqUaowaTu8zpp4ZdU1PT9drr71WjdHDaf1l48aN1qCnaeOEE07Qgw8+KKchoY9wZjqi+aPHdMSKzJgxQ2PGjNHEiRP1ySefWGUHDBignTt3VnusiAz0GYSjv5hR+SuvvFJ///vfqylyOLnP1K1bV2vXrrWeW3z99detxzYQnYLtL7Nnz1bbtm2tF2JDKH7GLF261PqS+T//+Y+VoH322WfV+C+Ak/pLQUGBPvjgAz399NNasWKF5s2bZ70cxTxDD2cw/3PNnDmzzLmePXt6MzIySo8LCwu9TZo08U6ZMsU6XrZsmXfQoEGl12+55Rbva6+9Vo1Rw2l9psSiRYu8Q4YMqbZY4dz+kpub6z3ppJO8L7/8crXGC2f/jClx/fXXe998880jHiuc2V/GjRvnPfbYY70tWrTwNmjQwJucnOy97777qj12OPdnzNixY70vvvjiEY8Vzuwvy5cv95555pml1x955BHr5SSM0DtYfn6+9e1j//79S8+53W7r2HzDZPTs2dNaFMRMs9+7d6/effdd61spxCY7fQbwp7+Y358jRozQ6aefrmHDhoUxWjilz5jReLPoppGdnW1NlzQ7sSD22OkvZqq92U0jKyvLWgxv9OjRuvfee8MYNSK9z5gR25KfMeZv34ULF1rrSSH25NvoL+axDDNa/+uvv6qoqMj6ndShQwc5ibOe+EcZu3btsp5XbdSoUZnz5tgsHmOYRR0ef/xxaysG00nvvPNOVhKOYXb6jGF+0JnpsOaXonnG1WxF1qdPnzBEjEjvL8uWLbOms5ntgUqeW3vllVesbV8Qe+z0mW+//VbXXHNN6WJ4N910E/0lRtn9nQT402fMl4Zm+13DlDVfApmkDbFnl81cyTyWcfLJJ1u/k84880yde+65chIS+hhw/vnnWy/Arvnz54c7BDhE3759rS8LAbvMzLE1a9aEOww4kJkNBFSlVatW1qAEYJfZQcPJu2gw5d7BzKrAZvufwxcTMsdm+yjgcPQZ+IP+An/RZ+AP+gv8RZ+BPxrGSH8hoXewxMREde/eXQsWLCg9Z0bKzDHTo1ER+gz8QX+Bv+gz8Af9Bf6iz8AfiTHSX5hyH+HMYh6bNm0qPTZb/JipivXr11fz5s2tbRiGDx+uHj16WNMYp06daj33PHLkyLDGjfChz8Af9Bf4iz4Df9Bf4C/6DPyxl/7CtnWRzmwdZv5nOvw1fPjw0jJ/+ctfvM2bN/cmJiZaWzN8+OGHYY0Z4UWfgT/oL/AXfQb+oL/AX/QZ+GMR/cXrMv8n3F8qAAAAAAAA//AMPQAAAAAADkRCDwAAAACAA5HQAwAAAADgQCT0AAAAAAA4EAk9AAAAAAAOREIPAAAAAIADkdADAAAAAOBAJPQAAAAAADgQCT0AAAAAAA5EQg8AAELO5XJp1qxZR/QekyZN0vHHH39E7wEAQCQjoQcAwIF++uknXX/99WrevLmSkpLUuHFjDRgwQMuWLVO0mDlzpnr37q2UlBTVqVNHHTt21K233lp6fezYsVqwYEFYYwQAIJziw3p3AAAQkCFDhig/P1/Tp09Xq1attGPHDiu5/fnnnxUNzL/lkksu0QMPPKDzzz/fGvFft26d5s2bV1qmdu3a1gsAgFjFCD0AAA6ze/duffDBB3r44Yd12mmnqUWLFurZs6fGjx9vJb8lnnjiCXXu3Fm1atVSs2bNdMMNN2jv3r2l11966SXVrVtX//vf/9SuXTvVrFlTQ4cO1f79+60vClJTU1WvXj3dfPPNKiwsLK1nzk+ePFmXXnqp1XbTpk2VmZlZacxbt27VxRdfbN2vfv36uuCCC5SVleWz/H//+1+deOKJuuOOO6zY2rZtq0GDBpW5z+FT7k3Sf/jLxFriiy++0MCBA60vARo1aqRhw4Zp165dfv7XBwAgcpDQAwDgMCUj0+YZ9by8PJ/l3G63pk2bpi+//NJK0BcuXKg777yzTBmTvJsy//znPzVnzhwtXrxYF154od555x3r9corr+hvf/ub/vWvf5Wp9+ijj6pr16769NNPNW7cON1yyy1lRs8PdeDAAetxADNt3nwRYR4LMPGfddZZ1iyDiphHCEzcJgm368cffyx9bdq0Sccdd5xOPvnk0i9BTj/9dHXr1k0ff/yx9W81sxrMlwwAADiVy+v1esMdBAAA8M+///1vjR49Wr/99ptOOOEEnXLKKfrjH/+oLl26+KxjkvLrrruudFTajNCPHDnSSn5bt25tnTPXTRJvkt2S6ewm8TYj3c8884x1bD536NBB7777bmnb5t45OTnWlwCGGR03z8CbUfVXX31V999/v9avX2+dN0wib0brzZcSZ555ZrlY9+3bZyXbpj0zA8E8S2/KXX755daaASUj9Kb+mjVrytQ1f9qYRxK+++476wuEGjVqWPc3n+fOnVta7vvvv7dmLnz11VfWDAAAAJyGEXoAABzIJKzbtm3Tf/7zHyvhNiPrJrE3SXqJ+fPnq1+/ftaUeDM6bqaYm2fszah8CTPNviSZN8xUdJOwH/psujm3c+fOMvfv06dPuWOTsFdk7dq11pcGJoaS2QVm2n1ubq42b95cYR0zlf/tt9+26t1zzz1Wndtvv916tODQ+Cty1113acWKFZo9e7aVzJfEsGjRotL7m1f79u2ta75iAAAg0rEoHgAADuXxeHTGGWdYrwkTJmjUqFGaOHGiRowYYT2ffu6551or4ZuF5UwCvXTpUl199dXW6LhJ5I2EhIQybZoR9IrOFRUVBRyneW6/e/fueu2118pdO+qooyqta75sMC/zb7v77rutkfQZM2ZYMwsqYmYDPPnkk9YXHOaLjENjOO+886x1Bw53zDHHBPTvAgAg3EjoAQCIEmlpaaV7v69evdpKwh9//HHrWXrjjTfeCNm9Pvzww3LHZhp+RczMAZOEH3300UpOTg74nmbmgPkiwkzHr4gZlTeJv3nm30zRPzwG85iCaSM+nj9/AADRgSn3AAA4jJk2bxZ4M6PRn332mbZs2aI333xTjzzyiLV6vGEWhDOL0f3lL3/RN998Yz0XX/IMfCiYhe3M/b7++mtr5Xlzf7MwXkXMc+8NGza0YjPPsZt4zQi6WT3fPMdeEfN8vFnAz5Qz5c3ie1dddZX1bzIzEg63fft2azE/8yy/WYDPHJvXTz/9ZF3PyMjQL7/8Yq3Mv2rVKmuavXme3oz0H7qCPwAATkJCDwCAw5jnv3v16mVNLTeruHfq1Mmacm8WyXvqqaesMmYFerNtnZlibq6b6e5TpkwJWQzmeXazWrxZNd4sOGfuZRLpiphR9SVLlqh58+YaPHiwNZJvpv6bZ+h9jdibRf7MFxFXXnml9ay72W7OJOjvvfeetY3d4TZs2GAt5GdW8zdT6Ete6enp1vUmTZpYX0KY5N0srme287v11luthflKZjAAAOA0rHIPAAD8Yqatm2TYvAAAQPjwlTQAAAAAAA5EQg8AAAAAgAMx5R4AAAAAAAdihB4AAAAAAAcioQcAAAAAwIFI6AEAAAAAcCASegAAAAAAHIiEHgAAAAAAByKhBwAAAADAgUjoAQAAAABwIBJ6AAAAAADkPP8PrR2Wk4333uwAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 1200x1000 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
-      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
-      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
-      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
-     ]
     }
    ],
    "source": [
@@ -223,7 +225,7 @@
     "fig, ax = pyplot.subplots(nrows=1, ncols=1, figsize=(12,10))\n",
     "ax.plot(np.arange(1, n+1), np.sqrt(lat_discs1), label=\"Kuo Lattice Discrepancy\")\n",
     "ax.plot(np.arange(1, n+1), np.sqrt(lat_discs2), label=\"New Lattice Discrepancy\")\n",
-    "# ax.plot(np.arange(1, n+1), np.sqrt(kron_discs), label=\"Kronecker Discrepancy\")\n",
+    "ax.plot(np.arange(1, n+1), np.sqrt(kron_discs), label=\"Kronecker Discrepancy\")\n",
     "ax.set_xscale(\"log\")\n",
     "ax.set_yscale(\"log\")\n",
     "ax.legend()\n",

From 9033e5990dc5fdd725910dfec60d96ec3317004a Mon Sep 17 00:00:00 2001
From: sou-cheng-choi <sou.cheng.choi@stanfordalumni.org>
Date: Fri, 17 Jul 2026 10:10:44 +0800
Subject: [PATCH 10/29] Add booktest file

---
 test/booktests/tb_lattice_kronecker_methods.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)
 create mode 100644 test/booktests/tb_lattice_kronecker_methods.py

diff --git a/test/booktests/tb_lattice_kronecker_methods.py b/test/booktests/tb_lattice_kronecker_methods.py
new file mode 100644
index 000000000..c10cc0473
--- /dev/null
+++ b/test/booktests/tb_lattice_kronecker_methods.py
@@ -0,0 +1,12 @@
+import unittest
+from testbook import testbook
+from __init__ import TB_TIMEOUT, BaseNotebookTest
+
+class NotebookTests(BaseNotebookTest):
+
+    @testbook('../../demos/lattice_kronecker_methods.ipynb', execute=True, timeout=TB_TIMEOUT)
+    def test_lattice_kronecker_methods_notebook(self, tb):
+        pass
+
+if __name__ == '__main__':
+    unittest.main()

From 15ee863c9ea855f7f80f47aa943b6ee86857f2c6 Mon Sep 17 00:00:00 2001
From: sou-cheng-choi <sou.cheng.choi@stanfordalumni.org>
Date: Fri, 17 Jul 2026 10:16:33 +0800
Subject: [PATCH 11/29] Add imports in __init__.py

---
 qmcpy/discrete_distribution/__init__.py           | 5 ++---
 qmcpy/discrete_distribution/kronecker/__init__.py | 3 ++-
 qmcpy/discrete_distribution/lattice/__init__.py   | 1 +
 3 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/qmcpy/discrete_distribution/__init__.py b/qmcpy/discrete_distribution/__init__.py
index 182a16516..d5f08e3a1 100644
--- a/qmcpy/discrete_distribution/__init__.py
+++ b/qmcpy/discrete_distribution/__init__.py
@@ -1,10 +1,10 @@
 from .abstract_discrete_distribution import AbstractDiscreteDistribution
 from .iid_std_uniform import IIDStdUniform
-from .lattice import Lattice
+from .lattice import Lattice, lattice_vector_wssd_search
 from .digital_net_b2 import DigitalNetB2
 from .digital_net_any_bases import DigitalNetAnyBases,Halton,Faure
 from .mpmc import MPMC
-from .kronecker import Kronecker
+from .kronecker import Kronecker, kronecker_search_march_2026
 
 DiscreteDistribution = AbstractDiscreteDistribution
 _DiscreteDistribution = AbstractDiscreteDistribution
@@ -12,4 +12,3 @@
 DigitalNet = DigitalNetB2
 Net = DigitalNetB2
 NetB2 = DigitalNetB2
-
diff --git a/qmcpy/discrete_distribution/kronecker/__init__.py b/qmcpy/discrete_distribution/kronecker/__init__.py
index 6d0653bb3..d88272a27 100644
--- a/qmcpy/discrete_distribution/kronecker/__init__.py
+++ b/qmcpy/discrete_distribution/kronecker/__init__.py
@@ -1 +1,2 @@
-from .kronecker import Kronecker
\ No newline at end of file
+from .kronecker import Kronecker
+from .kronecker_search_methods import kronecker_search_march_2026
diff --git a/qmcpy/discrete_distribution/lattice/__init__.py b/qmcpy/discrete_distribution/lattice/__init__.py
index b57762ece..3eb626fd7 100644
--- a/qmcpy/discrete_distribution/lattice/__init__.py
+++ b/qmcpy/discrete_distribution/lattice/__init__.py
@@ -1 +1,2 @@
 from .lattice import Lattice
+from .lattice_vector_wssd_search import lattice_vector_wssd_search

From 081936cba3374caf9aa77c59bb06986e87bc1f4b Mon Sep 17 00:00:00 2001
From: sou-cheng-choi <sou.cheng.choi@stanfordalumni.org>
Date: Fri, 17 Jul 2026 10:18:29 +0800
Subject: [PATCH 12/29] Comment out global high precision

---
 .../kronecker/kronecker_search_methods.py                     | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
index bd6108b95..0a0cb7569 100644
--- a/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
+++ b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
@@ -1,7 +1,7 @@
 import numpy as np
 from sympy import gcdex, primerange, prime
 import time
-np.set_printoptions(precision=17)
+#np.set_printoptions(precision=17)
 #https://github.com/sympy/sympy/releases
 
 
@@ -176,4 +176,4 @@ def kronecker_search_march_2026(N, dMax, searchsize, kernel=None, coord_weights=
 # print(a)
 # print(wssd)
 # end = time.time()
-# print("Run time (seconds): ", end - start)
\ No newline at end of file
+# print("Run time (seconds): ", end - start)

From a48c42e7b777da09e6d32818f7e0741b603bc6d7 Mon Sep 17 00:00:00 2001
From: sou-cheng-choi <sou.cheng.choi@stanfordalumni.org>
Date: Fri, 17 Jul 2026 10:33:14 +0800
Subject: [PATCH 13/29] Fix unit test for demo

---
 demos/lattice_kronecker_methods.ipynb         | 136 +++++++++++++-----
 .../booktests/tb_lattice_kronecker_methods.py |   8 +-
 2 files changed, 102 insertions(+), 42 deletions(-)

diff --git a/demos/lattice_kronecker_methods.ipynb b/demos/lattice_kronecker_methods.ipynb
index 384db68ee..0ab60dcfe 100644
--- a/demos/lattice_kronecker_methods.ipynb
+++ b/demos/lattice_kronecker_methods.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "2e06ea48",
    "metadata": {},
    "outputs": [],
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 2,
    "id": "958e16e1",
    "metadata": {},
    "outputs": [],
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 3,
    "id": "c8eec507",
    "metadata": {},
    "outputs": [
@@ -74,15 +74,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "193.7028792871213\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\headw\\AppData\\Local\\Temp\\ipykernel_26420\\204085947.py:9: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  kron_wssd = np.float64(kron.wssd_discrepancy(n = n, weights = sample_weights, k_tilde = kron_k_tilde, gamma = coord_weights)) # compute the wssd\n"
+      "[193.70287929]\n"
      ]
     }
    ],
@@ -117,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 4,
    "id": "524e3b99",
    "metadata": {},
    "outputs": [
@@ -125,20 +117,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken for lattice vector wssd search:  9.876056909561157\n",
+      "Time taken for lattice vector wssd search:  4.327883005142212\n",
       "Searched lattice vector:  [     1 444567 406809  53917 411513 111013  57773  23363 278133 179399\n",
       " 145725 480145 365723 134199 361515 297163 315729 250703 322429 236947\n",
       " 508553 455183 148433  37975  46187 474063 490317  14811 417263 342641\n",
-      " 474417 269197 309749  29993 366775 433399 240621 375377  84847 232327\n",
-      " 214987 375079  32109 388283 153487 140919 390453 362317 413527 405689\n",
-      " 307801  95733 176459 498361 147739 178349 300557 427387 162217 127697\n",
-      " 183267 336879 314911 122203]\n"
+      " 269197 474417 309749  29993 366775 433399 240621 375377  84847 232327\n",
+      " 214987 375079  32109 153487 388283 140919 390453 362317 405689 413527\n",
+      " 307801 147739 176459  95733 498361 178349 127697 427387 162217 183267\n",
+      " 300557 336879 314911 122203]\n"
      ]
     }
    ],
    "source": [
-    "from qmcpy.discrete_distribution.lattice.lattice_vector_wssd_search import lattice_vector_wssd_search\n",
-    "\n",
     "# note that the search requires that the sample weights be w_n = n, so they are not customizable\n",
     "\n",
     "time_start = time()\n",
@@ -158,13 +148,95 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "09388fbc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time taken for Kronecker vector wssd search:  417.6621832847595\n",
+      "Searched Kronecker vector:  (array([0.61803399, 0.44322929, 0.22874783, 0.85854891, 0.09862349,\n",
+      "       0.13027022, 0.30100237, 0.49129871, 0.10752283, 0.93244982,\n",
+      "       0.08420257, 0.2563596 , 0.32164088, 0.19570311, 0.5898421 ,\n",
+      "       0.60631972, 0.02965074, 0.57696365, 0.29815947, 0.72586386,\n",
+      "       0.81107075, 0.6439515 , 0.07966279, 0.05997121, 0.09380327,\n",
+      "       0.64980017, 0.27700713, 0.74102903, 0.87941726, 0.56144415,\n",
+      "       0.11886968, 0.41924865, 0.54660185, 0.08176813, 0.48158459,\n",
+      "       0.25388801, 0.23265409, 0.54636109, 0.10474602, 0.16138721,\n",
+      "       0.31612105, 0.39305959, 0.31975094, 0.03629234, 0.37544416,\n",
+      "       0.05323235, 0.16550695, 0.95164815, 0.15079678, 0.24254269,\n",
+      "       0.29654601, 0.10189676, 0.03117397, 0.49020769, 0.40708275,\n",
+      "       0.31187689, 0.41786611, 0.84794106, 0.31750284, 0.29872605,\n",
+      "       0.11568039, 0.32747855, 0.1734749 , 0.40610889]), 126.80242919921875, array([2.94259096e-01, 1.05815272e-01, 5.93755625e-02, ...,\n",
+      "       5.26598765e-11, 5.25139932e-11, 5.26756416e-11]), array([[43., 39., 97., 88.],\n",
+      "       [19.,  8., 83., 35.],\n",
+      "       [61.,  6., 71.,  7.],\n",
+      "       [67., 11., 61., 10.],\n",
+      "       [97., 72., 31., 23.],\n",
+      "       [ 7.,  3., 23., 10.],\n",
+      "       [29., 28., 59., 57.],\n",
+      "       [41., 31., 37., 28.],\n",
+      "       [83., 69., 89., 74.],\n",
+      "       [ 7.,  1., 83., 12.],\n",
+      "       [11., 10., 43., 39.],\n",
+      "       [19.,  9., 59., 28.],\n",
+      "       [37.,  6., 31.,  5.],\n",
+      "       [97., 62., 61., 39.],\n",
+      "       [37., 20., 61., 33.],\n",
+      "       [ 2.,  1., 67., 34.],\n",
+      "       [41., 15., 71., 26.],\n",
+      "       [11.,  3., 37., 10.],\n",
+      "       [53., 45., 73., 62.],\n",
+      "       [43., 30., 53., 37.],\n",
+      "       [47., 38., 73., 59.],\n",
+      "       [23.,  2., 11.,  1.],\n",
+      "       [71., 53., 67., 50.],\n",
+      "       [ 5.,  3., 53., 32.],\n",
+      "       [61., 33., 37., 20.],\n",
+      "       [23., 18., 83., 65.],\n",
+      "       [23., 20., 31., 27.],\n",
+      "       [73., 51., 83., 58.],\n",
+      "       [23.,  9., 41., 16.],\n",
+      "       [ 7.,  5., 59., 42.],\n",
+      "       [61., 44., 43., 31.],\n",
+      "       [29.,  6., 53., 11.],\n",
+      "       [ 5.,  4., 61., 49.],\n",
+      "       [43., 40., 29., 27.],\n",
+      "       [17., 16., 67., 63.],\n",
+      "       [17., 10., 73., 43.],\n",
+      "       [29.,  6., 53., 11.],\n",
+      "       [ 7.,  2., 67., 19.],\n",
+      "       [43., 36., 37., 31.],\n",
+      "       [13.,  6., 41., 19.],\n",
+      "       [ 5.,  2., 13.,  5.],\n",
+      "       [31.,  8., 97., 25.],\n",
+      "       [ 3.,  2., 83., 55.],\n",
+      "       [23.,  3., 61.,  8.],\n",
+      "       [ 2.,  1., 37., 19.],\n",
+      "       [ 5.,  1., 31.,  6.],\n",
+      "       [79., 59., 83., 62.],\n",
+      "       [11.,  8., 73., 53.],\n",
+      "       [83., 74., 37., 33.],\n",
+      "       [11.,  8., 37., 27.],\n",
+      "       [ 2.,  1., 19., 10.],\n",
+      "       [ 3.,  1., 97., 32.],\n",
+      "       [79., 76., 53., 51.],\n",
+      "       [83., 38., 59., 27.],\n",
+      "       [19.,  5., 61., 16.],\n",
+      "       [13.,  5., 31., 12.],\n",
+      "       [67., 39., 79., 46.],\n",
+      "       [13.,  7., 41., 22.],\n",
+      "       [61., 13., 47., 10.],\n",
+      "       [ 5.,  3., 43., 26.],\n",
+      "       [41.,  4., 31.,  3.],\n",
+      "       [ 5.,  4., 29., 23.],\n",
+      "       [89., 77., 37., 32.]]))\n"
+     ]
+    }
+   ],
    "source": [
-    "from qmcpy.discrete_distribution.kronecker.kronecker_search_methods import kronecker_search_march_2026\n",
-    "\n",
     "searchsize = 25 # the time cost is  O(dim * N * searchsize^2), so searchsize should be chosen with care. The largest I have run was in MATLAB with searchsize = 300, N = 2^20, d = 100, which took about 24 hours \n",
     "\n",
     "# note that the search requires that the sample weights be w_n = n, so they are not customizable\n",
@@ -187,23 +259,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 6,
    "id": "9f66d72b",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Periodic Discrepancy')"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1200x1000 with 1 Axes>"
       ]
@@ -250,7 +312,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.11"
+   "version": "3.13.13"
   }
  },
  "nbformat": 4,
diff --git a/test/booktests/tb_lattice_kronecker_methods.py b/test/booktests/tb_lattice_kronecker_methods.py
index c10cc0473..d98231616 100644
--- a/test/booktests/tb_lattice_kronecker_methods.py
+++ b/test/booktests/tb_lattice_kronecker_methods.py
@@ -1,12 +1,10 @@
 import unittest
-from testbook import testbook
-from __init__ import TB_TIMEOUT, BaseNotebookTest
+from __init__ import BaseNotebookTest
 
 class NotebookTests(BaseNotebookTest):
 
-    @testbook('../../demos/lattice_kronecker_methods.ipynb', execute=True, timeout=TB_TIMEOUT)
-    def test_lattice_kronecker_methods_notebook(self, tb):
-        pass
+    def test_lattice_kronecker_methods_notebook(self):
+        self.run_notebook('../../demos/lattice_kronecker_methods.ipynb')
 
 if __name__ == '__main__':
     unittest.main()

From cfbe5ec988d604aa20b0deae529c9d0ee473457a Mon Sep 17 00:00:00 2001
From: sou-cheng-choi <sou.cheng.choi@stanfordalumni.org>
Date: Fri, 17 Jul 2026 10:36:13 +0800
Subject: [PATCH 14/29] Replace with a smaller example in code cell [5]

---
 test/booktests/tb_lattice_kronecker_methods.py | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/test/booktests/tb_lattice_kronecker_methods.py b/test/booktests/tb_lattice_kronecker_methods.py
index d98231616..421ef956e 100644
--- a/test/booktests/tb_lattice_kronecker_methods.py
+++ b/test/booktests/tb_lattice_kronecker_methods.py
@@ -1,10 +1,21 @@
 import unittest
 from __init__ import BaseNotebookTest
 
+
 class NotebookTests(BaseNotebookTest):
 
     def test_lattice_kronecker_methods_notebook(self):
-        self.run_notebook('../../demos/lattice_kronecker_methods.ipynb')
+        # Keep enough lattice candidates for the reduced dimension: dim <= n / 4.
+        replacements = {
+            "dim = 64": "dim = 8",
+            "n = 2**20": "n = 2**5",
+            "searchsize = 25": "searchsize = 4",
+        }
+        self.run_notebook(
+            "../../demos/lattice_kronecker_methods.ipynb",
+            replacements=replacements,
+        )
+
 
 if __name__ == '__main__':
     unittest.main()

From a4055a10d323b98458049ed77a43c5f461334769 Mon Sep 17 00:00:00 2001
From: sou-cheng-choi <sou.cheng.choi@stanfordalumni.org>
Date: Fri, 17 Jul 2026 10:48:41 +0800
Subject: [PATCH 15/29] Add doc

---
 docs/api/discrete_distributions.md | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/docs/api/discrete_distributions.md b/docs/api/discrete_distributions.md
index c0d210da6..e6d90d456 100644
--- a/docs/api/discrete_distributions.md
+++ b/docs/api/discrete_distributions.md
@@ -24,6 +24,10 @@ jupyter:
 
 ::: qmcpy.discrete_distribution.lattice.Lattice
 
+## `lattice_vector_wssd_search`
+
+::: qmcpy.discrete_distribution.lattice.lattice_vector_wssd_search.lattice_vector_wssd_search
+
 ## `Halton`
 
 ::: qmcpy.discrete_distribution.digital_net_any_bases.halton.Halton
@@ -40,6 +44,10 @@ jupyter:
 
 ::: qmcpy.discrete_distribution.kronecker.Kronecker
 
+## `kronecker_search_march_2026`
+
+::: qmcpy.discrete_distribution.kronecker.kronecker_search_methods.kronecker_search_march_2026
+
 ## `IIDStdUniform`
 
 ::: qmcpy.discrete_distribution.iid_std_uniform.IIDStdUniform

From cfbcf9caf10510456830118db4ea866bc33efd1a Mon Sep 17 00:00:00 2001
From: sou-cheng-choi <schoi32@illinoistech.edu>
Date: Fri, 17 Jul 2026 10:51:44 +0800
Subject: [PATCH 16/29] Potential fix for pull request finding 'Unused import'

Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
---
 .../discrete_distribution/kronecker/kronecker_search_methods.py  | 1 -
 1 file changed, 1 deletion(-)

diff --git a/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
index 0a0cb7569..2e6849b6c 100644
--- a/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
+++ b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
@@ -1,6 +1,5 @@
 import numpy as np
 from sympy import gcdex, primerange, prime
-import time
 #np.set_printoptions(precision=17)
 #https://github.com/sympy/sympy/releases
 

From 2a3bbb4f2d84c23eb27d4e74ce30522406c26358 Mon Sep 17 00:00:00 2001
From: sou-cheng-choi <schoi32@illinoistech.edu>
Date: Fri, 17 Jul 2026 10:51:57 +0800
Subject: [PATCH 17/29] Potential fix for pull request finding 'Testing
 equality to None'

Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
---
 .../discrete_distribution/lattice/lattice_vector_wssd_search.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
index c1b02c2ad..1c953ac53 100644
--- a/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
+++ b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
@@ -3,7 +3,7 @@
 # I am not sure where the best place to put this is, will ask Aleksi
 
 def lattice_vector_wssd_search(N, d, kernel,coord_weights):
-    if kernel == None:
+    if kernel is None:
         kernel = lambda x: x * (x - 1) + 1 / 6 # default kernel is the second Bernoulli polynomial
     if coord_weights is None:
         coord_weights = np.array([j**(-2) for j in range(1, d + 1)], dtype=np.float64) # default coordinate weights are j^(-2)

From b0889dca7798b36c07f93ac4d2ef904beb572301 Mon Sep 17 00:00:00 2001
From: sou-cheng-choi <sou.cheng.choi@stanfordalumni.org>
Date: Fri, 17 Jul 2026 15:44:49 +0800
Subject: [PATCH 18/29] Fix a bug in warning

---
 qmcpy/discrete_distribution/kronecker/kronecker.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qmcpy/discrete_distribution/kronecker/kronecker.py b/qmcpy/discrete_distribution/kronecker/kronecker.py
index 23a40bf31..41dd553f7 100644
--- a/qmcpy/discrete_distribution/kronecker/kronecker.py
+++ b/qmcpy/discrete_distribution/kronecker/kronecker.py
@@ -391,7 +391,7 @@ def __init__(self,
             if not (self.dvec.max() < len(gen_vec)):
                 if warn:
                     warnings.warn(
-                        f"CBC generating vector only supports dimension <= {len(CBC)}; falling back to Richtmyer.",
+                        f"ANDERS_CBC generating vector only supports dimension <= {len(ANDERS_CBC)}; falling back to Richtmyer.",
                         RuntimeWarning,
                     )
                 self.gen_vec_source = "RICHTMYER"

From 98221ba13264f2b13c988a0c91ad6725668ad793 Mon Sep 17 00:00:00 2001
From: sou-cheng-choi <sou.cheng.choi@stanfordalumni.org>
Date: Fri, 17 Jul 2026 15:44:59 +0800
Subject: [PATCH 19/29] Add unit tests

---
 test/test_dd_lattice_kronecker.py | 189 ++++++++++++++++++++++++++++++
 1 file changed, 189 insertions(+)
 create mode 100644 test/test_dd_lattice_kronecker.py

diff --git a/test/test_dd_lattice_kronecker.py b/test/test_dd_lattice_kronecker.py
new file mode 100644
index 000000000..3f98b7b64
--- /dev/null
+++ b/test/test_dd_lattice_kronecker.py
@@ -0,0 +1,189 @@
+import numpy as np
+import numpy.testing as npt
+import pytest
+
+from qmcpy import (
+    Kronecker,
+    Lattice,
+    kronecker_search_march_2026,
+    lattice_vector_wssd_search,
+)
+
+######################################################
+# Helper functions
+######################################################
+def _bernoulli_two(x):
+    return x * (x - 1) + 1 / 6
+
+
+def _periodic_kernel(x, coord_weights):
+    return np.prod(1 + _bernoulli_two(x) * coord_weights, axis=-1)
+
+
+def _direct_squared_discrepancies(points, coord_weights):
+    """Evaluate the periodic-kernel definition directly for small prefixes."""
+    return np.array(
+        [
+            _periodic_kernel(
+                (points[:n, None] - points[None, :n]) % 1, coord_weights
+            ).mean()
+            - 1
+            for n in range(1, len(points) + 1)
+        ]
+    )
+
+
+######################################################
+# Test class for Lattice and Kronecker methods
+######################################################
+class TestLatticeKroneckerMethods(object):
+
+    def test_lattice_discrepancy_and_wssd(self):
+        n, coord_weights = 8, np.array([1.0, 0.25])
+        lattice = Lattice(2, randomize=False, order="RADICAL_INVERSE")
+        expected = _direct_squared_discrepancies(
+            lattice.gen_samples(n=n, warn=False), coord_weights
+        )
+
+        for actual in (
+            lattice.expected_squared_periodic_discrepancies(n),
+            lattice.expected_squared_periodic_discrepancies(
+                n, coord_weights=coord_weights, kernel=_bernoulli_two
+            ),
+        ):
+            assert actual.shape == (n,) and np.isfinite(actual).all()
+            npt.assert_allclose(actual, expected, rtol=0, atol=5e-15)
+
+        npt.assert_allclose(
+            lattice.wssd(n), np.arange(1, n + 1) @ expected, rtol=0, atol=5e-14
+        )
+        sample_weights = np.linspace(0.5, 1.5, n)
+        npt.assert_allclose(
+            lattice.wssd(
+                n, coord_weights=coord_weights, sample_weights=sample_weights
+            ),
+            sample_weights @ expected,
+            rtol=0,
+            atol=5e-14,
+        )
+
+    def test_lattice_validation(self):
+        lattice = Lattice(2, randomize=False)
+        with pytest.raises(ValueError, match="coord_weights"):
+            lattice.expected_squared_periodic_discrepancies(8, coord_weights=[1.0])
+        with pytest.raises(ValueError, match="coord_weights"):
+            lattice.wssd(8, coord_weights=[1.0])
+        with pytest.raises(ValueError, match="sample_weights"):
+            lattice.wssd(8, sample_weights=np.ones(7))
+        with pytest.raises(NotImplementedError, match="linear order"):
+            Lattice(2, randomize=False, order="LINEAR").expected_squared_periodic_discrepancies(8)
+
+    def test_lattice_vector_search(self):
+        default = lattice_vector_wssd_search(16, 4, None, None)
+        explicit = lattice_vector_wssd_search(
+            16, 4, _bernoulli_two, np.array([1.0, 0.25, 1 / 9, 1 / 16])
+        )
+        npt.assert_array_equal(default, np.array([1, 5, 3, 7]))
+        npt.assert_array_equal(explicit, default)
+        assert default.shape == (4,) and default.dtype.kind in "iu"
+        assert len(np.unique(default)) == len(default) and np.all(default % 2 == 1)
+
+    def test_kronecker_discrepancy_and_wssd(self):
+        n = 8
+        kronecker = Kronecker(
+            2, generating_vector="SUZUKI", randomize="SHIFT", shift=[0.1, 0.2]
+        )
+        points = (np.arange(n)[:, None] * kronecker.gen_vec[0]) % 1
+        sample_weights = np.arange(1, n + 1)
+        expected = _direct_squared_discrepancies(points, np.ones(2))
+        actual = kronecker.periodic_discrepancy(n) ** 2
+        assert actual.shape == (1, n)
+        npt.assert_allclose(actual, expected[None], rtol=0, atol=5e-15)
+        npt.assert_allclose(
+            kronecker.wssd_discrepancy(n, sample_weights),
+            [sample_weights @ expected],
+            rtol=0,
+            atol=5e-14,
+        )
+
+        coord_weights, kernel = np.array([1.0, 0.25]), (_periodic_kernel, 1)
+        expected = _direct_squared_discrepancies(points, coord_weights)
+        for actual in (
+            kronecker._square_periodic_discrepancies(n, kernel, coord_weights),
+            kronecker.periodic_discrepancy(
+                n, k_tilde=kernel, gamma=coord_weights
+            )
+            ** 2,
+        ):
+            npt.assert_allclose(actual, expected[None], rtol=0, atol=5e-15)
+        npt.assert_allclose(
+            kronecker.wssd_discrepancy(
+                n, sample_weights, k_tilde=kernel, gamma=coord_weights
+            ),
+            [sample_weights @ expected],
+            rtol=0,
+            atol=5e-14,
+        )
+
+    def test_anders_cbc_fallback(self):
+        kronecker = Kronecker(3, generating_vector="ANDERS_CBC", randomize=False)
+        assert kronecker.gen_vec_source == "ANDERS_CBC"
+        assert kronecker.gen_vec.shape == (1, 3) and np.isfinite(kronecker.gen_vec).all()
+
+        with pytest.warns(RuntimeWarning, match="ANDERS_CBC.*dimension <= 100"):
+            fallback = Kronecker(
+                101, generating_vector="ANDERS_CBC", randomize=False
+            )
+        assert fallback.gen_vec_source == "RICHTMYER"
+        assert fallback.gen_vec.shape == (1, 101)
+
+    def test_kronecker_search(self):
+        n = 8
+        vector, wssd, discrepancies, coefficients = kronecker_search_march_2026(
+            N=n, dMax=3, searchsize=3
+        )
+        assert vector.shape == (3,) and discrepancies.shape == (n,)
+        assert coefficients.shape == (2, 4)
+        assert np.isfinite(vector).all() and np.isfinite(discrepancies).all()
+        assert np.all((0 <= vector) & (vector < 1))
+        npt.assert_allclose(
+            wssd, np.arange(1, n + 1) @ discrepancies, rtol=0, atol=5e-14
+        )
+
+        coord_weights = np.array([1.0, 0.25, 1 / 9])
+        points = (np.arange(n)[:, None] * vector) % 1
+        npt.assert_allclose(
+            discrepancies,
+            _direct_squared_discrepancies(points, coord_weights),
+            rtol=0,
+            atol=5e-15,
+        )
+
+        vector, wssd, discrepancies, coefficients = kronecker_search_march_2026(
+            N=n,
+            dMax=3,
+            searchsize=3,
+            kernel=_bernoulli_two,
+            coord_weights=coord_weights,
+            gen_vec_init=1.25,
+        )
+        assert vector[0] == pytest.approx(0.25) and coefficients.shape == (2, 4)
+        npt.assert_allclose(
+            wssd, np.arange(1, n + 1) @ discrepancies, rtol=0, atol=5e-14
+        )
+
+    @pytest.mark.parametrize(
+        ("kwargs", "message"),
+        [
+            ({"N": 8, "dMax": 2, "searchsize": 1}, "searchsize"),
+            ({"N": 1, "dMax": 2, "searchsize": 2}, "N must"),
+            ({"N": 8, "dMax": 0, "searchsize": 2}, "dMax"),
+            (
+                {"N": 8, "dMax": 3, "searchsize": 2, "coord_weights": np.ones(2)},
+                "coord_weights",
+            ),
+        ],
+    )
+    def test_kronecker_search_validation(self, kwargs, message):
+        with pytest.raises(ValueError, match=message):
+            kronecker_search_march_2026(**kwargs)

From 2542d0d37a0ff0841e8199bec2e52e4a2238ad8b Mon Sep 17 00:00:00 2001
From: Anders Pride <apride@hawk.illinoistech.edu>
Date: Tue, 28 Jul 2026 16:05:09 -0500
Subject: [PATCH 20/29] Several miscellaneous requested changes:

Changed variable & function names to be more consistent and descriptive.
Fixed imports where needed and removed a few extraneous lines.
Added docstrings/doctests where needed.
Updated examples in the lattice_kronecker_methods demo to ensure a
reasonable run time.
---
 demos/lattice_kronecker_methods.ipynb         | 151 +++++-------------
 qmcpy/discrete_distribution/__init__.py       |   2 +-
 .../kronecker/__init__.py                     |   2 +-
 .../kronecker/kronecker.py                    |  15 +-
 .../kronecker/kronecker_search_methods.py     |  86 +++++-----
 .../lattice/lattice_vector_wssd_search.py     |  93 ++++++++---
 6 files changed, 159 insertions(+), 190 deletions(-)

diff --git a/demos/lattice_kronecker_methods.ipynb b/demos/lattice_kronecker_methods.ipynb
index 0ab60dcfe..d8e721f27 100644
--- a/demos/lattice_kronecker_methods.ipynb
+++ b/demos/lattice_kronecker_methods.ipynb
@@ -10,16 +10,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "2e06ea48",
    "metadata": {},
    "outputs": [],
    "source": [
     "from qmcpy import *\n",
     "import numpy as np\n",
-    "from matplotlib import pyplot\n",
     "from time import time\n",
-    "np.set_printoptions(legacy='1.25')"
+    "from matplotlib import pyplot"
    ]
   },
   {
@@ -43,17 +42,26 @@
    "execution_count": 2,
    "id": "958e16e1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "17.68626758525142\n"
+     ]
+    }
+   ],
    "source": [
-    "dim = 64\n",
-    "n = 2**20\n",
+    "dim = 100\n",
+    "n = 2**15\n",
     "lat = Lattice(dimension=dim, order=\"RADICAL_INVERSE\", seed=12) # initialize a lattice as usual\n",
     "coord_weights = np.array([j**(-2) for j in range(1, dim + 1)]) # define some coordinate weights\n",
     "\n",
     "lat_discs = lat.expected_squared_periodic_discrepancies(n_max=n, coord_weights=coord_weights) # compute the expected squared periodic discrepancies for n = 1, 2, ...\n",
     "\n",
     "sample_weights = np.arange(1, n+1) # define some sample weights\n",
-    "lat_wssd = lat.wssd(n_max=n, coord_weights=coord_weights, sample_weights=sample_weights) # compute the wssd"
+    "lat_wssd = lat.wssd(n_max=n, coord_weights=coord_weights, sample_weights=sample_weights) # compute the wssd\n",
+    "print(lat_wssd)"
    ]
   },
   {
@@ -74,20 +82,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[193.70287929]\n"
+      "[11.0370278]\n"
      ]
     }
    ],
    "source": [
-    "dim = 64\n",
+    "dim = 100\n",
+    "n = 2**15\n",
     "coord_weights = np.array([j**(-2) for j in range(1, dim + 1)])\n",
-    "sample_weights = np.arange(1, n+1) # define some sample weights\n",
-    "kron = Kronecker(dimension=dim, seed=12, generating_vector=\"ANDERS_CBC\") # initialize a Kronecker sequence as usual\n",
+    "\n",
+    "kron = Kronecker(dimension=dim, seed=12, generating_vector=\"CBC_MT\") # initialize a Kronecker sequence as usual\n",
     "kron_k_tilde = (lambda x, gamma: np.prod(1 + (x * (x - 1) + 1/6) * gamma, axis=-1), 1) # define the kernel function (in this case, the second Bernoulli polynomial)\n",
     "\n",
     "kron_discs = kron._square_periodic_discrepancies(n = n, k_tilde = kron_k_tilde, gamma = coord_weights).reshape(-1) # compute the expected squared periodic discrepancies for n = 1, 2, ...\n",
     "\n",
-    "kron_wssd = np.float64(kron.wssd_discrepancy(n = n, weights = sample_weights, k_tilde = kron_k_tilde, gamma = coord_weights)) # compute the wssd\n",
+    "sample_weights = np.arange(1, n+1) # define some sample weights\n",
+    "kron_wssd = kron.wssd_discrepancy(n = n, sample_weights = sample_weights, k_tilde = kron_k_tilde, gamma = coord_weights) # compute the wssd\n",
     "print(kron_wssd)"
    ]
   },
@@ -117,22 +127,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken for lattice vector wssd search:  4.327883005142212\n",
-      "Searched lattice vector:  [     1 444567 406809  53917 411513 111013  57773  23363 278133 179399\n",
-      " 145725 480145 365723 134199 361515 297163 315729 250703 322429 236947\n",
-      " 508553 455183 148433  37975  46187 474063 490317  14811 417263 342641\n",
-      " 269197 474417 309749  29993 366775 433399 240621 375377  84847 232327\n",
-      " 214987 375079  32109 153487 388283 140919 390453 362317 405689 413527\n",
-      " 307801 147739 176459  95733 498361 178349 127697 427387 162217 183267\n",
-      " 300557 336879 314911 122203]\n"
+      "Time taken for lattice vector wssd search:  0.17128682136535645\n",
+      "Searched lattice vector:  [    1  4825 13541 15249 15405  9909  7493 11407 14819 10089  3683  3347\n",
+      " 13789  8837  5309  6307  6447 12103  9097  2767]\n"
      ]
     }
    ],
    "source": [
-    "# note that the search requires that the sample weights be w_n = n, so they are not customizable\n",
+    "# note that the search method requires that the sample weights be w_n = n, so they are not customizable\n",
+    "n = 2**15\n",
+    "dim = 20\n",
+    "coord_weights = np.array([j**(-2) for j in range(1, dim + 1)])\n",
     "\n",
     "time_start = time()\n",
-    "searched_lattice_vector = lattice_vector_wssd_search(N = n, d = dim, kernel = lambda x: x * (x - 1) + 1 / 6, coord_weights = coord_weights) # search for a lattice vector with low wssd\n",
+    "searched_lattice_vector = lattice_vector_wssd_search(n_max = n, d_max = dim, kernel = lambda x: x * (x - 1) + 1 / 6, coord_weights = coord_weights) # search for a lattice vector with low wssd\n",
     "time_end = time()\n",
     "print(\"Time taken for lattice vector wssd search: \", time_end - time_start)\n",
     "print(\"Searched lattice vector: \", searched_lattice_vector)"
@@ -156,97 +164,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken for Kronecker vector wssd search:  417.6621832847595\n",
-      "Searched Kronecker vector:  (array([0.61803399, 0.44322929, 0.22874783, 0.85854891, 0.09862349,\n",
-      "       0.13027022, 0.30100237, 0.49129871, 0.10752283, 0.93244982,\n",
-      "       0.08420257, 0.2563596 , 0.32164088, 0.19570311, 0.5898421 ,\n",
-      "       0.60631972, 0.02965074, 0.57696365, 0.29815947, 0.72586386,\n",
-      "       0.81107075, 0.6439515 , 0.07966279, 0.05997121, 0.09380327,\n",
-      "       0.64980017, 0.27700713, 0.74102903, 0.87941726, 0.56144415,\n",
-      "       0.11886968, 0.41924865, 0.54660185, 0.08176813, 0.48158459,\n",
-      "       0.25388801, 0.23265409, 0.54636109, 0.10474602, 0.16138721,\n",
-      "       0.31612105, 0.39305959, 0.31975094, 0.03629234, 0.37544416,\n",
-      "       0.05323235, 0.16550695, 0.95164815, 0.15079678, 0.24254269,\n",
-      "       0.29654601, 0.10189676, 0.03117397, 0.49020769, 0.40708275,\n",
-      "       0.31187689, 0.41786611, 0.84794106, 0.31750284, 0.29872605,\n",
-      "       0.11568039, 0.32747855, 0.1734749 , 0.40610889]), 126.80242919921875, array([2.94259096e-01, 1.05815272e-01, 5.93755625e-02, ...,\n",
-      "       5.26598765e-11, 5.25139932e-11, 5.26756416e-11]), array([[43., 39., 97., 88.],\n",
-      "       [19.,  8., 83., 35.],\n",
-      "       [61.,  6., 71.,  7.],\n",
-      "       [67., 11., 61., 10.],\n",
-      "       [97., 72., 31., 23.],\n",
-      "       [ 7.,  3., 23., 10.],\n",
-      "       [29., 28., 59., 57.],\n",
-      "       [41., 31., 37., 28.],\n",
-      "       [83., 69., 89., 74.],\n",
-      "       [ 7.,  1., 83., 12.],\n",
-      "       [11., 10., 43., 39.],\n",
-      "       [19.,  9., 59., 28.],\n",
-      "       [37.,  6., 31.,  5.],\n",
-      "       [97., 62., 61., 39.],\n",
-      "       [37., 20., 61., 33.],\n",
-      "       [ 2.,  1., 67., 34.],\n",
-      "       [41., 15., 71., 26.],\n",
-      "       [11.,  3., 37., 10.],\n",
-      "       [53., 45., 73., 62.],\n",
-      "       [43., 30., 53., 37.],\n",
-      "       [47., 38., 73., 59.],\n",
-      "       [23.,  2., 11.,  1.],\n",
-      "       [71., 53., 67., 50.],\n",
-      "       [ 5.,  3., 53., 32.],\n",
-      "       [61., 33., 37., 20.],\n",
-      "       [23., 18., 83., 65.],\n",
-      "       [23., 20., 31., 27.],\n",
-      "       [73., 51., 83., 58.],\n",
-      "       [23.,  9., 41., 16.],\n",
-      "       [ 7.,  5., 59., 42.],\n",
-      "       [61., 44., 43., 31.],\n",
-      "       [29.,  6., 53., 11.],\n",
-      "       [ 5.,  4., 61., 49.],\n",
-      "       [43., 40., 29., 27.],\n",
-      "       [17., 16., 67., 63.],\n",
-      "       [17., 10., 73., 43.],\n",
-      "       [29.,  6., 53., 11.],\n",
-      "       [ 7.,  2., 67., 19.],\n",
-      "       [43., 36., 37., 31.],\n",
-      "       [13.,  6., 41., 19.],\n",
-      "       [ 5.,  2., 13.,  5.],\n",
-      "       [31.,  8., 97., 25.],\n",
-      "       [ 3.,  2., 83., 55.],\n",
-      "       [23.,  3., 61.,  8.],\n",
-      "       [ 2.,  1., 37., 19.],\n",
-      "       [ 5.,  1., 31.,  6.],\n",
-      "       [79., 59., 83., 62.],\n",
-      "       [11.,  8., 73., 53.],\n",
-      "       [83., 74., 37., 33.],\n",
-      "       [11.,  8., 37., 27.],\n",
-      "       [ 2.,  1., 19., 10.],\n",
-      "       [ 3.,  1., 97., 32.],\n",
-      "       [79., 76., 53., 51.],\n",
-      "       [83., 38., 59., 27.],\n",
-      "       [19.,  5., 61., 16.],\n",
-      "       [13.,  5., 31., 12.],\n",
-      "       [67., 39., 79., 46.],\n",
-      "       [13.,  7., 41., 22.],\n",
-      "       [61., 13., 47., 10.],\n",
-      "       [ 5.,  3., 43., 26.],\n",
-      "       [41.,  4., 31.,  3.],\n",
-      "       [ 5.,  4., 29., 23.],\n",
-      "       [89., 77., 37., 32.]]))\n"
+      "Time taken for kronecker vector wssd search:  5.96858549118042\n",
+      "Searched Kronecker vector:  [0.61803399 0.26774665 0.91444648 0.22708655 0.12137476 0.71267465\n",
+      " 0.69787961 0.10230792 0.18609503 0.31195642 0.41561801 0.13176115\n",
+      " 0.22004561 0.56882224 0.1079203  0.10500649 0.16477572 0.85934099\n",
+      " 0.79691446 0.21233239]\n"
      ]
     }
    ],
    "source": [
-    "searchsize = 25 # the time cost is  O(dim * N * searchsize^2), so searchsize should be chosen with care. The largest I have run was in MATLAB with searchsize = 300, N = 2^20, d = 100, which took about 24 hours \n",
-    "\n",
-    "# note that the search requires that the sample weights be w_n = n, so they are not customizable\n",
+    "# note that the search method requires that the sample weights be w_n = n, so they are not customizable\n",
+    "n = 2**15\n",
+    "dim = 20\n",
+    "coord_weights = np.array([j**(-2) for j in range(1, dim + 1)])\n",
+    "searchsize = 20 # the time cost is O(dim * n * searchsize^2), so searchsize should be chosen with care. The largest search I have run was in MATLAB with searchsize = 300, N = 2^20, d = 100, which took about 24 hours \n",
     "\n",
     "time_start = time()\n",
-    "searched_kron_vector = kronecker_search_march_2026(N = n, dMax = dim, kernel = lambda x: x * (x - 1) + 1 / 6, searchsize = searchsize, coord_weights = coord_weights) # search for a Kronecker vector with low wssd\n",
+    "searched_kron_vector = kronecker_vector_search_mobius_transform(n_max = n, d_max = dim, kernel = lambda x: x * (x - 1) + 1 / 6, searchsize = searchsize, coord_weights = coord_weights) # search for a Kronecker vector with low wssd\n",
     "time_end = time()\n",
     "\n",
-    "print(\"Time taken for Kronecker vector wssd search: \", time_end - time_start)\n",
-    "print(\"Searched Kronecker vector: \", searched_kron_vector)"
+    "print(\"Time taken for kronecker vector wssd search: \", time_end - time_start)\n",
+    "print(\"Searched Kronecker vector: \", searched_kron_vector[0])"
    ]
   },
   {
@@ -275,8 +213,7 @@
     }
    ],
    "source": [
-    "gen_vec = np.loadtxt(\"kuo.lattice-39102-1024-1048576.3600.txt\",dtype=np.uint64) \n",
-    "lat1 = Lattice(dimension=dim, order=\"RADICAL_INVERSE\", seed=12, generating_vector=gen_vec[:,1], m_max=20) # initialize a lattice with the generating vector of all 1s\n",
+    "lat1 = Lattice(dimension=dim, order=\"RADICAL_INVERSE\", seed=12, generating_vector=\"kuo.lattice-33002-1024-1048576.9125.txt\", m_max=20) # initialize a lattice with the generating vector of all 1s\n",
     "lat_discs1 = lat1.expected_squared_periodic_discrepancies(n_max=n, coord_weights=coord_weights)\n",
     "\n",
     "\n",
diff --git a/qmcpy/discrete_distribution/__init__.py b/qmcpy/discrete_distribution/__init__.py
index d5f08e3a1..1244ac8ee 100644
--- a/qmcpy/discrete_distribution/__init__.py
+++ b/qmcpy/discrete_distribution/__init__.py
@@ -4,7 +4,7 @@
 from .digital_net_b2 import DigitalNetB2
 from .digital_net_any_bases import DigitalNetAnyBases,Halton,Faure
 from .mpmc import MPMC
-from .kronecker import Kronecker, kronecker_search_march_2026
+from .kronecker import Kronecker, kronecker_vector_search_mobius_transform
 
 DiscreteDistribution = AbstractDiscreteDistribution
 _DiscreteDistribution = AbstractDiscreteDistribution
diff --git a/qmcpy/discrete_distribution/kronecker/__init__.py b/qmcpy/discrete_distribution/kronecker/__init__.py
index d88272a27..69aed4fdf 100644
--- a/qmcpy/discrete_distribution/kronecker/__init__.py
+++ b/qmcpy/discrete_distribution/kronecker/__init__.py
@@ -1,2 +1,2 @@
 from .kronecker import Kronecker
-from .kronecker_search_methods import kronecker_search_march_2026
+from .kronecker_search_methods import kronecker_vector_search_mobius_transform
\ No newline at end of file
diff --git a/qmcpy/discrete_distribution/kronecker/kronecker.py b/qmcpy/discrete_distribution/kronecker/kronecker.py
index 41dd553f7..f06a50045 100644
--- a/qmcpy/discrete_distribution/kronecker/kronecker.py
+++ b/qmcpy/discrete_distribution/kronecker/kronecker.py
@@ -243,6 +243,7 @@ def __init__(self,
                 - `"CBC"`: uses the first $d$ components of a known good Component-by-Component (CBC) generating vector.
                 - `"RICHTMYER"`: uses $\boldsymbol{\alpha}_j = \sqrt{p_j} \bmod 1$, where $p_j$ are primes. This is the classical Richtmyer construction.
                 - `"SUZUKI"`: uses a deterministic construction $\boldsymbol{\alpha}_j = 2^{j/(d+1)}$.
+                - `"CBC_MT"`: uses the first $d$ components of a known good CBC generating vector obtained using the Mobius transformation method, which can be found in kronecker_search_methods.py.
                 - np.array: user-specified generating vector.
 
             shift (np.ndarray): Shift vector $\boldsymbol{\delta}$. If `randomize=True`, this is ignored and a random shift is generated. Otherwise, a fixed shift is used.
@@ -285,9 +286,9 @@ def __init__(self,
         elif isinstance(generating_vector, str) and generating_vector.lower() == "suzuki":
             self.gen_vec_source = "SUZUKI"
             gen_vec = _suzuki_generating_vector(self.dvec.max()+1)       
-        elif isinstance(generating_vector, str) and generating_vector.lower() == "anders_cbc":
-            self.gen_vec_source = "ANDERS_CBC"
-            ANDERS_CBC = np.array([0.618033988749895,
+        elif isinstance(generating_vector, str) and generating_vector.lower() == "cbc_mt":
+            self.gen_vec_source = "CBC_MT"
+            CBC_MT = np.array([0.618033988749895,
                 0.3173225474723,
                 0.59332263014446,
                 0.20776441643926,
@@ -387,11 +388,11 @@ def __init__(self,
                 0.337431120990153,
                 0.542476014178907,
                 0.307279789725491], dtype=np.float64)
-            gen_vec = ANDERS_CBC
+            gen_vec = CBC_MT
             if not (self.dvec.max() < len(gen_vec)):
                 if warn:
                     warnings.warn(
-                        f"ANDERS_CBC generating vector only supports dimension <= {len(ANDERS_CBC)}; falling back to Richtmyer.",
+                        f"CBC_MT generating vector only supports dimension <= {len(CBC_MT)}; falling back to Richtmyer.",
                         RuntimeWarning,
                     )
                 self.gen_vec_source = "RICHTMYER"
@@ -460,7 +461,7 @@ def periodic_discrepancy(self, n, k_tilde=None, gamma=None):
         return np.sqrt(self._square_periodic_discrepancies(n, k_tilde, gamma))
         
 
-    def wssd_discrepancy(self, n, weights, k_tilde = None, gamma = None):
+    def wssd_discrepancy(self, n, sample_weights, k_tilde = None, gamma = None):
         # calculates the weighted sum of square discrepancy
         if gamma is None:
             gamma = np.ones(self.d)
@@ -469,7 +470,7 @@ def wssd_discrepancy(self, n, weights, k_tilde = None, gamma = None):
             k_tilde = (lambda x, gamma: np.prod(1 + (x * (x - 1) + 1/6) * gamma, axis=-1), 1)
 
         discrepancies = self._square_periodic_discrepancies(n, k_tilde, gamma)
-        return np.sum(weights * discrepancies, axis=-1)
+        return np.sum(sample_weights * discrepancies, axis=-1)
 
     
     def _square_periodic_discrepancies(self, n, k_tilde, gamma):
diff --git a/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
index 2e6849b6c..b4ef4b77c 100644
--- a/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
+++ b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
@@ -1,43 +1,42 @@
 import numpy as np
-from sympy import gcdex, primerange, prime
-#np.set_printoptions(precision=17)
-#https://github.com/sympy/sympy/releases
+import sympy
 
-
-# I can't find where Jimmy's code for the kronecker search from SURE 2025 is, so I've temporarily put my method here
-
-def kronecker_search_march_2026(N, dMax, searchsize, kernel=None, coord_weights=None, gen_vec_init=None):
+def kronecker_vector_search_mobius_transform(n_max, d_max, searchsize, kernel=None, coord_weights=None, gen_vec_init=None):
     """
+    CBC search method for finding a generating vector for a Kronecker sequence, minimizing the weighted sum of squared discrepancies (WSSD).
+        - The first component is gen_vec_init, defaults to the golden ratio.
+        - We use a modified mobius transformation f(x) = (a*x + b)/(c*x + d) where a, c are distinct primes and b, d are the two pairs of the smallest positive integers such that |a*d - b*c| = 1.
+        - Each subsequent component is found by performing the mobius transformation on the previous component, searching over all pairs of distinct primes from the first searchsize many primes.
     Args:
-        N (int): The maximum sample size to be searched over.
-        dMax (int): The maximum dimension for which to find the generating vector.
-        kernel (function): The kernel function to use in the search.
+        n_max (int): The maximum sample size to be searched over.
+        d_max (int): The maximum dimension for which to find the generating vector.
+        kernel (callable): The kernel function to use in the search.
         searchsize (int): The number of primes to search over for each component of the generating vector.
         coord_weights (array-like, optional): An array of coordinate weights to use in the search. If None, weights are set to j^(-2).
         gen_vec_init (array-like, optional): The initial value for the generating vector. If None, the golden ratio is used for the first component. Note that gen_vec_init is taken mod 1.
     Returns:
         generating_vector, wssd, discrepancies, coeff (tuple):
         - generating_vector (numpy array): The generating vector found by the search.
-        - wssd (float): The weighted sum of squared discrepancies for n = 1,...,N, for the generating vector found.
-        - discrepancies (numpy array): The discrepancies for n = 1,...,N.
+        - wssd (float): The weighted sum of squared discrepancies for n = 1,...,n_max, for the generating vector found.
+        - discrepancies (numpy array): The discrepancies for n = 1,...,n_max.
         - coeff (numpy array): The coefficients of the linear transformation used in the search. A description of the coeff array is found below.
     Time cost:
-        The time cost of the search is O(searchsize^2 * dMax * N).
+        The time cost of the search is O(searchsize^2 * d_max * n_max).
     Approach:
-        Uses the quadratic Bernoulli polynomial kernel to conduct a CBC search for a generating vector, minimizing the weighted sum of squared discrepancies (wssd) with weights w_n = n.
+        Uses the quadratic Bernoulli polynomial kernel to conduct a CBC search for a generating vector, minimizing the weighted sum of squared discrepancies (wssd) with sample weights w_n = n.
     Details on coeff array:
-        The coeff array is a (dMax-1) x 4 array where each row corresponds to a dimension from 2 to dMax. The columns correspond to the coefficients of the linear transformation used to compute the gen_vec component for that dimension. Specifically,
+        The coeff array is a (d_max-1) x 4 array where each row corresponds to a dimension from 2 to d_max. The columns correspond to the coefficients of the linear transformation used to compute the gen_vec component for that dimension. Specifically,
         - gen_vec[dim+1] = (coeff[dim, 0] * gen_vec[dim] + coeff[dim, 1]) / (coeff[dim, 2] * gen_vec[dim] + coeff[dim, 3])
     """
 
     if searchsize < 2:
         raise ValueError("searchsize must be at least 2.")
-    if N < 2:
-        raise ValueError("N must be at least 2.")
-    if dMax < 1:
-        raise ValueError("dMax must be at least 1.")
-    if coord_weights is not None and len(coord_weights) < dMax:
-        raise ValueError("Length of coord_weights must be greater than or equal to dMax.")
+    if n_max < 2:
+        raise ValueError("n_max must be at least 2.")
+    if d_max < 1:
+        raise ValueError("d_max must be at least 1.")
+    if coord_weights is not None and len(coord_weights) < d_max:
+        raise ValueError("Length of coord_weights must be greater than or equal to d_max.")
     
 
     # the quadratic Bernoulli polynomial
@@ -46,13 +45,13 @@ def kronecker_search_march_2026(N, dMax, searchsize, kernel=None, coord_weights=
     
     # define coordinate weights if not provided, default to j^(-2)
     if coord_weights is None:
-        coord_weights = np.array([j**(-2) for j in range(1, dMax + 1)], dtype=np.float64)
+        coord_weights = np.array([j**(-2) for j in range(1, d_max + 1)], dtype=np.float64)
 
     # search over the first n primes, n = searchsize
-    searchspace = np.array(list(primerange(1, prime(searchsize)+1)), dtype=np.float64)
+    searchspace = np.array(list(sympy.primerange(1, sympy.prime(searchsize)+1)), dtype=np.float64)
 
     # gen_vec is our generating vector, will be found cbc
-    gen_vec = np.zeros(dMax, dtype=np.float64)
+    gen_vec = np.zeros(d_max, dtype=np.float64)
 
     # we pick the golden ratio as the first component of gen_vec, or let the user specify
     if gen_vec_init is None:
@@ -61,14 +60,14 @@ def kronecker_search_march_2026(N, dMax, searchsize, kernel=None, coord_weights=
         gen_vec[0] = np.mod(gen_vec_init, 1,dtype=np.float64)
 
     # precompute several constants for the wssd calculation
-    diff = np.cumsum(1.0 / np.arange(N, 1, -1,dtype=np.float64))
+    diff = np.cumsum(1.0 / np.arange(n_max, 1, -1,dtype=np.float64))
     freq = np.cumsum(diff)
     freq = np.flip(freq)
 
-    num = N * (N + 1) / 2
+    num = n_max * (n_max + 1) / 2
 
     nK0 = (1 + coord_weights/6)
-    nK0 = N * np.cumprod(nK0)
+    nK0 = n_max * np.cumprod(nK0)
 
     # precompute Bezout coefficients for all pairs of primes in the search space
     bezoutCoeffs = np.zeros((searchsize, searchsize))
@@ -77,19 +76,19 @@ def kronecker_search_march_2026(N, dMax, searchsize, kernel=None, coord_weights=
         for j in range(i + 1, searchsize):
             c = searchspace[j]
             # Use sympy.gcdex to get Bezout coefficients
-            d_coeff, b_coeff, _ = gcdex(int(a), int(c))
+            d_coeff, b_coeff, _ = sympy.gcdex(int(a), int(c))
             bezoutCoeffs[i, j] = np.float64(b_coeff)
             bezoutCoeffs[j, i] = np.float64(d_coeff)
 
 
     # setting up some useful variables for the search
-    coeff = np.zeros((dMax - 1, 4)) # stores the coefficients of the linear transformation at each dimension
-    t = gen_vec[0] * np.arange(1, N) % 1 # t vector is the vector of coordinates generated for the first dimension
+    coeff = np.zeros((d_max - 1, 4)) # stores the coefficients of the linear transformation at each dimension
+    t = gen_vec[0] * np.arange(1, n_max) % 1 # t vector is the vector of coordinates generated for the first dimension
     kPrev = 1 + coord_weights[0] * kernel(t) # gets the k vector for the first dimension, which is used in the wssd calculation and updated each dimension of the search.
-    # The k vector is Ktilde(x_i) for i = 1,...,N-1, where Ktilde is the kernel and x_i are the points generated by the gen_vec vector, up to the current dimension.
+    # The k vector is Ktilde(x_i) for i = 1,...,n_max-1, where Ktilde is the kernel and x_i are the points generated by the gen_vec vector, up to the current dimension.
     
     # the main search loop
-    for dim in range(1, dMax):
+    for dim in range(1, d_max):
         best_wssd = np.inf # stores the current wssd found for each dimension, initialized to infinity
         best_gen_vec = 0 # stores the current best gen_vec component found for this dimension, initialized to 0
         best_k = None # stores the k vector for the current best gen_vec, used to update the k vector for the next dimension after the search is done for this dimension
@@ -117,8 +116,8 @@ def kronecker_search_march_2026(N, dMax, searchsize, kernel=None, coord_weights=
                 
                 gen_vec_dim1 = (p1 * gen_vec[dim - 1] + b1) /  (p2 * gen_vec[dim - 1] + d1) # the linear transformation to get the next gen_vec_dim candidate to test
                 gen_vec_dim2 = (p1 * gen_vec[dim - 1] + b2) / (p2 * gen_vec[dim - 1] + d2) # the other candidate from the linear transformation
-                t1 = (gen_vec_dim1 * np.arange(1, N)) - np.floor(gen_vec_dim1 * np.arange(1, N)) # vector of coordinates generated by this candidate component
-                t2 = (gen_vec_dim2 * np.arange(1, N)) - np.floor(gen_vec_dim2 * np.arange(1, N)) 
+                t1 = (gen_vec_dim1 * np.arange(1, n_max)) - np.floor(gen_vec_dim1 * np.arange(1, n_max)) # vector of coordinates generated by this candidate component
+                t2 = (gen_vec_dim2 * np.arange(1, n_max)) - np.floor(gen_vec_dim2 * np.arange(1, n_max)) 
                 k_vector1 = kPrev * (1 + kernel(t1) * coord_weights[dim]) # get the k vector for this candidate component, used in the wssd calculation
                 k_vector2 = kPrev * (1 + kernel(t2) * coord_weights[dim]) 
 
@@ -153,26 +152,17 @@ def kronecker_search_march_2026(N, dMax, searchsize, kernel=None, coord_weights=
 
         # print(coeff[dim - 1, :], (nK0[dim] - num + 2 * best_wssd)) # debugging line to check the coefficients and wssd at each dimension
     
-    # Adapted from Jimmy's code for calculating the discrepancies for n = 1,...,N from SURE 2025
-    n_array = np.arange(1, N + 1)
+    # Adapted from Jimmy's code for calculating the discrepancies for n = 1,...,n_max from SURE 2025
+    n_array = np.arange(1, n_max + 1)
     k_tilde = lambda x, coord_weight: np.prod(1 + kernel(x) * coord_weight, axis=1)
-    k_tilde_terms = k_tilde(gen_vec * np.arange(N).reshape((N, 1)) - np.floor(gen_vec * np.arange(N).reshape((N, 1))), coord_weights)
+    k_tilde_terms = k_tilde(gen_vec * np.arange(n_max).reshape((n_max, 1)) - np.floor(gen_vec * np.arange(n_max).reshape((n_max, 1))), coord_weights)
 
     left_sum = np.cumsum(k_tilde_terms[1:]) * n_array[1:]
     right_sum = np.cumsum(n_array[:-1] * k_tilde_terms[1:])
         
     k_tilde_zero_terms = k_tilde_terms[0] * n_array
-    summation = np.zeros(N)
+    summation = np.zeros(n_max)
     summation[1:] = left_sum - right_sum
     discrepancies = (k_tilde_zero_terms + 2 * summation) / (n_array ** 2) - 1
 
-    return gen_vec, best_wssd, discrepancies, coeff
-
-
-# quick and dirty test
-# start = time.time()
-# a, wssd, _, _ = kronecker_search_march_2026(2**10, 20, 50)
-# print(a)
-# print(wssd)
-# end = time.time()
-# print("Run time (seconds): ", end - start)
+    return gen_vec, best_wssd, discrepancies, coeff
\ No newline at end of file
diff --git a/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
index 1c953ac53..0f416a4d0 100644
--- a/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
+++ b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
@@ -1,25 +1,68 @@
 import numpy as np
 
-# I am not sure where the best place to put this is, will ask Aleksi
-
-def lattice_vector_wssd_search(N, d, kernel,coord_weights):
-    if kernel is None:
+def lattice_vector_wssd_search(n_max, d_max, coord_weights=None, kernel=None):
+    """
+    CBC search method for finding a lattice rule minimizing the WSSD.
+    Args:
+        n_max (int): The maximum number of points the lattice rule is optimized for.
+        d_max (int): The dimension of the lattice rule.
+        coord_weights (array-like, optional): The coordinate weights used to compute the discrepancy. Defaults to j^(-2) for j=1,...,d_max.
+        kernel (callable, optional): The kernel used to compute the discrepancy. Should accept a single argument and return a scalar. Defaults to the second Bernoulli polynomial.
+    Returns:
+        gen_vec (array-like): The generating vector of the lattice that minimizes the WSSD.
+    Time cost:
+        The time cost of the search is O(d_max * n_max * log(n_max)), though the contribution of d_max is smaller until around d_max = 100.
+    Note:
+        Uses sample weights of w_n = n for n = 1,...,n_max when calculating the WSSD.
+    
+    Examples:
+        >>> lattice_vector_wssd_search(n_max = 2**10, d_max = 5)
+        array([1, 403, 361, 281, 421])
+        >>> lattice_vector_wssd_search(n_max = 2**15, d_max = 10)
+        array([1, 4825, 13541, 15249, 15405, 9909, 7493, 11407, 14819, 10089])
+        
+        Custom coordinate weights
+
+        >>> lattice_vector_wssd_search(n_max = 2**15, d_max = 10, coord_weights = [j**(-1) for j in range(1, 6)])
+        array([1, 4825, 13541, 15249, 7311, 10339, 5933, 6307, 14729, 13037])
+
+        Custom kernels
+
+        >>> bernoulli6 = lambda x: x**6 - 3 * x**5 + 5 / 2 * x**4 - 1 / 2 * x**2 + 1 / 42
+        >>> lattice_vector_wssd_search(n_max = 2**15, d_max = 10, coord_weights = None, kernel = bernoulli6)
+        array([1, 1635, 6875, 8665, 8531, 1361, 11771, 10987, 2805, 9961])
+    """
+
+    if kernel == None:
         kernel = lambda x: x * (x - 1) + 1 / 6 # default kernel is the second Bernoulli polynomial
     if coord_weights is None:
-        coord_weights = np.array([j**(-2) for j in range(1, d + 1)], dtype=np.float64) # default coordinate weights are j^(-2)
-
-    m = np.ceil(np.log2(N)).astype(int)
+        coord_weights = np.array([j**(-2) for j in range(1, d_max + 1)], dtype=np.float64) # default coordinate weights are j^(-2)
+
+    if not callable(kernel):
+        raise ValueError("kernel must be a callable function")
+    if not isinstance(coord_weights, (list, np.ndarray)):
+        raise ValueError("coord_weights must be array-like")
+    if not isinstance(n_max, int) or not isinstance(d_max, int):
+        raise ValueError("n_max and d_max must be integers")
+    
+    if len(coord_weights) < d_max:
+        raise ValueError("coord_weights must have length at least d_max")
+    if n_max < 3:
+        raise ValueError("n_max must be at least 3")
+    if d_max < 1:
+        raise ValueError("d_max must be at least 1")
+
+    m = np.ceil(np.log2(n_max)).astype(int)
 
     # ----------------------------------------------------------------------
     # Set up rhovector
     # ----------------------------------------------------------------------
-    bits = np.zeros((N, m), dtype=int)
-    for i in range(N):
-        # 2*bitget(i,1:m) in MATLAB
+    bits = np.zeros((n_max, m), dtype=int)
+    for i in range(n_max):
         bits[i, :] = 2 * np.array([((i >> j) & 1) for j in range(m)], dtype=int)
 
-    cumsumbits = np.cumsum(bits, axis=0)        # N x m
-    rhovector = np.dot((1.0 / np.arange(1, N + 1)), cumsumbits)  # 1 x m
+    cumsumbits = np.cumsum(bits, axis=0)        # n_max x m
+    rhovector = np.dot((1.0 / np.arange(1, n_max + 1)), cumsumbits)  # 1 x m
 
     rhovectorNx1 = np.zeros((2**m - 1, 1))
     rIdx1 = 0
@@ -80,10 +123,10 @@ def lattice_vector_wssd_search(N, d, kernel,coord_weights):
     # ----------------------------------------------------------------------
     # Begin search
     # ----------------------------------------------------------------------
-    h = np.ones(d, dtype=int)
+    gen_vec = np.ones(d_max, dtype=int)
 
-    for hComp in range(2, d + 1):
-        WSSD = np.zeros(2**(m - 2))
+    for hComp in range(2, d_max + 1):
+        wssd = np.zeros(2**(m - 2))
 
         gamma = coord_weights[hComp - 1]
         omega = lambda x: 1 + gamma * (x * (x - 1) + 1 / 6)
@@ -104,30 +147,28 @@ def lattice_vector_wssd_search(N, d, kernel,coord_weights):
 
             wVector = 2 * np.fft.ifft(np.fft.fft(fftCol) * np.fft.fft(pCol)).real
             numrep = 2**(m - l)
-            WSSD = WSSD + np.tile(wVector, numrep)
+            wssd = wssd + np.tile(wVector, numrep)
 
             curIdx2 = nextIdx2 + 1
             prodIdx1 = prodIdx2 + 2**(l - 2) + 1
 
-        WSSD = WSSD + omega(1 / 2) * prodV[-1, 0]
-        WSSD = WSSD + N * k0 - N * (N + 1) / 2
+        wssd = wssd + omega(1 / 2) * prodV[-1, 0]
+        wssd = wssd + n_max * k0 - n_max * (n_max + 1) / 2
 
-        bestIdx = int(np.argmin(WSSD))
-        bestWSSD = float(WSSD[bestIdx])
+        bestIdx = int(np.argmin(wssd))
         newH = int(gR[bestIdx])
 
         # Avoid duplicates
-        while newH in h:
-            WSSD[bestIdx] = np.inf
-            bestIdx = int(np.argmin(WSSD))
-            bestWSSD = float(WSSD[bestIdx])
+        while newH in gen_vec:
+            wssd[bestIdx] = np.inf
+            bestIdx = int(np.argmin(wssd))
             newH = int(gR[bestIdx])
 
-        h[hComp - 1] = newH
+        gen_vec[hComp - 1] = newH
 
         rowV = (newH * rowVects) % 2**m
         rowV = rowV / 2**m
         rowV = omega(rowV)
         prodV = prodV * rowV[:, None]
 
-    return h
\ No newline at end of file
+    return gen_vec
\ No newline at end of file

From dea6e2c5bcbc76f175e814f2e41d672709c9da95 Mon Sep 17 00:00:00 2001
From: Anders Pride <apride@hawk.illinoistech.edu>
Date: Tue, 28 Jul 2026 16:55:16 -0500
Subject: [PATCH 21/29] Updated docs to match new kronecker search name

---
 docs/api/discrete_distributions.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/api/discrete_distributions.md b/docs/api/discrete_distributions.md
index e6d90d456..f769e8d56 100644
--- a/docs/api/discrete_distributions.md
+++ b/docs/api/discrete_distributions.md
@@ -44,9 +44,9 @@ jupyter:
 
 ::: qmcpy.discrete_distribution.kronecker.Kronecker
 
-## `kronecker_search_march_2026`
+## `kronecker_vector_search_mobius_transform`
 
-::: qmcpy.discrete_distribution.kronecker.kronecker_search_methods.kronecker_search_march_2026
+::: qmcpy.discrete_distribution.kronecker.kronecker_search_methods.kronecker_vector_search_mobius_transform
 
 ## `IIDStdUniform`
 

From fe041c427987226bc9f4590463fda727ad605607 Mon Sep 17 00:00:00 2001
From: AndersPride <apride@hawk.iit.edu>
Date: Tue, 28 Jul 2026 16:59:04 -0500
Subject: [PATCH 22/29] Potential fix for pull request finding 'Testing
 equality to None'

Changed == None to is None

Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
---
 .../discrete_distribution/lattice/lattice_vector_wssd_search.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
index 0f416a4d0..99300b7b0 100644
--- a/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
+++ b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
@@ -33,7 +33,7 @@ def lattice_vector_wssd_search(n_max, d_max, coord_weights=None, kernel=None):
         array([1, 1635, 6875, 8665, 8531, 1361, 11771, 10987, 2805, 9961])
     """
 
-    if kernel == None:
+    if kernel is None:
         kernel = lambda x: x * (x - 1) + 1 / 6 # default kernel is the second Bernoulli polynomial
     if coord_weights is None:
         coord_weights = np.array([j**(-2) for j in range(1, d_max + 1)], dtype=np.float64) # default coordinate weights are j^(-2)

From c98da32ac31d5ca2ba79681bf720f5b2eb99bbad Mon Sep 17 00:00:00 2001
From: sou-cheng-choi <sou.cheng.choi@stanfordalumni.org>
Date: Sat, 8 Aug 2026 18:19:44 +0800
Subject: [PATCH 23/29] Fix test failures

---
 .../booktests/tb_lattice_kronecker_methods.py |  7 ++-
 test/test_dd_lattice_kronecker.py             | 59 +++++++++++--------
 2 files changed, 39 insertions(+), 27 deletions(-)

diff --git a/test/booktests/tb_lattice_kronecker_methods.py b/test/booktests/tb_lattice_kronecker_methods.py
index 421ef956e..5164e63a2 100644
--- a/test/booktests/tb_lattice_kronecker_methods.py
+++ b/test/booktests/tb_lattice_kronecker_methods.py
@@ -7,9 +7,10 @@ class NotebookTests(BaseNotebookTest):
     def test_lattice_kronecker_methods_notebook(self):
         # Keep enough lattice candidates for the reduced dimension: dim <= n / 4.
         replacements = {
-            "dim = 64": "dim = 8",
-            "n = 2**20": "n = 2**5",
-            "searchsize = 25": "searchsize = 4",
+            "dim = 100": "dim = 8",
+            "dim = 20": "dim = 8",
+            "n = 2**15": "n = 2**5",
+            "searchsize = 20": "searchsize = 4",
         }
         self.run_notebook(
             "../../demos/lattice_kronecker_methods.ipynb",
diff --git a/test/test_dd_lattice_kronecker.py b/test/test_dd_lattice_kronecker.py
index 3f98b7b64..a16690826 100644
--- a/test/test_dd_lattice_kronecker.py
+++ b/test/test_dd_lattice_kronecker.py
@@ -5,7 +5,7 @@
 from qmcpy import (
     Kronecker,
     Lattice,
-    kronecker_search_march_2026,
+    kronecker_vector_search_mobius_transform,
     lattice_vector_wssd_search,
 )
 
@@ -81,7 +81,10 @@ def test_lattice_validation(self):
     def test_lattice_vector_search(self):
         default = lattice_vector_wssd_search(16, 4, None, None)
         explicit = lattice_vector_wssd_search(
-            16, 4, _bernoulli_two, np.array([1.0, 0.25, 1 / 9, 1 / 16])
+            n_max=16,
+            d_max=4,
+            coord_weights=np.array([1.0, 0.25, 1 / 9, 1 / 16]),
+            kernel=_bernoulli_two,
         )
         npt.assert_array_equal(default, np.array([1, 5, 3, 7]))
         npt.assert_array_equal(explicit, default)
@@ -125,22 +128,23 @@ def test_kronecker_discrepancy_and_wssd(self):
             atol=5e-14,
         )
 
-    def test_anders_cbc_fallback(self):
-        kronecker = Kronecker(3, generating_vector="ANDERS_CBC", randomize=False)
-        assert kronecker.gen_vec_source == "ANDERS_CBC"
-        assert kronecker.gen_vec.shape == (1, 3) and np.isfinite(kronecker.gen_vec).all()
+    def test_cbc_mobius_fallback(self):
+        kronecker = Kronecker(3, generating_vector="CBC_MT", randomize=False)
+        assert kronecker.gen_vec_source == "CBC_MT"
+        assert kronecker.gen_vec.shape == (1, 3)
+        assert np.isfinite(kronecker.gen_vec).all()
 
-        with pytest.warns(RuntimeWarning, match="ANDERS_CBC.*dimension <= 100"):
-            fallback = Kronecker(
-                101, generating_vector="ANDERS_CBC", randomize=False
-            )
+        with pytest.warns(RuntimeWarning, match="CBC_MT.*dimension <= 100"):
+            fallback = Kronecker(101, generating_vector="CBC_MT", randomize=False)
         assert fallback.gen_vec_source == "RICHTMYER"
         assert fallback.gen_vec.shape == (1, 101)
 
     def test_kronecker_search(self):
         n = 8
-        vector, wssd, discrepancies, coefficients = kronecker_search_march_2026(
-            N=n, dMax=3, searchsize=3
+        vector, wssd, discrepancies, coefficients = (
+            kronecker_vector_search_mobius_transform(
+                n_max=n, d_max=3, searchsize=3
+            )
         )
         assert vector.shape == (3,) and discrepancies.shape == (n,)
         assert coefficients.shape == (2, 4)
@@ -159,13 +163,15 @@ def test_kronecker_search(self):
             atol=5e-15,
         )
 
-        vector, wssd, discrepancies, coefficients = kronecker_search_march_2026(
-            N=n,
-            dMax=3,
-            searchsize=3,
-            kernel=_bernoulli_two,
-            coord_weights=coord_weights,
-            gen_vec_init=1.25,
+        vector, wssd, discrepancies, coefficients = (
+            kronecker_vector_search_mobius_transform(
+                n_max=n,
+                d_max=3,
+                searchsize=3,
+                kernel=_bernoulli_two,
+                coord_weights=coord_weights,
+                gen_vec_init=1.25,
+            )
         )
         assert vector[0] == pytest.approx(0.25) and coefficients.shape == (2, 4)
         npt.assert_allclose(
@@ -175,15 +181,20 @@ def test_kronecker_search(self):
     @pytest.mark.parametrize(
         ("kwargs", "message"),
         [
-            ({"N": 8, "dMax": 2, "searchsize": 1}, "searchsize"),
-            ({"N": 1, "dMax": 2, "searchsize": 2}, "N must"),
-            ({"N": 8, "dMax": 0, "searchsize": 2}, "dMax"),
+            ({"n_max": 8, "d_max": 2, "searchsize": 1}, "searchsize"),
+            ({"n_max": 1, "d_max": 2, "searchsize": 2}, "n_max must"),
+            ({"n_max": 8, "d_max": 0, "searchsize": 2}, "d_max"),
             (
-                {"N": 8, "dMax": 3, "searchsize": 2, "coord_weights": np.ones(2)},
+                {
+                    "n_max": 8,
+                    "d_max": 3,
+                    "searchsize": 2,
+                    "coord_weights": np.ones(2),
+                },
                 "coord_weights",
             ),
         ],
     )
     def test_kronecker_search_validation(self, kwargs, message):
         with pytest.raises(ValueError, match=message):
-            kronecker_search_march_2026(**kwargs)
+            kronecker_vector_search_mobius_transform(**kwargs)

From 9874b968a4e51463f0c18c99e0075c65836dedce Mon Sep 17 00:00:00 2001
From: sou-cheng-choi <sou.cheng.choi@stanfordalumni.org>
Date: Sat, 8 Aug 2026 18:35:10 +0800
Subject: [PATCH 24/29] Fix doc tests

---
 .../lattice/lattice_vector_wssd_search.py     | 21 ++++++++++++-------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
index 99300b7b0..cbca41913 100644
--- a/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
+++ b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
@@ -3,6 +3,7 @@
 def lattice_vector_wssd_search(n_max, d_max, coord_weights=None, kernel=None):
     """
     CBC search method for finding a lattice rule minimizing the WSSD.
+
     Args:
         n_max (int): The maximum number of points the lattice rule is optimized for.
         d_max (int): The dimension of the lattice rule.
@@ -10,27 +11,31 @@ def lattice_vector_wssd_search(n_max, d_max, coord_weights=None, kernel=None):
         kernel (callable, optional): The kernel used to compute the discrepancy. Should accept a single argument and return a scalar. Defaults to the second Bernoulli polynomial.
     Returns:
         gen_vec (array-like): The generating vector of the lattice that minimizes the WSSD.
+
     Time cost:
         The time cost of the search is O(d_max * n_max * log(n_max)), though the contribution of d_max is smaller until around d_max = 100.
     Note:
         Uses sample weights of w_n = n for n = 1,...,n_max when calculating the WSSD.
     
     Examples:
-        >>> lattice_vector_wssd_search(n_max = 2**10, d_max = 5)
-        array([1, 403, 361, 281, 421])
-        >>> lattice_vector_wssd_search(n_max = 2**15, d_max = 10)
-        array([1, 4825, 13541, 15249, 15405, 9909, 7493, 11407, 14819, 10089])
+        >>> lattice_vector_wssd_search(n_max=2**10, d_max=5)
+        array([  1, 403, 361, 281, 421])
+        >>> lattice_vector_wssd_search(n_max=2**15, d_max=10)
+        array([    1,  4825, 13541, 15249, 15405,  9909,  7493, 11407, 14819,
+               10089])
         
         Custom coordinate weights
 
-        >>> lattice_vector_wssd_search(n_max = 2**15, d_max = 10, coord_weights = [j**(-1) for j in range(1, 6)])
-        array([1, 4825, 13541, 15249, 7311, 10339, 5933, 6307, 14729, 13037])
+        >>> lattice_vector_wssd_search(n_max=2**15, d_max=10, coord_weights=[j**(-1) for j in range(1, 11)])
+        array([    1,  4825, 13541, 15249,  7311, 10339,  5933,  6307, 14729,
+               13037])
 
         Custom kernels
 
         >>> bernoulli6 = lambda x: x**6 - 3 * x**5 + 5 / 2 * x**4 - 1 / 2 * x**2 + 1 / 42
-        >>> lattice_vector_wssd_search(n_max = 2**15, d_max = 10, coord_weights = None, kernel = bernoulli6)
-        array([1, 1635, 6875, 8665, 8531, 1361, 11771, 10987, 2805, 9961])
+        >>> lattice_vector_wssd_search(n_max=2**15, d_max=10, coord_weights=None, kernel=bernoulli6)
+        array([    1,  1635,  6875,  8665,  8531,  1361, 11771, 10987,  2805,
+                9961])
     """
 
     if kernel is None:

From 24e21959b4ca84c6ff50fa672e8fdd4cc2c66cc2 Mon Sep 17 00:00:00 2001
From: AndersPride <apride@hawk.iit.edu>
Date: Mon, 24 Aug 2026 19:18:19 -0500
Subject: [PATCH 25/29] Apply select Copilot suggestions from code review

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
---
 .../kronecker/kronecker_search_methods.py                    | 2 ++
 qmcpy/discrete_distribution/lattice/lattice.py               | 4 ++--
 .../lattice/lattice_vector_wssd_search.py                    | 5 ++---
 3 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
index b4ef4b77c..d316869a0 100644
--- a/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
+++ b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
@@ -46,6 +46,8 @@ def kronecker_vector_search_mobius_transform(n_max, d_max, searchsize, kernel=No
     # define coordinate weights if not provided, default to j^(-2)
     if coord_weights is None:
         coord_weights = np.array([j**(-2) for j in range(1, d_max + 1)], dtype=np.float64)
+    else:
+        coord_weights = np.asarray(coord_weights, dtype=np.float64)
 
     # search over the first n primes, n = searchsize
     searchspace = np.array(list(sympy.primerange(1, sympy.prime(searchsize)+1)), dtype=np.float64)
diff --git a/qmcpy/discrete_distribution/lattice/lattice.py b/qmcpy/discrete_distribution/lattice/lattice.py
index 3018a06bf..5cc170f9b 100644
--- a/qmcpy/discrete_distribution/lattice/lattice.py
+++ b/qmcpy/discrete_distribution/lattice/lattice.py
@@ -446,9 +446,9 @@ def expected_squared_periodic_discrepancies(self, n_max, coord_weights=None, ker
 
 
     def wssd(self, n_max, coord_weights=None, sample_weights=None):
-        """Returns the weighted sum of the expected squared periodic discrepancies for the first n points of the lattice sequence.
+        """Returns the weighted sum of the expected squared periodic discrepancies for the first n_max points of the lattice sequence.
         Args:
-            n (int): Number of points to calculate the weighted squared periodic discrepancy for.
+            n_max (int): Number of points to calculate the weighted squared periodic discrepancy for.
             coord_weights (Union[None, np.ndarray]): Coordinate weights for the discrepancy calculation. If None, uses weights gamma_j = j^(-2).
             sample_weights (Union[None, np.ndarray]): Sample weights for the weighted squared periodic discrepancy calculation. If None, uses weights w_n = n. Note that the time cost may be higher for other sample weights.
         Returns:
diff --git a/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
index cbca41913..26c0b78b3 100644
--- a/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
+++ b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
@@ -119,7 +119,7 @@ def lattice_vector_wssd_search(n_max, d_max, coord_weights=None, kernel=None):
 
     # Initial 1D case
     rowV = rowVects / 2**m
-    rowV = 1 + kernel(rowV)
+    rowV = 1 + coord_weights[0] * kernel(rowV)
     prodV = prodV * rowV[:, None]
 
     # Set up k0
@@ -134,8 +134,7 @@ def lattice_vector_wssd_search(n_max, d_max, coord_weights=None, kernel=None):
         wssd = np.zeros(2**(m - 2))
 
         gamma = coord_weights[hComp - 1]
-        omega = lambda x: 1 + gamma * (x * (x - 1) + 1 / 6)
-
+        omega = lambda x: 1 + gamma * kernel(x)
         k0 = k0 * (1 + gamma * kernel(0))
 
         curIdx2 = 0

From 0a6bc4e1b32577d96374172fb935414e68040689 Mon Sep 17 00:00:00 2001
From: Anders Pride <apride@hawk.illinoistech.edu>
Date: Mon, 24 Aug 2026 19:25:53 -0500
Subject: [PATCH 26/29] Addresses issues in lattice.py raised by Copilot:
 Improved docstring consistency Fixed two instances where a previous approach
 was not properly updated

---
 qmcpy/discrete_distribution/lattice/lattice.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qmcpy/discrete_distribution/lattice/lattice.py b/qmcpy/discrete_distribution/lattice/lattice.py
index 3018a06bf..890dec409 100644
--- a/qmcpy/discrete_distribution/lattice/lattice.py
+++ b/qmcpy/discrete_distribution/lattice/lattice.py
@@ -441,7 +441,7 @@ def expected_squared_periodic_discrepancies(self, n_max, coord_weights=None, ker
             freq_mtx[:,i] = freq_mtx[:,i] * (i + 1)**(-2)
 
         # multiply by the frequency matrix and add the constant vector
-        discs = k_const + np.vecmat(k_sum, freq_mtx)
+        discs = k_const + (k_sum @ freq_mtx)
         return discs
 
 
@@ -457,7 +457,7 @@ def wssd(self, n_max, coord_weights=None, sample_weights=None):
         if coord_weights is not None and len(coord_weights) < self.d:
             raise ValueError("Length of coord_weights must be greater than or equal to the dimension of the lattice")
         if sample_weights is not None and len(sample_weights) < n_max:
-            raise ValueError("Length of sample_weights must be equal to n_max")
+            raise ValueError("Length of sample_weights must be at least n_max")
         if sample_weights is None:
             sample_weights = np.arange(1, n_max + 1, dtype=np.float64)
 

From f223ce76eede51e50c4d1d4a0272b45370060b52 Mon Sep 17 00:00:00 2001
From: Anders Pride <apride@hawk.illinoistech.edu>
Date: Mon, 24 Aug 2026 22:06:31 -0500
Subject: [PATCH 27/29] Addresses sympy dependency and vector placement - Adds
 exception handling for import sympy. Prompts user to install sympy or choose
 to use the slower, recursive implementation instead. - Removes
 kuo.lattice-39102-1024-1048576.3600.txt file from demos and replaces it with
 a .npy file in the lattice\generating_vectors folder, like the other Kuo
 vector. Also edits lattice.py to accommodate this. Changes the demo notebook
 to use this vector, fairer comparison. - Adds the kronecker vector .txt file
 to pyproject.toml so that it is included in the package.

---
 demos/kuo.lattice-39102-1024-1048576.3600.txt | 3600 -----------------
 demos/lattice_kronecker_methods.ipynb         |   34 +-
 pyproject.toml                                |    1 +
 .../kronecker/kronecker_search_methods.py     |   81 +-
 .../kuo.lattice-39102-1024-1048576.3600.npy   |  Bin 0 -> 28880 bytes
 .../discrete_distribution/lattice/lattice.py  |   11 +
 6 files changed, 102 insertions(+), 3625 deletions(-)
 delete mode 100644 demos/kuo.lattice-39102-1024-1048576.3600.txt
 create mode 100644 qmcpy/discrete_distribution/lattice/generating_vectors/kuo.lattice-39102-1024-1048576.3600.npy

diff --git a/demos/kuo.lattice-39102-1024-1048576.3600.txt b/demos/kuo.lattice-39102-1024-1048576.3600.txt
deleted file mode 100644
index e147de362..000000000
--- a/demos/kuo.lattice-39102-1024-1048576.3600.txt
+++ /dev/null
@@ -1,3600 +0,0 @@
-    1         1
-    2    433461
-    3    472323
-    4    440637
-    5    231645
-    6    275007
-    7    113895
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-  196    481205
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diff --git a/demos/lattice_kronecker_methods.ipynb b/demos/lattice_kronecker_methods.ipynb
index d8e721f27..7287b212c 100644
--- a/demos/lattice_kronecker_methods.ipynb
+++ b/demos/lattice_kronecker_methods.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "2e06ea48",
    "metadata": {},
    "outputs": [],
@@ -47,7 +47,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "17.68626758525142\n"
+      "63.560193378614166\n"
      ]
     }
    ],
@@ -82,7 +82,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[11.0370278]\n"
+      "[39.95402303]\n"
      ]
     }
    ],
@@ -127,7 +127,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken for lattice vector wssd search:  0.17128682136535645\n",
+      "Time taken for lattice vector wssd search:  0.1646568775177002\n",
       "Searched lattice vector:  [    1  4825 13541 15249 15405  9909  7493 11407 14819 10089  3683  3347\n",
       " 13789  8837  5309  6307  6447 12103  9097  2767]\n"
      ]
@@ -164,11 +164,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken for kronecker vector wssd search:  5.96858549118042\n",
+      "Time taken for kronecker vector wssd search:  4.109135389328003\n",
       "Searched Kronecker vector:  [0.61803399 0.26774665 0.91444648 0.22708655 0.12137476 0.71267465\n",
       " 0.69787961 0.10230792 0.18609503 0.31195642 0.41561801 0.13176115\n",
       " 0.22004561 0.56882224 0.1079203  0.10500649 0.16477572 0.85934099\n",
-      " 0.79691446 0.21233239]\n"
+      " 0.20308554 0.43452109]\n"
      ]
     }
    ],
@@ -197,13 +197,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "9f66d72b",
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "text/plain": [
+       "Text(0, 0.5, 'Periodic Discrepancy')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1200x1000 with 1 Axes>"
       ]
@@ -213,13 +223,13 @@
     }
    ],
    "source": [
-    "lat1 = Lattice(dimension=dim, order=\"RADICAL_INVERSE\", seed=12, generating_vector=\"kuo.lattice-33002-1024-1048576.9125.txt\", m_max=20) # initialize a lattice with the generating vector of all 1s\n",
+    "lat1 = Lattice(dimension=dim, order=\"RADICAL_INVERSE\", seed=12, generating_vector=\"kuo.lattice-39102-1024-1048576.3600.txt\", m_max=20)\n",
     "lat_discs1 = lat1.expected_squared_periodic_discrepancies(n_max=n, coord_weights=coord_weights)\n",
     "\n",
-    "\n",
-    "lat2 = Lattice(dimension=dim, order=\"RADICAL_INVERSE\", seed=12, generating_vector=np.uint64(searched_lattice_vector), m_max=20) # initialize a lattice with the searched lattice vector\n",
+    "lat2 = Lattice(dimension=dim, order=\"RADICAL_INVERSE\", seed=12, generating_vector=np.uint64(searched_lattice_vector), m_max=20)\n",
     "lat_discs2 = lat2.expected_squared_periodic_discrepancies(n_max=n, coord_weights=coord_weights)\n",
     "\n",
+    "# Note that the new lattice rule beats the Kuo lattice rule for low sample sizes, but they are comparable closer to n = 2^20.\n",
     "\n",
     "fig, ax = pyplot.subplots(nrows=1, ncols=1, figsize=(12,10))\n",
     "ax.plot(np.arange(1, n+1), np.sqrt(lat_discs1), label=\"Kuo Lattice Discrepancy\")\n",
@@ -249,7 +259,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.13"
+   "version": "3.12.14"
   }
  },
  "nbformat": 4,
diff --git a/pyproject.toml b/pyproject.toml
index 704a112bc..9a273cb9a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -164,6 +164,7 @@ includes = [
     "qmcpy",
     "qmcpy/discrete_distribution/digital_net_b2/generating_matrices/*.npy",
     "qmcpy/discrete_distribution/lattice/generating_vectors/*.npy",
+    "qmcpy/discrete_distribution/kronecker/generating_vectors/*.txt",
     "qmcpy/util/qmcpy.mplstyle",
     ]
 excludes = []
diff --git a/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
index d316869a0..be4e4fc52 100644
--- a/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
+++ b/qmcpy/discrete_distribution/kronecker/kronecker_search_methods.py
@@ -1,9 +1,10 @@
 import numpy as np
-import sympy
 
 def kronecker_vector_search_mobius_transform(n_max, d_max, searchsize, kernel=None, coord_weights=None, gen_vec_init=None):
     """
-    CBC search method for finding a generating vector for a Kronecker sequence, minimizing the weighted sum of squared discrepancies (WSSD).
+    Note that the sympy package is highly recommended for this search method, though not required.
+
+    A deterministic CBC search method for finding a generating vector for a Kronecker sequence, minimizing the weighted sum of squared discrepancies (WSSD).
         - The first component is gen_vec_init, defaults to the golden ratio.
         - We use a modified mobius transformation f(x) = (a*x + b)/(c*x + d) where a, c are distinct primes and b, d are the two pairs of the smallest positive integers such that |a*d - b*c| = 1.
         - Each subsequent component is found by performing the mobius transformation on the previous component, searching over all pairs of distinct primes from the first searchsize many primes.
@@ -23,7 +24,7 @@ def kronecker_vector_search_mobius_transform(n_max, d_max, searchsize, kernel=No
     Time cost:
         The time cost of the search is O(searchsize^2 * d_max * n_max).
     Approach:
-        Uses the quadratic Bernoulli polynomial kernel to conduct a CBC search for a generating vector, minimizing the weighted sum of squared discrepancies (wssd) with sample weights w_n = n.
+        Conducts a deterministic CBC search for a generating vector, minimizing the weighted sum of squared discrepancies (wssd) with sample weights w_n = n.
     Details on coeff array:
         The coeff array is a (d_max-1) x 4 array where each row corresponds to a dimension from 2 to d_max. The columns correspond to the coefficients of the linear transformation used to compute the gen_vec component for that dimension. Specifically,
         - gen_vec[dim+1] = (coeff[dim, 0] * gen_vec[dim] + coeff[dim, 1]) / (coeff[dim, 2] * gen_vec[dim] + coeff[dim, 3])
@@ -49,9 +50,52 @@ def kronecker_vector_search_mobius_transform(n_max, d_max, searchsize, kernel=No
     else:
         coord_weights = np.asarray(coord_weights, dtype=np.float64)
 
-    # search over the first n primes, n = searchsize
-    searchspace = np.array(list(sympy.primerange(1, sympy.prime(searchsize)+1)), dtype=np.float64)
+    # use sympy if it's already installed, otherwise uses slower and recursive direct implementation
+    try:
+        import sympy
+    except ImportError:
+        print("While not required, installing the sympy package is recommended for this search method. It is used to compute the Bezout coefficients for the linear transformation used in the search. If sympy is not installed, the search will use a recursive and likely slower implementation of the Euclidean algorithm instead.")
+        response = input("Do you want to continue without sympy? (y/n): ")
+        if response.lower() != 'y':
+            raise ImportError("Please install sympy and try again.")
+        else:
+            has_sympy = False
+            print("Continuing without sympy. This may take longer.")
+    else:
+        has_sympy = True
 
+    if has_sympy:
+        # search over the first n primes, n = searchsize
+        searchspace = np.array(list(sympy.primerange(1, sympy.prime(searchsize)+1)), dtype=np.float64)
+    else:
+        def get_primes(n):
+            primes = []
+            num = 2
+            while len(primes) < n:
+                is_prime = True
+                for p in primes:
+                    if p * p > num:
+                        break
+                    if num % p == 0:
+                        is_prime = False
+                        break
+                if is_prime:
+                    primes.append(num)
+                num += 1
+            return primes
+        
+        # search over the first n primes, n = searchsize
+        searchspace = np.array(get_primes(searchsize), dtype=np.float64)
+
+        # we define this method here for convenience, to use in computing Bezout coefficients if necessary
+        def recursive_euclidean_algorithm(a, b):
+            if b == 0:
+                return 1, 0, a
+            x1, y1, gcd = recursive_euclidean_algorithm(b, a % b)
+            x = y1
+            y = x1 - (a // b) * y1
+            return x, y, gcd
+    
     # gen_vec is our generating vector, will be found cbc
     gen_vec = np.zeros(d_max, dtype=np.float64)
 
@@ -73,14 +117,25 @@ def kronecker_vector_search_mobius_transform(n_max, d_max, searchsize, kernel=No
 
     # precompute Bezout coefficients for all pairs of primes in the search space
     bezoutCoeffs = np.zeros((searchsize, searchsize))
-    for i in range(searchsize - 1):
-        a = searchspace[i]
-        for j in range(i + 1, searchsize):
-            c = searchspace[j]
-            # Use sympy.gcdex to get Bezout coefficients
-            d_coeff, b_coeff, _ = sympy.gcdex(int(a), int(c))
-            bezoutCoeffs[i, j] = np.float64(b_coeff)
-            bezoutCoeffs[j, i] = np.float64(d_coeff)
+    if has_sympy:
+        from sympy.core.intfunc import igcdex
+        for i in range(searchsize - 1):
+            a = searchspace[i]
+            for j in range(i + 1, searchsize):
+                c = searchspace[j]
+                # Use sympy.igcdex to get Bezout coefficients
+                d_coeff, b_coeff, _ = igcdex(int(a), int(c))
+                bezoutCoeffs[i, j] = np.float64(b_coeff)
+                bezoutCoeffs[j, i] = np.float64(d_coeff)
+    else:
+        for i in range(searchsize - 1):
+            a = searchspace[i]
+            for j in range(i + 1, searchsize):
+                c = searchspace[j]
+                # Use the recursive Euclidean algorithm to get Bezout coefficients
+                d_coeff, b_coeff, _ = recursive_euclidean_algorithm(int(a), int(c))
+                bezoutCoeffs[i, j] = np.float64(b_coeff)
+                bezoutCoeffs[j, i] = np.float64(d_coeff)
 
 
     # setting up some useful variables for the search
diff --git a/qmcpy/discrete_distribution/lattice/generating_vectors/kuo.lattice-39102-1024-1048576.3600.npy b/qmcpy/discrete_distribution/lattice/generating_vectors/kuo.lattice-39102-1024-1048576.3600.npy
new file mode 100644
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A{Qv*}

literal 0
HcmV?d00001

diff --git a/qmcpy/discrete_distribution/lattice/lattice.py b/qmcpy/discrete_distribution/lattice/lattice.py
index c9284b85b..5db8d591f 100644
--- a/qmcpy/discrete_distribution/lattice/lattice.py
+++ b/qmcpy/discrete_distribution/lattice/lattice.py
@@ -187,6 +187,17 @@ def __init__(
             )[None, :]
             d_limit = 9125
             n_limit = 1048576
+        elif (
+            isinstance(generating_vector, str)
+            and generating_vector == "kuo.lattice-39102-1024-1048576.3600.txt"
+        ):
+            self.gen_vec_source = generating_vector
+            gen_vec = np.load(
+                dirname(abspath(__file__))
+                + "/generating_vectors/kuo.lattice-39102-1024-1048576.3600.npy"
+            )[None, :]
+            d_limit = 3600
+            n_limit = 1048576
         elif isinstance(generating_vector, str):
             self.gen_vec_source = generating_vector
             assert generating_vector[-4:] == ".txt"

From 8b341338fde9688669ddbddc155f070a3d7f5dd6 Mon Sep 17 00:00:00 2001
From: Anders Pride <apride@hawk.illinoistech.edu>
Date: Tue, 25 Aug 2026 18:54:57 -0500
Subject: [PATCH 28/29] Significantly improves speed of lattice discrepancies
 -New approach in getting the squared discrepancy values for n=1,...,N is
 nearly an order of magnitude faster for large N, with help from Claude. -Also
 includes a fix to a doctest for lattice_vector_wssd_search.

---
 demos/lattice_kronecker_methods.ipynb         |  8 +++----
 .../discrete_distribution/lattice/lattice.py  | 23 +++++++++++--------
 .../lattice/lattice_vector_wssd_search.py     |  3 +--
 3 files changed, 18 insertions(+), 16 deletions(-)

diff --git a/demos/lattice_kronecker_methods.ipynb b/demos/lattice_kronecker_methods.ipynb
index 7287b212c..941bd9fb0 100644
--- a/demos/lattice_kronecker_methods.ipynb
+++ b/demos/lattice_kronecker_methods.ipynb
@@ -47,7 +47,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "63.560193378614166\n"
+      "63.560193378767764\n"
      ]
     }
    ],
@@ -127,7 +127,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken for lattice vector wssd search:  0.1646568775177002\n",
+      "Time taken for lattice vector wssd search:  0.16274404525756836\n",
       "Searched lattice vector:  [    1  4825 13541 15249 15405  9909  7493 11407 14819 10089  3683  3347\n",
       " 13789  8837  5309  6307  6447 12103  9097  2767]\n"
      ]
@@ -164,7 +164,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken for kronecker vector wssd search:  4.109135389328003\n",
+      "Time taken for kronecker vector wssd search:  3.8035733699798584\n",
       "Searched Kronecker vector:  [0.61803399 0.26774665 0.91444648 0.22708655 0.12137476 0.71267465\n",
       " 0.69787961 0.10230792 0.18609503 0.31195642 0.41561801 0.13176115\n",
       " 0.22004561 0.56882224 0.1079203  0.10500649 0.16477572 0.85934099\n",
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "9f66d72b",
    "metadata": {},
    "outputs": [
diff --git a/qmcpy/discrete_distribution/lattice/lattice.py b/qmcpy/discrete_distribution/lattice/lattice.py
index 5db8d591f..8124542bc 100644
--- a/qmcpy/discrete_distribution/lattice/lattice.py
+++ b/qmcpy/discrete_distribution/lattice/lattice.py
@@ -440,16 +440,19 @@ def expected_squared_periodic_discrepancies(self, n_max, coord_weights=None, ker
         for i in range(k_sum.size):
             k_sum[i] = np.sum(k_vector[2**i:(2**(i+1))])
 
-        # get the frequency matrix for how often each kernel evaluation appears (this is always the same and can be precomputed, not done here to avoid adding >1GB txt file to git)
-        freq_mtx = np.zeros((k_sum.size, n_max), dtype=np.float64)
-        for i in range(1,n_max):
-            for j in range(k_sum.size):
-                if np.floor(i / 2**j) % 2 == 1:
-                    freq_mtx[j, i] = freq_mtx[j, i-1] + 2
-                else:
-                    freq_mtx[j, i] = freq_mtx[j, i-1]         
-        for i in range(n_max):
-            freq_mtx[:,i] = freq_mtx[:,i] * (i + 1)**(-2)
+        # get the frequency matrix for how often each kernel evaluation appears
+        # this is always the same and can be precomputed, but for values of n_max large enough to matter (~ 2^25)
+        # the precomputed file is >1GB and would take longer to load than to compute
+        i = np.arange(2**k_sum.size)
+        pattern = np.zeros((k_sum.size, 2**k_sum.size), dtype=np.float64) # start with the pattern for the full power of two
+        for l in range(k_sum.size):
+            pattern[l] = ((i >> (l)) & 1) * 2
+
+        # truncate the matrix to the correct size, get the cumsum and divide by the square of the index
+        pattern = pattern[:, :n_max]
+        freq_mtx = np.cumsum(pattern, axis=1)
+        divisor = np.arange(1, n_max + 1) ** 2
+        freq_mtx /= divisor
 
         # multiply by the frequency matrix and add the constant vector
         discs = k_const + (k_sum @ freq_mtx)
diff --git a/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
index 26c0b78b3..32aeb4398 100644
--- a/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
+++ b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
@@ -34,8 +34,7 @@ def lattice_vector_wssd_search(n_max, d_max, coord_weights=None, kernel=None):
 
         >>> bernoulli6 = lambda x: x**6 - 3 * x**5 + 5 / 2 * x**4 - 1 / 2 * x**2 + 1 / 42
         >>> lattice_vector_wssd_search(n_max=2**15, d_max=10, coord_weights=None, kernel=bernoulli6)
-        array([    1,  1635,  6875,  8665,  8531,  1361, 11771, 10987,  2805,
-                9961])
+        array([    1, 12589, 15515,  3957,  1879,  8985, 15139,  9529,  7363,  6089])
     """
 
     if kernel is None:

From 86bae718124e747fe36d333829addd8e26f0e944 Mon Sep 17 00:00:00 2001
From: Anders Pride <apride@hawk.illinoistech.edu>
Date: Tue, 25 Aug 2026 20:08:47 -0500
Subject: [PATCH 29/29] Potential fix to doctest error in lattice search

---
 .../lattice/lattice_vector_wssd_search.py              | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
index 32aeb4398..ce41f80b2 100644
--- a/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
+++ b/qmcpy/discrete_distribution/lattice/lattice_vector_wssd_search.py
@@ -158,13 +158,19 @@ def lattice_vector_wssd_search(n_max, d_max, coord_weights=None, kernel=None):
         wssd = wssd + omega(1 / 2) * prodV[-1, 0]
         wssd = wssd + n_max * k0 - n_max * (n_max + 1) / 2
 
-        bestIdx = int(np.argmin(wssd))
+        # Choose the best candidate, using the smallest index in case of ties to avoid different platforms providing different outputs
+        min_wssd = np.min(wssd)
+        rtol = 1e-15
+        best_indices = np.where(np.abs(wssd - min_wssd) <= rtol * np.abs(min_wssd))[0]
+        bestIdx = int(best_indices[0])
         newH = int(gR[bestIdx])
 
         # Avoid duplicates
         while newH in gen_vec:
             wssd[bestIdx] = np.inf
-            bestIdx = int(np.argmin(wssd))
+            min_wssd = np.min(wssd)
+            best_indices = np.where(np.abs(wssd - min_wssd) <= rtol * np.abs(min_wssd))[0]
+            bestIdx = int(best_indices[0])
             newH = int(gR[bestIdx])
 
         gen_vec[hComp - 1] = newH