diff --git a/sw/cpp/.clang-format b/sw/cpp/.clang-format
deleted file mode 100644
index 4b9f225..0000000
--- a/sw/cpp/.clang-format
+++ /dev/null
@@ -1,28 +0,0 @@
-BasedOnStyle: LLVM
-Language: Cpp
-
-IndentWidth: 4
-UseTab: Never
-#NamespaceIndentation: All
-
-PointerAlignment: Left
-AccessModifierOffset: -4
-AlwaysBreakTemplateDeclarations: true
-LambdaBodyIndentation: Signature
-
-MaxEmptyLinesToKeep: 3
-# ColumnLimit: 128
-
-CompactNamespaces: true
-FixNamespaceComments: true
-
-AllowShortFunctionsOnASingleLine: false
-AllowShortIfStatementsOnASingleLine: true
-
-AlignConsecutiveAssignments: true
-AlignConsecutiveBitFields: true
-AlignConsecutiveDeclarations: true
-AlignConsecutiveMacros: true
-
-#BraceWrapping:
-#  BeforeElse: true
diff --git a/sw/cpp/CMakeLists.txt b/sw/cpp/CMakeLists.txt
deleted file mode 100644
index d7d8baa..0000000
--- a/sw/cpp/CMakeLists.txt
+++ /dev/null
@@ -1,37 +0,0 @@
-cmake_minimum_required (VERSION 3.0)
-project(cpp_decoders)
-
-
-if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
-    message(STATUS "Setting build type to 'Release' as none was specified.")
-    set(CMAKE_BUILD_TYPE
-            Release
-            CACHE STRING "Choose the type of build." FORCE)
-    set_property(
-            CACHE CMAKE_BUILD_TYPE
-            PROPERTY STRINGS
-            "Debug"
-            "Release")
-endif()
-
-set(CMAKE_CXX_STANDARD 23)
-
-find_package(Eigen3 3.3 REQUIRED NO_MODULE)
-find_package(pybind11 CONFIG REQUIRED)
-
-include_directories(${pybind11_INCLUDE_DIRS})
-
-
-find_package(OpenMP REQUIRED)
-
-#add_compile_options(-ffast-math)
-
-pybind11_add_module(cpp_decoders src/python_interface.cpp)
-target_link_libraries(cpp_decoders PRIVATE Eigen3::Eigen OpenMP::OpenMP_CXX)
-
-set(INSTALL_DIR ${CMAKE_CURRENT_SOURCE_DIR}/../cpp_modules)
-
-install(TARGETS cpp_decoders ARCHIVE DESTINATION ${INSTALL_DIR}
-                             LIBRARY DESTINATION ${INSTALL_DIR}
-                             RUNTIME DESTINATION ${INSTALL_DIR})
-
diff --git a/sw/cpp/src/proximal.h b/sw/cpp/src/proximal.h
deleted file mode 100644
index 42e47b9..0000000
--- a/sw/cpp/src/proximal.h
+++ /dev/null
@@ -1,277 +0,0 @@
-#pragma once
-
-#include <Eigen/Dense>
-#include <bit>
-#include <cstdlib>
-#include <iostream>
-#include <stdexcept>
-
-#include <pybind11/eigen.h>
-#include <pybind11/stl.h>
-
-
-/*
- *
- * Using declarations
- *
- */
-
-
-template <int t_rows, int t_cols>
-using MatrixiR = Eigen::Matrix<int, t_rows, t_cols, Eigen::RowMajor>;
-
-template <int t_rows, int t_cols>
-using MatrixdR = Eigen::Matrix<double, t_rows, t_cols, Eigen::RowMajor>;
-
-template <int t_size>
-using RowVectori = Eigen::RowVector<int, t_size>;
-
-template <int t_size>
-using RowVectord = Eigen::RowVector<double, t_size>;
-
-
-/*
- *
- * Proximal decoder implementation
- *
- */
-
-
-/**
- * @brief Class implementing the Proximal Decoding algorithm. See "Proximal
- * Decoding for LDPC Codes" by Tadashi Wadayama, and Satoshi Takabe.
- * @tparam t_m Number of rows of the H Matrix
- * @tparam t_n Number of columns of the H Matrix
- */
-template <int t_m, int t_n>
-class ProximalDecoder {
-public:
-    /**
-     * @brief Constructor
-     * @param H Parity-Check Matrix
-     * @param K Number of iterations to run while decoding
-     * @param omega Step size
-     * @param gamma Positive constant. Arises in the approximation of the prior
-     * PDF
-     * @param eta Positive constant slightly larger than one. See 3.2,  p. 3
-     */
-    ProximalDecoder(const Eigen::Ref<const MatrixiR<t_m, t_n>>& H, int K,
-                    double omega, double gamma, double eta)
-        : mK(K), mOmega(omega), mGamma(gamma), mEta(eta), mH(H),
-          mH_zero_indices(find_zero(H)) {
-
-        Eigen::setNbThreads(8);
-    }
-
-    /**
-     * @brief Decode a received signal. The algorithm is detailed in 3.2, p.3.
-     * This function assumes a BPSK modulated signal and an AWGN channel.
-     * @param y Vector of received values. (y = x + w, where 'x' is element of
-     * [-1, 1]^n and 'w' is noise)
-     * @return \b std::pair of the form (x_hat, num_iter), x_hat is the most
-     * probably sent codeword and num_iter is the number of iterations that were
-     * performed. If the parity check fails and no valid codeword is reached,
-     * num_iter is -1
-     */
-    std::pair<RowVectori<t_n>, int>
-    decode(const Eigen::Ref<const RowVectord<t_n>>& y) {
-        if (y.size() != mH.cols())
-            throw std::runtime_error("Length of vector must match H matrix");
-
-        RowVectord<t_n> s = RowVectord<t_n>::Zero(t_n);
-        RowVectori<t_n> x_hat;
-        RowVectord<t_n> r;
-
-        for (std::size_t i = 0; i < mK; ++i) {
-            r = s - mOmega * L_awgn(s, y);
-
-            s = projection(r - mGamma * grad_H(r));
-
-            x_hat = s.unaryExpr([](double d) {
-                         uint64_t bits = std::bit_cast<uint64_t>(d);
-                         // Return the sign bit: 1 for negative, 0 for positive
-                         return (bits >> 63);
-                     }).template cast<int>();
-
-            if (check_parity(x_hat)) {
-                return {x_hat, i + 1};
-            }
-        }
-
-        return {x_hat, -1};
-    }
-
-    /**
-     * @brief Decode a received signal an measure error value (x - sign(x_hat))
-     * @param x Transmitted word
-     * @param y Received signal
-     * @return \b std::vector of error values. Each element corresponds to one
-     * iteration of the algorithm
-     */
-    std::vector<double>
-    get_error_values(const Eigen::Ref<const RowVectori<t_n>>& x,
-                     const Eigen::Ref<const RowVectord<t_n>>& y) {
-
-        if (y.size() != mH.cols())
-            throw std::runtime_error("Length of vector must match H matrix");
-
-        std::vector<double> error_values;
-        error_values.reserve(mK);
-
-        RowVectord<t_n> s = RowVectord<t_n>::Zero(t_n);
-        RowVectori<t_n> x_hat;
-        RowVectord<t_n> r;
-
-        for (std::size_t i = 0; i < mK; ++i) {
-            r = s - mOmega * L_awgn(s, y);
-
-            s = projection(r - mGamma * grad_H(r));
-
-            x_hat = s.unaryExpr([](double d) {
-                         uint64_t bits = std::bit_cast<uint64_t>(d);
-                         // Return the sign bit: 1 for negative, 0 for positive
-                         return (bits >> 63);
-                     }).template cast<int>();
-
-            RowVectord<t_n> x_hat_bpsk =
-                -1 * ((2 * x_hat.template cast<double>()).array() - 1).matrix();
-            error_values.push_back(
-                (x.template cast<double>() - x_hat_bpsk).norm());
-
-            if (check_parity(x_hat)) {
-                break;
-            }
-        }
-
-        return error_values;
-    }
-
-    /**
-     * @brief Decode a received signal and measure the norm of the two gradients
-     * at each iteration
-     * @param y
-     * @return \b std::vector of \b std::pair of gradient values. Each element corresponds to
-     * one iteration. Result is of the form [(grad_H_1, grad_L_1), ...]
-     */
-    std::vector<std::pair<double, double>>
-    get_gradient_values(const Eigen::Ref<const RowVectord<t_n>>& y) {
-
-        if (y.size() != mH.cols())
-            throw std::runtime_error("Length of vector must match H matrix");
-
-        std::vector<std::pair<double, double>> gradient_values;
-        gradient_values.reserve(mK);
-
-        RowVectord<t_n> s = RowVectord<t_n>::Zero(t_n);
-        RowVectori<t_n> x_hat;
-        RowVectord<t_n> r;
-
-        for (std::size_t i = 0; i < mK; ++i) {
-            RowVectord<t_n> gradl = L_awgn(s, y);
-            r = s - mOmega * gradl;
-
-            RowVectord<t_n> gradh = grad_H(r);
-            s = projection(r - mGamma * gradh);
-
-            gradient_values.push_back({gradh.norm(), gradl.norm()});
-
-            x_hat = s.unaryExpr([](double d) {
-                         uint64_t bits = std::bit_cast<uint64_t>(d);
-                         // Return the sign bit: 1 for negative, 0 for positive
-                         return (bits >> 63);
-                     }).template cast<int>();
-
-            if (check_parity(x_hat)) {
-                break;
-            }
-        }
-
-        return gradient_values;
-    }
-
-    /**
-     * @brief Get the values of all member variables necessary to recreate an
-     * exact copy of this class. Used for pickling
-     * @return \b std::tuple
-     */
-    auto get_decoder_state() const {
-        return std::tuple(mH, mK, mOmega, mGamma, mEta);
-    }
-
-private:
-    const int    mK;
-    const double mOmega;
-    const double mGamma;
-    const double mEta;
-
-    const MatrixiR<t_m, t_n>        mH;
-    const std::vector<Eigen::Index> mH_zero_indices;
-
-
-    /**
-     * Variation of the negative log-likelihood for the special case of AWGN
-     * noise. See 4.1, p. 4.
-     */
-    static Eigen::RowVectorXd L_awgn(const RowVectord<t_n>& s,
-                                     const RowVectord<t_n>& y) {
-        return s.array() - y.array();
-    }
-
-    /**
-     * @brief Find all indices of a matrix, where the corresponding value is
-     * zero
-     * @return \b std::vector of indices
-     */
-    static std::vector<Eigen::Index> find_zero(MatrixiR<t_m, t_n> mat) {
-        std::vector<Eigen::Index> indices;
-
-        for (Eigen::Index i = 0; i < mat.size(); ++i)
-            if (mat(i) == 0) indices.push_back(i);
-
-        return indices;
-    }
-
-    /**
-     * Gradient of the code-constraint polynomial. See 2.3, p. 2.
-     */
-    RowVectord<t_n> grad_H(const RowVectord<t_n>& x) {
-        MatrixdR<t_m, t_n> A_prod_matrix = x.replicate(t_m, 1);
-
-        for (const auto& index : mH_zero_indices)
-            A_prod_matrix(index) = 1;
-        RowVectord<t_m> A_prods = A_prod_matrix.rowwise().prod();
-
-        RowVectord<t_m> B_terms =
-            (A_prods.array().pow(2) - A_prods.array()).matrix().transpose();
-
-        RowVectord<t_n> B_sums = B_terms * mH.template cast<double>();
-
-        RowVectord<t_n> result = 4 * (x.array().pow(2) - 1) * x.array() +
-                                 (2 * x.array().inverse()) * B_sums.array();
-
-        return result;
-    }
-
-    /**
-     * Perform a parity check for a given codeword.
-     * @param x_hat: codeword to be checked (element of [0, 1]^n)
-     * @return \b True if the parity check passes, i.e. the codeword is valid.
-     * False otherwise
-     */
-    bool check_parity(const RowVectori<t_n>& x_hat) {
-        RowVectori<t_m> syndrome =
-            (mH * x_hat.transpose()).unaryExpr([](int i) { return i % 2; });
-
-        return !(syndrome.count() > 0);
-    }
-
-    /**
-     * Project a vector onto [-eta, eta]^n in order to avoid numerical
-     * instability. Detailed in 3.2, p. 3 (Equation (15)).
-     * @param v Vector to project
-     * @return v clipped to [-eta, eta]^n
-     */
-    RowVectord<t_n> projection(const RowVectord<t_n>& v) {
-        return v.cwiseMin(mEta).cwiseMax(-mEta);
-    }
-};
diff --git a/sw/cpp/src/python_interface.cpp b/sw/cpp/src/python_interface.cpp
deleted file mode 100644
index 4970e75..0000000
--- a/sw/cpp/src/python_interface.cpp
+++ /dev/null
@@ -1,58 +0,0 @@
-#define EIGEN_STACK_ALLOCATION_LIMIT 1048576
-
-#include "proximal.h"
-
-#include <pybind11/pybind11.h>
-
-
-namespace py = pybind11;
-using namespace pybind11::literals;
-
-
-#define DEF_PROXIMAL_DECODER(name, m, n)                                       \
-    py::class_<ProximalDecoder<m, n>>(proximal, name)                          \
-        .def(py::init<MatrixiR<m, n>, int, double, double, double>(),          \
-             "H"_a.noconvert(), "K"_a = 100, "omega"_a = 0.0002,               \
-             "gamma"_a = .05, "eta"_a = 1.5)                                   \
-        .def("decode", &ProximalDecoder<m, n>::decode, "y"_a.noconvert())      \
-        .def("get_error_values", &ProximalDecoder<m, n>::get_error_values,     \
-             "x"_a.noconvert(), "y"_a.noconvert())                             \
-        .def("get_gradient_values",                                            \
-             &ProximalDecoder<m, n>::get_gradient_values, "y"_a.noconvert())   \
-        .def(py::pickle(                                                       \
-            [](const ProximalDecoder<m, n>& a) {                               \
-                auto state = a.get_decoder_state();                            \
-                                                                               \
-                MatrixiR<m, n> H     = std::get<0>(state);                     \
-                int            K     = std::get<1>(state);                     \
-                double         omega = std::get<2>(state);                     \
-                double         gamma = std::get<3>(state);                     \
-                double         eta   = std::get<4>(state);                     \
-                                                                               \
-                return py::make_tuple(H, K, omega, gamma, eta);                \
-            },                                                                 \
-            [](py::tuple t) {                                                  \
-                return ProximalDecoder<m, n>{                                  \
-                    t[0].cast<MatrixiR<m, n>>(), t[1].cast<int>(),             \
-                    t[2].cast<double>(), t[3].cast<double>(),                  \
-                    t[4].cast<double>()};                                      \
-            }));
-
-
-PYBIND11_MODULE(cpp_decoders, proximal) {
-    proximal.doc() = "Proximal decoder";
-
-    DEF_PROXIMAL_DECODER("ProximalDecoder_7_3", 3, 7)
-    DEF_PROXIMAL_DECODER("ProximalDecoder_31_20", 20, 31)
-    DEF_PROXIMAL_DECODER("ProximalDecoder_31_5", 5, 31)
-    DEF_PROXIMAL_DECODER("ProximalDecoder_96_48", 48, 96)
-    DEF_PROXIMAL_DECODER("ProximalDecoder_204_102", 102, 204)
-    DEF_PROXIMAL_DECODER("ProximalDecoder_408_204", 204, 408)
-    DEF_PROXIMAL_DECODER("ProximalDecoder_Dynamic", Eigen::Dynamic,
-                         Eigen::Dynamic)
-
-    py::register_exception<std::runtime_error>(
-        proximal, "CppException: std::runtime_error");
-    py::register_exception<std::bad_alloc>(proximal,
-                                           "CppException: std::bad_alloc");
-}
\ No newline at end of file
diff --git a/sw/python/README.md b/sw/python/README.md
deleted file mode 100644
index bd6f18e..0000000
--- a/sw/python/README.md
+++ /dev/null
@@ -1,24 +0,0 @@
-# Bachelorarbeit
-
-## Software
-
-Warning: At least a `Python 3.9` or higher is required to properly run the
-scripts in this project
-
-### Create a python virtual environment
-
-In the root directory of this repository run
-
-```bash
-$ python3 -m venv venv
-$ source venv/bin/activate
-$ pip3 install -r requirements.txt
-```
-
-### Run the unit tests
-
-In the root directory of this repository run
-
-```bash
-$ python -m unittest discover test
-```
diff --git a/sw/python/cpp_modules/__init__.py b/sw/python/cpp_modules/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/sw/python/cpp_modules/cpp_decoders.cpython-310-x86_64-linux-gnu.so b/sw/python/cpp_modules/cpp_decoders.cpython-310-x86_64-linux-gnu.so
deleted file mode 100755
index a56c94a..0000000
Binary files a/sw/python/cpp_modules/cpp_decoders.cpython-310-x86_64-linux-gnu.so and /dev/null differ
diff --git a/sw/python/decoders/__init__.py b/sw/python/decoders/__init__.py
deleted file mode 100644
index c051f50..0000000
--- a/sw/python/decoders/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-"""This package contains a number of different decoder implementations for
-LDPC codes."""
diff --git a/sw/python/decoders/maximum_likelihood.py b/sw/python/decoders/maximum_likelihood.py
deleted file mode 100644
index ce76ddc..0000000
--- a/sw/python/decoders/maximum_likelihood.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import numpy as np
-import itertools
-
-
-class MLDecoder:
-    """This class naively implements a soft decision decoder. The decoder
-    calculates the correlation between the received signal and each codeword
-    and then chooses the one with the largest correlation.
-    """
-
-    def __init__(self, G: np.array, H: np.array):
-        """Construct a new SoftDecisionDecoder object.
-
-        :param G: Generator matrix
-        :param H: Parity check matrix
-        """
-        self._G = G
-        self._H = H
-        self._datawords, self._codewords = self._gen_codewords()
-
-        # The codewords, but mapped to [-1, 1]^n
-        self._codewords_bpsk = 1 - 2 * self._codewords
-
-    def _gen_codewords(self) -> np.array:
-        """Generate a list of all possible codewords.
-
-        :return: Numpy array of the form [[codeword_1], [codeword_2], ...]
-        (Each generated codeword is an element of [0, 1]^n)
-        """
-        k, n = self._G.shape
-
-        # Generate a list of all possible data words
-        u_lst = [list(i) for i in itertools.product([0, 1], repeat=k)]
-        u_lst = np.array(u_lst)
-
-        # Map each data word onto a codeword
-        c_lst = np.dot(u_lst, self._G) % 2
-
-        return u_lst, c_lst
-
-    def decode(self, y: np.array) -> np.array:
-        """Decode a received signal.
-
-        This function assumes a BPSK modulated signal.
-
-        :param y: Vector of received values. (y = x + w, where 'x' is
-        element of [-1, 1]^n and 'w' is noise)
-        :return: Most probably sent codeword (element of [0, 1]^k)
-        """
-        correlations = np.dot(self._codewords_bpsk, y)
-
-        return self._codewords[np.argmax(correlations)]
diff --git a/sw/python/decoders/proximal.py b/sw/python/decoders/proximal.py
deleted file mode 100644
index f0aeb81..0000000
--- a/sw/python/decoders/proximal.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import numpy as np
-
-
-class ProximalDecoder:
-    """Class implementing the Proximal Decoding algorithm. See "Proximal
-    Decoding for LDPC Codes"
-    by Tadashi Wadayama, and Satoshi Takabe.
-    """
-
-    def __init__(self, H: np.array, K: int = 1000, omega: float = 0.0002,
-                 gamma: float = 0.05, eta: float = 1.5):
-        """Construct a new ProximalDecoder Object.
-
-        :param H: Parity Check Matrix
-        :param K: Max number of iterations to perform when decoding
-        :param omega: Step size for the gradient descent process
-        :param gamma: Positive constant. Arises in the approximation of the
-        prior PDF
-        :param eta: Positive constant slightly larger than one. See 3.2,  p. 3
-        """
-        self._H = H
-        self._K = K
-        self._step_size = omega
-        self._gamma = gamma
-        self._eta = eta
-
-        self._k, self._n = self._H.shape
-        self._H_ne_0 = H != 0
-
-    @staticmethod
-    def _L_awgn(s: np.array, y: np.array) -> np.array:
-        """Variation of the negative log-likelihood for the special case of
-        AWGN noise. See 4.1, p. 4.
-        """
-        return s - y
-
-    def _grad_h(self, x: np.array) -> np.array:
-        """Gradient of the code-constraint polynomial. See 2.3, p. 2."""
-        # Pre-computations
-
-        A_prod_matrix = np.tile(x, (self._k, 1))
-        A_prods = np.prod(A_prod_matrix, axis=1, where=self._H_ne_0)
-
-        # Calculate gradient
-
-        sums = np.dot(A_prods ** 2 - A_prods, self._H)
-
-        result = 4 * (x ** 2 - 1) * x + (2 / x) * sums
-
-        return result
-
-    def _projection(self, v):
-        """Project a vector onto [-eta, eta]^n in order to avoid numerical
-        instability. Detailed in 3.2, p. 3 (Equation (15)).
-
-        :param v: Vector to project
-        :return: x clipped to [-eta, eta]^n
-        """
-        return np.clip(v, -self._eta, self._eta)
-
-    def _check_parity(self, x_hat: np.array) -> bool:
-        """Perform a parity check for a given codeword.
-
-        :param x_hat: codeword to be checked (element of [0, 1]^n)
-        :return: True if the parity check passes, i.e. the codeword is
-        valid. False otherwise
-        """
-        syndrome = np.dot(self._H, x_hat) % 2
-        return not np.any(syndrome)
-
-    def decode(self, y: np.array) -> np.array:
-        """Decode a received signal. The algorithm is detailed in 3.2, p.3.
-
-        This function assumes a BPSK modulated signal and an AWGN channel.
-
-        :param y: Vector of received values. (y = x + w, where 'x' is
-        element of [-1, 1]^n and 'w' is noise)
-        :return: Most probably sent codeword (element of [0, 1]^n). If
-        decoding fails, the returned value is 'None'
-        """
-        s = np.zeros(self._n)
-        x_hat = np.zeros(self._n)
-        for k in range(self._K):
-            r = s - self._step_size * self._L_awgn(s, y)
-
-            s = r - self._gamma * self._grad_h(r)
-            s = self._projection(s)  # Equation (15)
-
-            x_hat = np.sign(s)
-
-            # Map the codeword from [ -1, 1]^n to [0, 1]^n
-            x_hat = (x_hat == -1) * 1
-
-            if self._check_parity(x_hat):
-                return x_hat
-
-        return None
diff --git a/sw/python/requirements.txt b/sw/python/requirements.txt
deleted file mode 100644
index 0fe1d13..0000000
--- a/sw/python/requirements.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-matplotlib==3.6.2
-pandas==1.5.2
-seaborn==0.12.1
-tqdm==4.64.1
diff --git a/sw/python/res/204.33.484.alist b/sw/python/res/204.33.484.alist
deleted file mode 100644
index c3871ba..0000000
--- a/sw/python/res/204.33.484.alist
+++ /dev/null
@@ -1,310 +0,0 @@
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diff --git a/sw/python/res/204.33.486.alist b/sw/python/res/204.33.486.alist
deleted file mode 100644
index 8d6c749..0000000
--- a/sw/python/res/204.33.486.alist
+++ /dev/null
@@ -1,310 +0,0 @@
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diff --git a/sw/python/res/204.55.187.alist b/sw/python/res/204.55.187.alist
deleted file mode 100644
index b36a0d0..0000000
--- a/sw/python/res/204.55.187.alist
+++ /dev/null
@@ -1,310 +0,0 @@
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diff --git a/sw/python/res/408.33.844.alist b/sw/python/res/408.33.844.alist
deleted file mode 100644
index f70918f..0000000
--- a/sw/python/res/408.33.844.alist
+++ /dev/null
@@ -1,616 +0,0 @@
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diff --git a/sw/python/res/816.1A4.845.alist b/sw/python/res/816.1A4.845.alist
deleted file mode 100644
index 7206495..0000000
--- a/sw/python/res/816.1A4.845.alist
+++ /dev/null
@@ -1,1364 +0,0 @@
-816 544
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diff --git a/sw/python/res/96.3.965.alist b/sw/python/res/96.3.965.alist
deleted file mode 100644
index 9ec6591..0000000
--- a/sw/python/res/96.3.965.alist
+++ /dev/null
@@ -1,148 +0,0 @@
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diff --git a/sw/python/res/999.111.3.5543.alist b/sw/python/res/999.111.3.5543.alist
deleted file mode 100644
index 6d07c8d..0000000
--- a/sw/python/res/999.111.3.5543.alist
+++ /dev/null
@@ -1,1114 +0,0 @@
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diff --git a/sw/python/res/999.111.3.5565.alist b/sw/python/res/999.111.3.5565.alist
deleted file mode 100644
index 7130dd6..0000000
--- a/sw/python/res/999.111.3.5565.alist
+++ /dev/null
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diff --git a/sw/python/res/BCH_31_11.alist b/sw/python/res/BCH_31_11.alist
deleted file mode 100644
index 206145c..0000000
--- a/sw/python/res/BCH_31_11.alist
+++ /dev/null
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diff --git a/sw/python/res/BCH_31_26.alist b/sw/python/res/BCH_31_26.alist
deleted file mode 100644
index 7a22846..0000000
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diff --git a/sw/python/res/BCH_7_4.alist b/sw/python/res/BCH_7_4.alist
deleted file mode 100644
index 3ec087e..0000000
--- a/sw/python/res/BCH_7_4.alist
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diff --git a/sw/python/res/PEGReg252x504.alist b/sw/python/res/PEGReg252x504.alist
deleted file mode 100644
index e65e3dd..0000000
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diff --git a/sw/python/scripts/proximal/simulate_2d_BER.py b/sw/python/scripts/proximal/simulate_2d_BER.py
deleted file mode 100644
index e3cfb37..0000000
--- a/sw/python/scripts/proximal/simulate_2d_BER.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import sys, os
-import sys, os
-sys.path.append(os.path.abspath('../..'))
-print(sys.path)
-
-
-import numpy as np
-import seaborn as sns
-import matplotlib.pyplot as plt
-import signal
-from timeit import default_timer
-from functools import partial
-
-from utility import codes, noise, misc
-from utility.simulation.simulators import GenericMultithreadedSimulator
-from utility.simulation import SimulationManager
-
-from cpp_modules.cpp_decoders import ProximalDecoder_204_102 as ProximalDecoder
-
-
-def task_func(params):
-    """Function called by the GenericMultithreadedSimulator instance.
-
-    Calculate the BER, FER, and DFR  for a given SNR and gamma.
-    """
-    signal.signal(signal.SIGINT, signal.SIG_IGN)
-
-    decoder = params["decoder"]
-    max_iterations = params["max_iterations"]
-    SNR = params["SNR"]
-    n = params["n"]
-    k = params["k"]
-
-    c = np.zeros(n)
-    x_bpsk = c + 1
-
-    total_bit_errors = 0
-    total_frame_errors = 0
-    dec_fails = 0
-
-    num_iterations = 0
-
-    for i in range(max_iterations):
-        x = noise.add_awgn(x_bpsk, SNR, n, k)
-        x_hat, k_max = decoder.decode(x)
-
-        bit_errors = misc.count_bit_errors(x_hat, c)
-        if bit_errors > 0:
-            total_bit_errors += bit_errors
-            total_frame_errors += 1
-
-        num_iterations += 1
-
-        if k_max == -1:
-            dec_fails += 1
-
-        if total_frame_errors > 100:
-            break
-
-    BER = total_bit_errors / (num_iterations * n)
-    FER = total_frame_errors / num_iterations
-    DFR = dec_fails / (num_iterations + dec_fails)
-
-    return {"BER": BER, "FER": FER, "DFR": DFR,
-            "num_iterations": num_iterations}
-
-
-def get_params(code_name: str):
-    """In this function all parameters for the simulation are defined."""
-    # Define global simulation parameters
-
-    H_file = f"../../res/{code_name}.alist"
-
-    H = codes.read_alist_file(H_file)
-    n_min_k, n = H.shape
-    k = n - n_min_k
-
-    omega = 0.05
-    K = 100
-    gammas = np.arange(0.0, 0.17, 0.01)
-
-    SNRs = np.arange(1, 6, 0.5)
-    max_iterations = 20000
-
-    # Define parameters different for each task
-
-    task_params = []
-    for i, SNR in enumerate(SNRs):
-        for j, gamma in enumerate(gammas):
-            decoder = ProximalDecoder(H=H.astype('int32'), K=K, omega=omega,
-                                      gamma=gamma)
-
-            task_params.append(
-                {"decoder": decoder, "max_iterations": max_iterations,
-                 "SNR": SNR, "gamma": gamma, "n": n, "k": k})
-
-    return omega, K, task_params
-
-
-def configure_new_simulation(sim_mgr: SimulationManager, code_name: str,
-                             sim_name: str) -> None:
-    sim = GenericMultithreadedSimulator()
-
-    omega, K, task_params = get_params(code_name)
-
-    sim.task_params = task_params
-    sim.task_func = task_func
-    sim.format_func = partial(misc.pgf_reformat_data_3d, x_param_name="SNR",
-                              y_param_name="gamma",
-                              z_param_names=["BER", "FER", "DFR",
-                                             "num_iterations"])
-
-    sim_mgr.configure_simulation(simulator=sim, name=sim_name,
-                                 additional_metadata={"omega": omega, "K": K})
-
-
-def main():
-    # code_name = "BCH_7_4"
-    # code_name = "BCH_31_11"
-    # code_name = "BCH_31_26"
-    # code_name = "96.3.965"
-    # code_name = "204.33.486"
-    code_name = "204.33.484"
-    # code_name = "204.55.187"
-    # code_name = "408.33.844"
-
-    sim_name = f"2d_BER_FER_DFR_{misc.slugify(code_name)}"
-
-    # Run simulation
-
-    sim_mgr = SimulationManager(saves_dir="sim_saves",
-                                results_dir="sim_results")
-
-    unfinished_sims = sim_mgr.get_unfinished()
-    if len(unfinished_sims) > 0:
-        sim_mgr.load_unfinished(unfinished_sims[0])
-    else:
-        configure_new_simulation(sim_mgr=sim_mgr, code_name=code_name,
-                                 sim_name=sim_name)
-
-    sim_mgr.simulate()
-
-    # Plot results
-
-    sns.set_theme()
-    ax = sns.lineplot(data=sim_mgr.get_current_results(), x="SNR", y="BER",
-                      hue="gamma")
-    ax.set_yscale('log')
-    ax.set_ylim((5e-5, 2e-0))
-    plt.show()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/sw/python/scripts/proximal/simulate_2d_avg_error.py b/sw/python/scripts/proximal/simulate_2d_avg_error.py
deleted file mode 100644
index 975baa4..0000000
--- a/sw/python/scripts/proximal/simulate_2d_avg_error.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import sys, os
-sys.path.append(os.path.abspath('../..'))
-
-import numpy as np
-import seaborn as sns
-import matplotlib.pyplot as plt
-import signal
-from timeit import default_timer
-from functools import partial
-import pandas as pd
-
-from utility import codes, noise, misc
-from utility.simulation.simulators import GenericMultithreadedSimulator
-from utility.simulation import SimulationManager
-
-from cpp_modules.cpp_decoders import ProximalDecoder_204_102 as ProximalDecoder
-
-
-def task_func(params):
-    """Function called by the GenericMultithreadedSimulator instance.
-
-    Calculate the average error over a number of iterations.
-    """
-    signal.signal(signal.SIGINT, signal.SIG_IGN)
-
-    decoder = params["decoder"]
-    num_iterations = params["num_iterations"]
-    x_bpsk = params["x_bpsk"]
-    SNR = params["SNR"]
-    n = params["n"]
-    k = params["k"]
-    K = params["K"]
-
-    avg_error_values = np.zeros(K)
-
-    for i in range(num_iterations):
-        x = noise.add_awgn(x_bpsk, SNR, n, k)
-
-        error_values = decoder.get_error_values(x_bpsk.astype('int32'), x)
-
-        for j, val in enumerate(error_values):
-            avg_error_values[j] += val
-
-    avg_error_values = avg_error_values / num_iterations
-
-    return {"err": avg_error_values}
-
-
-def get_params(code_name: str):
-    """In this function all parameters for the simulation are defined."""
-    # Define global simulation parameters
-
-    H_file = f"../../res/{code_name}.alist"
-    H = codes.read_alist_file(H_file)
-    n_min_k, n = H.shape
-    k = n - n_min_k
-
-    SNR = 8
-    omegas = np.logspace(-0, -10, 40)
-    K = 200
-
-    num_iterations = 1000
-    x_bpsk = np.zeros(n) + 1
-
-    # Define parameters different for each task
-
-    task_params = []
-    for i, omega in enumerate(omegas):
-        decoder = ProximalDecoder(H=H.astype('int32'), K=K,
-                                  omega=omega)
-        task_params.append(
-            {"decoder": decoder, "num_iterations": num_iterations,
-             "x_bpsk": x_bpsk, "SNR": SNR, "n": n, "k": k, "K": K,
-             "omega": omega})
-
-    return SNR, K, task_params
-
-
-def reformat_data(results):
-    """Reformat the data obtained from the GenericMultithreadedSimulator to
-    be usable by pgfplots.
-    """
-    K = 200
-    num_points = len(results) * K
-
-    x = np.zeros(num_points)
-    y = np.zeros(num_points)
-    z = np.zeros(num_points)
-
-    for i, (params, result) in enumerate(results.items()):
-        np.put(x, np.arange(i * K, (i + 1) * K), np.arange(1, K+1))
-        np.put(y, np.arange(i * K, (i + 1) * K), params["omega"])
-        np.put(z, np.arange(i * K, (i + 1) * K), result["err"])
-
-    x = x[::4]
-    y = y[::4]
-    z = z[::4]
-
-    df = pd.DataFrame({"k": x, "omega": y, "err": z}).sort_values(
-        by=['k', 'omega'], ascending=[True, False])
-
-    return df
-
-
-def configure_new_simulation(sim_mgr: SimulationManager, code_name: str,
-                             sim_name: str) -> None:
-    sim = GenericMultithreadedSimulator()
-
-    SNR, K, task_params = get_params(code_name)
-
-    sim.task_params = task_params
-    sim.task_func = task_func
-    sim.format_func = reformat_data
-
-    sim_mgr.configure_simulation(simulator=sim, name=sim_name,
-                                 additional_metadata={"SNR": SNR, "K": K})
-
-
-def main():
-    # code_name = "BCH_7_4"
-    # code_name = "BCH_31_11"
-    # code_name = "BCH_31_26"
-    # code_name = "96.3.965"
-    # code_name = "204.33.486"
-    code_name = "204.33.484"
-    # code_name = "204.55.187"
-    # code_name = "408.33.844"
-
-    sim_name = f"2d_avg_error_{misc.slugify(code_name)}"
-
-    # Run simulation
-
-    sim_mgr = SimulationManager(saves_dir="sim_saves",
-                                results_dir="sim_results")
-
-    unfinished_sims = sim_mgr.get_unfinished()
-    if len(unfinished_sims) > 0:
-        sim_mgr.load_unfinished(unfinished_sims[0])
-    else:
-        configure_new_simulation(sim_mgr=sim_mgr, code_name=code_name,
-                                 sim_name=sim_name)
-
-    sim_mgr.simulate()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/sw/python/scripts/proximal/simulate_gradient.py b/sw/python/scripts/proximal/simulate_gradient.py
deleted file mode 100644
index 7b1966e..0000000
--- a/sw/python/scripts/proximal/simulate_gradient.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import sys, os
-sys.path.append(os.path.abspath('../..'))
-
-import numpy as np
-import pandas as pd
-import seaborn as sns
-import matplotlib.pyplot as plt
-import signal
-from timeit import default_timer
-from tqdm import tqdm
-
-from utility import codes, noise, misc
-from utility.simulation.simulators import GenericMultithreadedSimulator
-
-# from cpp_modules.cpp_decoders import ProximalDecoder
-from cpp_modules.cpp_decoders import ProximalDecoder_204_102 as ProximalDecoder
-
-
-def simulate(H_file, SNR, omega, K, gamma):
-    H = codes.read_alist_file(f"../../res/{H_file}")
-    n_min_k, n = H.shape
-    k = n - n_min_k
-
-    decoder = ProximalDecoder(H.astype('int32'), K=K, omega=omega, gamma=gamma)
-
-    c = np.zeros(n)
-    x_bpsk = (c + 1)
-
-    avg_grad_values = np.zeros(shape=(K, 2))
-
-    for i in range(1000):
-        x = noise.add_awgn(x_bpsk, SNR, n, k)
-        grad_values = decoder.get_gradient_values(x)
-
-        for j, (val_h, val_l) in enumerate(grad_values):
-            avg_grad_values[j, 0] += val_h
-            avg_grad_values[j, 1] += val_l
-
-    avg_grad_values = avg_grad_values / 1000
-
-    return avg_grad_values
-
-
-def reformat_data(results):
-    return pd.DataFrame({"k": np.arange(0, results.size // 2, 1), "grad_h": results[:, 0], "grad_l": results[:, 1]})
-
-
-def main():
-    # Set up simulation params
-
-    sim_name = "avg_grad_1dB"
-
-    # H_file = "96.3.965.alist"
-    H_file = "204.33.486.alist"
-    # H_file = "204.33.484.alist"
-    # H_file = "204.55.187.alist"
-    # H_file = "408.33.844.alist"
-    # H_file = "BCH_7_4.alist"
-    # H_file = "BCH_31_11.alist"
-    # H_file = "BCH_31_26.alist"
-
-    SNR = 1
-    omega = 0.05
-    K = 100
-    gamma = 0.05
-
-    # Run simulation
-
-    start_time = default_timer()
-    results = simulate(H_file, SNR, omega, K, gamma)
-    end_time = default_timer()
-
-    print(f"duration: {end_time - start_time}")
-
-    df = reformat_data(results)
-
-    df.to_csv(
-        f"sim_results/{sim_name}_{misc.slugify(H_file)}.csv", index=False)
-
-    sns.set_theme()
-    sns.lineplot(data=df, x="k", y="grad_h")
-    sns.lineplot(data=df, x="k", y="grad_l")
-
-    plt.show()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/sw/python/utility/__init__.py b/sw/python/utility/__init__.py
deleted file mode 100644
index 104b7ec..0000000
--- a/sw/python/utility/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-"""This package contains various utilities that can be used in combination
-with the decoders."""
diff --git a/sw/python/utility/codes.py b/sw/python/utility/codes.py
deleted file mode 100644
index 31d2409..0000000
--- a/sw/python/utility/codes.py
+++ /dev/null
@@ -1,249 +0,0 @@
-"""This file Helper functions for generating an H matrix from alist data.
-Code from https://github.com/gnuradio/gnuradio/blob/master/gr-fec/python/fec
-/LDPC/Generate_LDPC_matrix_functions.py
-"""
-
-import numpy as np
-
-
-#
-# Related to alist files
-#
-
-
-def _parse_alist_header(header):
-    size = header.split()
-    return int(size[0]), int(size[1])
-
-
-def read_alist_file(filename):
-    """
-    This function reads in an alist file and creates the
-    corresponding parity check matrix H. The format of alist
-    files is described at:
-    http://www.inference.phy.cam.ac.uk/mackay/codes/alist.html
-    """
-
-    with open(filename, 'r') as myfile:
-        data = myfile.readlines()
-        numCols, numRows = _parse_alist_header(data[0])
-
-        H = np.zeros((numRows, numCols))
-
-        # The locations of 1s starts in the 5th line of the file
-        for lineNumber in np.arange(4, 4 + numCols):
-            indices = data[lineNumber].split()
-            for index in indices:
-                H[int(index) - 1, lineNumber - 4] = 1
-
-        return H
-
-
-#
-# G matrices of specific codes
-#
-
-
-# @formatter:off
-
-Gs = {'Hamming_7_4': np.array([[1, 0, 0, 0, 0, 1, 1],
-                               [0, 1, 0, 0, 1, 0, 1],
-                               [0, 0, 1, 0, 1, 1, 0],
-                               [0, 0, 0, 1, 1, 1, 1]]),
-
-      'Golay_24_12': np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1],
-                               [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0],
-                               [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1],
-                               [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0],
-                               [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1],
-                               [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0],
-                               [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1],
-                               [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1]]),
-
-      'BCH_15_7': np.array([[1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0],
-                            [0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0],
-                            [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0],
-                            [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1],
-                            [0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0],
-                            [0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1],
-                            [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1]]),
-
-      'BCH_31_6': np.array([[1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1],
-                            [0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0],
-                            [0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1],
-                            [0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1],
-                            [0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1],
-                            [0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1]]),
-
-      'BCH_31_11': np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0],
-                             [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1],
-                             [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0],
-                             [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0],
-                             [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0],
-                             [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0],
-                             [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1]]),
-
-      'BCH_31_16': np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0],
-                             [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1],
-                             [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0],
-                             [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0],
-                             [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0],
-                             [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0],
-                             [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1]]),
-
-      'BCH_31_21': np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0],
-                             [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0],
-                             [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1],
-                             [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0],
-                             [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1],
-                             [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0],
-                             [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1]]),
-
-      'BCH_63_16': np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1],
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-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1]]),
-
-      'BCH_63_30': np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1],
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-
-      # TODO: Fix this. This code should be systematic
-      'BCH_63_45': np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1],
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-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1],
-                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1]])
-      }
-
-# @formatter:on
-
-
-#
-# Utilities for systematic codes
-#
-
-
-def get_systematic_H(G: np.array) -> np.array:
-    """Compute the H matrix for a systematic code.
-
-    :param G: Generator matrix of the systematic code
-    :return: Parity check matrix H
-    """
-    k, n = G.shape
-
-    I = G[:, :k]
-    assert np.array_equal(I, np.identity(k))
-
-    P = G[:, k:]
-
-    H = np.zeros(shape=(n - k, n))
-    H[:, :k] = P.T
-    H[:, k:] = np.identity(n - k)
-
-    return H
diff --git a/sw/python/utility/misc.py b/sw/python/utility/misc.py
deleted file mode 100644
index 6619015..0000000
--- a/sw/python/utility/misc.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import unicodedata
-import re
-import typing
-import pandas as pd
-import numpy as np
-
-
-def slugify(value, allow_unicode=False):
-    """
-    Taken from https://github.com/django/django/blob/master/django/utils
-    /text.py
-    Convert to ASCII if 'allow_unicode' is False. Convert spaces or repeated
-    dashes to single dashes. Remove characters that aren't alphanumerics,
-    underscores, or hyphens. Convert to lowercase. Also strip leading and
-    trailing whitespace, dashes, and underscores.
-    """
-    value = str(value)
-    if allow_unicode:
-        value = unicodedata.normalize('NFKC', value)
-    else:
-        value = unicodedata.normalize('NFKD', value).encode('ascii',
-                                                            'ignore').decode(
-            'ascii')
-    value = re.sub(r'[^\w\s-]', '', value.lower())
-    return re.sub(r'[-\s]+', '-', value).strip('-_')
-
-
-def pgf_reformat_data_3d(results: typing.Sequence, x_param_name: str,
-                         y_param_name: str,
-                         z_param_names: typing.Sequence[str]):
-    """Reformat the results obtained from the GenericMultithreadedSimulator
-    into a form usable by pgfplots.
-
-    :param results: Results from GenericMultiThreadedSimulator
-    (dict of the form {params1: results1, params2: results2, ...}),
-    where resultsN and paramsN are themselves dicts:
-        paramsN = {param_name_1: val, param_name_2: val, ...}
-        resultsN = {result_name_1: val, result_name_2: val, ...}
-    :param x_param_name:
-    :param y_param_name:
-    :param z_param_names:
-    :return: pandas DataFrame of the following form:
-        {x_param_name: [x1, x1, x1, ..., x2, x2, x2, ...],
-        y_param_name: [y1, y2, y3, ..., y1, y2, y3, ...],
-        z_param_name: [z11, z21, z31, ..., z12, z22, z32, ...]}
-    """
-    # Create result variables
-    x = np.zeros(len(results))
-    y = np.zeros(len(results))
-    zs = {name: np.zeros(len(results)) for name in z_param_names}
-
-    # Populate result variables
-    for i, (params, result) in enumerate(results.items()):
-        x_val = params[x_param_name]
-        y_val = params[y_param_name]
-        for z_param_name in z_param_names:
-            zs[z_param_name][i] = result[z_param_name]
-
-        x[i] = x_val
-        y[i] = y_val
-
-    # Create and return pandas DataFrame
-    df = pd.DataFrame({x_param_name: x, y_param_name: y})
-    for z_param_name in z_param_names:
-        df[z_param_name] = zs[z_param_name]
-
-    return df.sort_values(by=[x_param_name, y_param_name])
-
-
-def count_bit_errors(x: np.array, x_hat: np.array) -> int:
-    """Count the number of different bits between two words."""
-    return np.sum(x != x_hat)
diff --git a/sw/python/utility/noise.py b/sw/python/utility/noise.py
deleted file mode 100644
index 33ba026..0000000
--- a/sw/python/utility/noise.py
+++ /dev/null
@@ -1,34 +0,0 @@
-"""Utility functions relating to noise and SNR calculations."""
-
-import numpy as np
-
-
-def get_noise_variance_from_SNR(SNR: float, n: int, k: int) -> float:
-    """Calculate the variance of the noise from an SNR and the signal
-    amplitude.
-
-    :param SNR: Signal-to-Noise-Ratio in dB (E_b/N_0)
-    :param n: Length of a codeword of the used code
-    :param k: Length of a dataword of the used code
-    :return: Variance of the noise
-    """
-    SNR_linear = 10 ** (SNR / 10)
-    variance = 1 / (2 * (k / n) * SNR_linear)
-
-    return variance
-
-
-def add_awgn(c: np.array, SNR: float, n: int, k: int) -> np.array:
-    """Add Additive White Gaussian Noise to a data vector. As this function
-    adds random noise to the input, the output changes, even if it is called
-    multiple times with the same input.
-
-    :param c: Binary vector representing the data to be transmitted
-    :param SNR: Signal-to-Noise-Ratio in dB
-    :param n: Length of a codeword of the used code
-    :param k: Length of a dataword of the used code
-    :return: Data vector with added noise
-    """
-    noise_var = get_noise_variance_from_SNR(SNR, n, k)
-    y = c + np.sqrt(noise_var) * np.random.normal(size=c.size)
-    return y
diff --git a/sw/python/utility/simulation/__init__.py b/sw/python/utility/simulation/__init__.py
deleted file mode 100644
index 27197a0..0000000
--- a/sw/python/utility/simulation/__init__.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""Simulation package.
-
-This package provides a way to easily define simulations in such a way that
-they can be paused and resumed.
-
-General Structure
-=================
-The package consists of 3 main components:
-  - The 'SimulationDeSerializer': Responsible for file IO
-  - The 'Simulator': Responsible for the actual simulating
-  - The 'SimulationManager': Delegates work to the DeSerializer and the
-    Simulator
-
-The Simulator Class
-===================
-For each new simulating task, a new 'Simulator' must be defined. The
-requirements for this class are the following:
-  - Must define the 'start_or_continue()', 'stop()' and
-    'get_current_results()' functions
-  - Must be picklable in order to store the simulation state
-
-An example simulator could look as follows:
-----------------------------------------------------------------
-class SomeSimulator:
-    def __init__(self, num_iterations):
-        self._num_iterations = num_iterations
-        self._current_iter = 0
-
-        self._simulation_running = False
-
-        self._results = pd.DataFrame()
-
-    def _perform_iteration(self):
-        # Perform iteration and append results
-        ...
-
-    def start_or_continue(self) -> None:
-        self._simulation_running = True
-
-        while self._simulation_running and (
-                self._current_iter < self._num_iterations):
-            self._perform_iteration()
-
-    def stop(self) -> None:
-        self._simulation_running = False
-
-    def get_current_results(self) -> pd.DataFrame:
-        return self._results
-----------------------------------------------------------------
-
-Usage
-=====
-To start a new simulation:
-----------------------------------------------------------------
-sim_mgr = SimulationManager(results_dir="results", saves_dir="saves")
-
-sim = SomeSimulator(num_iterations=100)
-sim_mgr.configure_simulation(simulator=sim, name='Some Simulation', \
-                             column_labels=['label1', 'label2'])
-sim_mgr.start()
-----------------------------------------------------------------
-
-To check for a previously interrupted simulation and continue:
-----------------------------------------------------------------
-sim_mgr = SimulationManager(results_dir="results", saves_dir="saves")
-
-unfinished_sims = sim_mgr.get_unfinished()
-
-if len(unfinished_sims) > 0:
-    sim_mgr.load_unfinished(unfinished_sims[0])
-    sim_mgr.simulate()
-----------------------------------------------------------------
-"""
-
-
-from utility.simulation.management import SimulationManager, \
-    SimulationDeSerializer
diff --git a/sw/python/utility/simulation/management.py b/sw/python/utility/simulation/management.py
deleted file mode 100644
index f325929..0000000
--- a/sw/python/utility/simulation/management.py
+++ /dev/null
@@ -1,239 +0,0 @@
-import json
-import pandas as pd
-import typing
-import signal
-import pickle
-import os
-from pathlib import Path
-import platform
-from datetime import datetime
-import timeit
-import collections.abc
-
-from utility import misc
-
-
-class SimulationDeSerializer:
-    """Class responsible for file management, de- and serialization of
-    Simulator objects."""
-
-    def __init__(self, save_dir: str, results_dir: str):
-        self._saves_dir = save_dir
-        self._results_dir = results_dir
-
-        Path(self._saves_dir).mkdir(parents=True, exist_ok=True)
-        Path(self._results_dir).mkdir(parents=True, exist_ok=True)
-
-    def _get_savefile_path(self, sim_name):
-        return f"{self._saves_dir}/{misc.slugify(sim_name)}_state.pickle"
-
-    def _get_metadata_path(self, sim_name):
-        return f"{self._results_dir}/{misc.slugify(sim_name)}_metadata.json"
-
-    def _get_results_path(self, sim_name):
-        return f"{self._results_dir}/{misc.slugify(sim_name)}.csv"
-
-    def _read_metadata(self, sim_name) -> typing.Dict:
-        with open(self._get_metadata_path(sim_name), 'r',
-                  encoding='utf-8') as f:
-            return json.load(f)
-
-    def _save_metadata(self, sim_name, metadata) -> None:
-        with open(self._get_metadata_path(sim_name), 'w+',
-                  encoding='utf-8') as f:
-            json.dump(metadata, f, ensure_ascii=False, indent=4)
-
-    def unfinished_sim_present(self, sim_name: str):
-        """Check if the savefile of a previously paused simulation is
-        present.
-
-        :param sim_name: Name
-        :return: True if a paused simulation with the given name is found
-        """
-        return os.path.isfile(
-            self._get_savefile_path(sim_name)) and os.path.isfile(
-            self._get_metadata_path(sim_name))
-
-    # TODO: Make the directories configurable in the init function
-    def get_unfinished_sims(self) -> typing.List[str]:
-        """Get a list unfinished simulations."""
-        save_files = [f for f in os.listdir(self._saves_dir) if
-                      os.path.isfile(os.path.join(self._saves_dir, f))]
-
-        state_files = [f for f in save_files if f.endswith("_state.pickle")]
-        sim_slugs = [f.removesuffix("_state.pickle") for f in state_files]
-
-        sim_names = [self._read_metadata(slug)["name"] for slug in sim_slugs]
-
-        return sim_names
-
-    def remove_unfinished_sim(self, sim_name: str):
-        """Remove the savefile of a previously paused simulation.
-
-        :param sim_name: Name of the simulation
-        """
-        os.remove(self._get_savefile_path(sim_name))
-        # os.remove(self._get_metadata_path(sim_name))
-
-    def save_state(self, simulator: typing.Any, sim_name: str,
-                   metadata: typing.Dict) -> None:
-        """Save the state of a currently running simulation.
-
-        :param simulator: Simulator object
-        :param sim_name: Name of the simulation
-        :param metadata: Metadata to be saved besides the actual state
-        """
-        # Save metadata
-        self._save_metadata(sim_name, metadata)
-
-        # Save simulation state
-        with open(self._get_savefile_path(sim_name), "wb") as file:
-            pickle.dump(simulator, file)
-
-    def read_state(self, sim_name: str) -> typing.Tuple[
-        typing.Any, typing.Dict]:
-        """Read the saved state of a paused simulation.
-
-        :param sim_name: Name of the simulation
-        :return: Tuple of the form (simulator, metadata)
-        """
-        # Read metadata
-        metadata = self._read_metadata(sim_name)
-
-        # Read simulation state
-        simulator = None
-        with open(self._get_savefile_path(sim_name), "rb") as file:
-            simulator = pickle.load(file)
-
-        return simulator, metadata
-
-    # TODO: Is the simulator object actually necessary here?
-    def save_results(self, simulator: typing.Any, sim_name: str,
-                     metadata: typing.Dict) -> None:
-        """Save simulation results to file.
-
-        :param simulator: Simulator object. Used to obtain the data
-        :param sim_name: Name of the simulation. Determines the filename
-        :param metadata: Metadata to be saved besides the actual simulation
-        results
-        """
-        # Save metadata
-        self._save_metadata(sim_name, metadata)
-
-        # Save current results
-        simulator.current_results.to_csv(self._get_results_path(sim_name),
-                                         index=False)
-
-    def read_results(self, sim_name: str) -> typing.Tuple[
-        pd.DataFrame, typing.Dict]:
-        """Read simulation results from file.
-
-        :param sim_name: Name of the simulation.
-        :return: Tuple of the form (data, metadata), where data is a pandas
-        dataframe and metadata is a dict
-        """
-        # Read metadata
-        metadata = self._read_metadata(sim_name)
-
-        # Read results
-        results = pd.read_csv(self._get_results_path(sim_name))
-
-        return results, metadata
-
-
-# TODO: Autosave simulation every so often
-# TODO: Comment explaining what a Simulator class is
-class SimulationManager:
-    """This class only contains functions relating to stopping and
-    restarting of simulations (and storing of the simulation state in a
-    file, to be resumed at a later date).
-
-    All actual work is outsourced to a provided simulator class.
-    """
-
-    def __init__(self, saves_dir: str, results_dir: str):
-        """Construct a SimulationManager object.
-
-        :param saves_dir: Directory in which the simulation state of a paused
-        simulation should be stored
-        :param results_dir: Directory in which the results of the simulation
-        should be stored
-        """
-        self._de_serializer = SimulationDeSerializer(saves_dir, results_dir)
-
-        self._simulator = None
-        self._sim_name = None
-        self._metadata = {"duration": 0}
-        self._sim_start_time = None
-
-    def _sim_configured(self) -> bool:
-        """Check whether 'configure_simulation()' has been called."""
-        return (self._simulator is not None) and (
-                self._sim_name is not None) and (
-                       self._metadata is not None)
-
-    def configure_simulation(self, simulator: typing.Any, name: str,
-                             additional_metadata: dict = {}) -> None:
-        """Configure a new simulation."""
-        self._simulator = simulator
-        self._sim_name = name
-        self._metadata["name"] = name
-        self._metadata["platform"] = platform.platform()
-        self._metadata.update(additional_metadata)
-
-    def get_unfinished(self) -> typing.List[str]:
-        """Get a list of names of all present unfinished simulations."""
-        return self._de_serializer.get_unfinished_sims()
-
-    def load_unfinished(self, sim_name: str) -> None:
-        """Load the state of an unfinished simulation form its savefile.
-
-        Warning: This function deletes the savefile after loading.
-        """
-        assert self._de_serializer.unfinished_sim_present(sim_name)
-
-        self._sim_name = sim_name
-        self._simulator, self._metadata = self._de_serializer.read_state(
-            sim_name)
-
-        self._de_serializer.remove_unfinished_sim(sim_name)
-
-    # TODO: Metadata is being written twice here. Should save_results() also
-    #  save the metadata?
-    def _exit_gracefully(self, *args) -> None:
-        """Handler called when the program is interrupted. Pauses and saves
-        the currently running simulation."""
-        if self._sim_configured():
-            self._simulator.stop()
-
-            self._metadata["end_time"] = f"{datetime.now(tz=None)}"
-            self._metadata["duration"] \
-                += timeit.default_timer() - self._sim_start_time
-
-            self._de_serializer.save_state(self._simulator, self._sim_name,
-                                           self._metadata)
-            self._de_serializer.save_results(self._simulator, self._sim_name,
-                                             self._metadata)
-
-        exit()
-
-    def simulate(self) -> None:
-        """Start the simulation. This is a blocking call."""
-        assert self._sim_configured()
-
-        try:
-            self._sim_start_time = timeit.default_timer()
-
-            self._simulator.start_or_continue()
-
-            self._metadata["end_time"] = f"{datetime.now(tz=None)}"
-            self._metadata["duration"] \
-                += timeit.default_timer() - self._sim_start_time
-
-            self._de_serializer.save_results(self._simulator, self._sim_name,
-                                             self._metadata)
-        except KeyboardInterrupt:
-            self._exit_gracefully()
-
-    def get_current_results(self) -> pd.DataFrame:
-        return self._simulator.current_results
diff --git a/sw/python/utility/simulation/simulators.py b/sw/python/utility/simulation/simulators.py
deleted file mode 100644
index 6f82984..0000000
--- a/sw/python/utility/simulation/simulators.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import pandas as pd
-import numpy as np
-import typing
-from tqdm import tqdm
-from concurrent.futures import ProcessPoolExecutor, process, wait
-from functools import partial
-from multiprocessing import Lock
-
-from utility import noise
-
-
-# TODO: Fix ProximalDecoder_Dynamic
-# from cpp_modules.cpp_decoders import ProximalDecoder_Dynamic as
-# ProximalDecoder
-
-
-def count_bit_errors(d: np.array, d_hat: np.array) -> int:
-    """Count the number of wrong bits in a decoded codeword.
-
-    :param d: Originally sent data
-    :param d_hat: Received data
-    :return: Number of bit errors
-    """
-    return np.sum(d != d_hat)
-
-
-class HashableDict:
-    """Class behaving like an immutable dict. More importantly it is
-    hashable and thus usable as a key type for another dict."""
-
-    def __init__(self, data_dict):
-        assert (isinstance(data_dict, dict))
-        for key, val in data_dict.items():
-            self.__dict__[key] = val
-
-    def __getitem__(self, item):
-        return self.__dict__[item]
-
-    def __str__(self):
-        return str(self.__dict__)
-
-
-class GenericMultithreadedSimulator:
-    def __init__(self, max_workers=8):
-        self._format_func = None
-        self._task_func = None
-        self._task_params = None
-        self._max_workers = max_workers
-
-        self._results = {}
-        self._executor = None
-
-    @property
-    def task_params(self):
-        return self._task_params
-
-    @task_params.setter
-    def task_params(self, sim_params):
-        self._task_params = {HashableDict(iteration_params): iteration_params
-                             for iteration_params in sim_params}
-
-    @property
-    def task_func(self):
-        return self._task_func
-
-    @task_func.setter
-    def task_func(self, func):
-        self._task_func = func
-
-    @property
-    def format_func(self):
-        return self._format_func
-
-    @format_func.setter
-    def format_func(self, func):
-        self._format_func = func
-
-    def start_or_continue(self):
-        assert self._task_func is not None
-        assert self._task_params is not None
-        assert self._format_func is not None
-
-        self._executor = ProcessPoolExecutor(max_workers=self._max_workers)
-
-        with tqdm(total=(len(self._task_params)), leave=False) as pbar:
-            def done_callback(key, f):
-                try:
-                    pbar.update(1)
-                    self._results[key] = f.result()
-                    del self._task_params[key]
-                except process.BrokenProcessPool:
-                    # This exception is thrown when the program is
-                    # prematurely stopped with a KeyboardInterrupt
-                    pass
-
-            futures = []
-
-            for key, params in list(self._task_params.items()):
-                future = self._executor.submit(self._task_func, params)
-                future.add_done_callback(partial(done_callback, key))
-                futures.append(future)
-
-            self._executor.shutdown(wait=True, cancel_futures=False)
-
-    def stop(self):
-        assert self._executor is not None, "The simulation has to be started" \
-                                           " before it can be stopped"
-        self._executor.shutdown(wait=True, cancel_futures=True)
-
-    @property
-    def current_results(self):
-        return self._format_func(self._results)
-
-    def __getstate__(self):
-        state = self.__dict__.copy()
-        state["_executor"] = None
-        return state
-
-    def __setstate__(self, state):
-        self.__dict__.update(state)
-        self._executor = ProcessPoolExecutor()
diff --git a/sw/python/utility/test/__init__.py b/sw/python/utility/test/__init__.py
deleted file mode 100644
index 2f312a6..0000000
--- a/sw/python/utility/test/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""This package contains unit tests."""
diff --git a/sw/python/utility/test/test_proximal.py b/sw/python/utility/test/test_proximal.py
deleted file mode 100644
index 650b0e6..0000000
--- a/sw/python/utility/test/test_proximal.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import unittest
-import numpy as np
-from decoders import proximal
-
-
-class CheckParityTestCase(unittest.TestCase):
-    """Test case for the check_parity function."""
-
-    def test_check_parity(self):
-        # Hamming(7,4) code
-        G = np.array([[1, 1, 1, 0, 0, 0, 0],
-                      [1, 0, 0, 1, 1, 0, 0],
-                      [0, 1, 0, 1, 0, 1, 0],
-                      [1, 1, 0, 1, 0, 0, 1]])
-        H = np.array([[1, 0, 1, 0, 1, 0, 1],
-                      [0, 1, 1, 0, 0, 1, 1],
-                      [0, 0, 0, 1, 1, 1, 1]])
-        R = np.array([[0, 0, 1, 0, 0, 0, 0],
-                      [0, 0, 0, 0, 1, 0, 0],
-                      [0, 0, 0, 0, 0, 1, 0],
-                      [0, 0, 0, 0, 0, 0, 1]])
-
-        decoder = proximal.ProximalDecoder(H)
-
-        d1 = np.array([0, 1, 0, 1])
-        c1 = np.dot(np.transpose(G), d1) % 2
-
-        d2 = np.array([0, 0, 0, 0])
-        c2 = np.dot(np.transpose(G), d2) % 2
-
-        d3 = np.array([1, 1, 1, 1])
-        c3 = np.dot(np.transpose(G), d3) % 2
-
-        invalid_codeword = np.array([0, 1, 1, 0, 1, 1, 1])
-
-        self.assertEqual(decoder._check_parity(c1), True)
-        self.assertEqual(decoder._check_parity(c2), True)
-        self.assertEqual(decoder._check_parity(c3), True)
-        self.assertEqual(decoder._check_parity(invalid_codeword), False)
-
-
-class GradientTestCase(unittest.TestCase):
-    """Test case for the calculation of the gradient of the
-    code-constraint-polynomial."""
-
-    def test_grad_h(self):
-        """Test the gradient of the code-constraint polynomial."""
-        # Hamming(7,4) code
-        G = np.array([[1, 1, 1, 0, 0, 0, 0],
-                      [1, 0, 0, 1, 1, 0, 0],
-                      [0, 1, 0, 1, 0, 1, 0],
-                      [1, 1, 0, 1, 0, 0, 1]])
-        H = np.array([[1, 0, 1, 0, 1, 0, 1],
-                      [0, 1, 1, 0, 0, 1, 1],
-                      [0, 0, 0, 1, 1, 1, 1]])
-        R = np.array([[0, 0, 1, 0, 0, 0, 0],
-                      [0, 0, 0, 0, 1, 0, 0],
-                      [0, 0, 0, 0, 0, 1, 0],
-                      [0, 0, 0, 0, 0, 0, 1]])
-
-        x = np.array([1, 2, -1, -2, 2, 1, -1])  # Some randomly chosen vector
-        expected_grad_h = np.array(
-            [4, 26, -8, -36, 38, 28, -32])  # Manually calculated result
-
-        decoder = proximal.ProximalDecoder(H)
-        grad_h = decoder._grad_h(x)
-
-        self.assertEqual(np.array_equal(grad_h, expected_grad_h), True)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/sw/python/utility/test/test_soft_decision.py b/sw/python/utility/test/test_soft_decision.py
deleted file mode 100644
index d0c93d6..0000000
--- a/sw/python/utility/test/test_soft_decision.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import unittest
-import numpy as np
-from decoders import maximum_likelihood
-
-
-class CodewordGenerationTestCase(unittest.TestCase):
-    def test_codeword_generation(self):
-        """Test case for data word and code word generation."""
-        # Hamming(7,4) code
-        G = np.array([[1, 1, 1, 0, 0, 0, 0],
-                      [1, 0, 0, 1, 1, 0, 0],
-                      [0, 1, 0, 1, 0, 1, 0],
-                      [1, 1, 0, 1, 0, 0, 1]])
-        H = np.array([[1, 0, 1, 0, 1, 0, 1],
-                      [0, 1, 1, 0, 0, 1, 1],
-                      [0, 0, 0, 1, 1, 1, 1]])
-
-        decoder = maximum_likelihood.MLDecoder(G, H)
-
-        expected_datawords = np.array([[0, 0, 0, 0],
-                                       [0, 0, 0, 1],
-                                       [0, 0, 1, 0],
-                                       [0, 0, 1, 1],
-                                       [0, 1, 0, 0],
-                                       [0, 1, 0, 1],
-                                       [0, 1, 1, 0],
-                                       [0, 1, 1, 1],
-                                       [1, 0, 0, 0],
-                                       [1, 0, 0, 1],
-                                       [1, 0, 1, 0],
-                                       [1, 0, 1, 1],
-                                       [1, 1, 0, 0],
-                                       [1, 1, 0, 1],
-                                       [1, 1, 1, 0],
-                                       [1, 1, 1, 1]])
-
-        expected_codewords = np.array([[0, 0, 0, 0, 0, 0, 0],
-                                       [1, 1, 0, 1, 0, 0, 1],
-                                       [0, 1, 0, 1, 0, 1, 0],
-                                       [1, 0, 0, 0, 0, 1, 1],
-                                       [1, 0, 0, 1, 1, 0, 0],
-                                       [0, 1, 0, 0, 1, 0, 1],
-                                       [1, 1, 0, 0, 1, 1, 0],
-                                       [0, 0, 0, 1, 1, 1, 1],
-                                       [1, 1, 1, 0, 0, 0, 0],
-                                       [0, 0, 1, 1, 0, 0, 1],
-                                       [1, 0, 1, 1, 0, 1, 0],
-                                       [0, 1, 1, 0, 0, 1, 1],
-                                       [0, 1, 1, 1, 1, 0, 0],
-                                       [1, 0, 1, 0, 1, 0, 1],
-                                       [0, 0, 1, 0, 1, 1, 0],
-                                       [1, 1, 1, 1, 1, 1, 1]])
-
-        self.assertEqual(np.array_equal(decoder._datawords, expected_datawords), True)
-        self.assertEqual(np.array_equal(decoder._codewords, expected_codewords), True)
diff --git a/sw/python/utility/test/test_utility.py b/sw/python/utility/test/test_utility.py
deleted file mode 100644
index 2a589ed..0000000
--- a/sw/python/utility/test/test_utility.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import unittest
-import numpy as np
-
-from utility import noise, codes
-
-
-# TODO: Rewrite tests for new SNR calculation
-class NoiseAmpFromSNRTestCase(unittest.TestCase):
-    """Test case for noise amplitude calculation."""
-
-    def test_get_noise_amp_from_SNR(self):
-        SNR1 = 0
-        SNR2 = 3
-        SNR3 = 20
-        SNR4 = -20
-
-        var1 = noise.get_noise_variance_from_SNR(SNR1, n=8, k=8)
-        var2 = noise.get_noise_variance_from_SNR(SNR2, n=8, k=8)
-        var3 = noise.get_noise_variance_from_SNR(SNR3, n=8, k=8)
-        var4 = noise.get_noise_variance_from_SNR(SNR4, n=8, k=8)
-
-        self.assertEqual(var1, 1 * 0.5)
-        self.assertAlmostEqual(var2, 0.5 * 0.5, places=2)
-        self.assertEqual(var3, 0.01 * 0.5)
-        self.assertEqual(var4, 100 * 0.5)
-
-
-class CodesTestCase(unittest.TestCase):
-    """Tests relating to the 'codes' utilities."""
-
-    def test_get_systematic_H(self):
-        # Hamming(7,4) code
-        G = np.array([[1, 0, 0, 0, 0, 1, 1],
-                      [0, 1, 0, 0, 1, 0, 1],
-                      [0, 0, 1, 0, 1, 1, 0],
-                      [0, 0, 0, 1, 1, 1, 1]])
-
-        expected_H = np.array([[0, 1, 1, 1, 1, 0, 0],
-                               [1, 0, 1, 1, 0, 1, 0],
-                               [1, 1, 0, 1, 0, 0, 1]])
-
-        H = codes.get_systematic_H(G)
-
-        self.assertEqual(np.array_equal(expected_H, H), True)
-
-
-if __name__ == '__main__':
-    unittest.main()
diff --git a/sw/python/utility/test/test_visualization.py b/sw/python/utility/test/test_visualization.py
deleted file mode 100644
index a668b77..0000000
--- a/sw/python/utility/test/test_visualization.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import unittest
-from utility import visualization
-
-
-class NumRowsTestCase(unittest.TestCase):
-    def test_get_num_rows(self):
-        """Test case for number of row calculation."""
-
-        num_rows1 = visualization._get_num_rows(num_graphs=4, num_cols=3)
-        expected_rows1 = 2
-
-        num_rows2 = visualization._get_num_rows(num_graphs=5, num_cols=2)
-        expected_rows2 = 3
-
-        num_rows3 = visualization._get_num_rows(num_graphs=4, num_cols=4)
-        expected_rows3 = 1
-
-        num_rows4 = visualization._get_num_rows(num_graphs=4, num_cols=5)
-        expected_rows4 = 1
-
-        self.assertEqual(num_rows1, expected_rows1)
-        self.assertEqual(num_rows2, expected_rows2)
-        self.assertEqual(num_rows3, expected_rows3)
-        self.assertEqual(num_rows4, expected_rows4)
diff --git a/sw/python/utility/visualization.py b/sw/python/utility/visualization.py
deleted file mode 100644
index 46ab790..0000000
--- a/sw/python/utility/visualization.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import seaborn as sns
-import matplotlib.pyplot as plt
-import pandas as pd
-import typing
-from itertools import chain
-import math
-
-
-def _get_num_rows(num_graphs: int, num_cols: int) -> int:
-    """Get the minimum number of rows needed to show a certain number of
-    graphs, given a certain number of columns.
-
-    :param num_graphs: Number of graphs
-    :param num_cols: Number of columns
-    :return: Number of rows
-    """
-    return math.ceil(num_graphs / num_cols)
-
-
-# TODO: Handle number of graphs not nicely fitting into rows and columns
-def plot_BERs(title: str,
-              data: typing.Sequence[
-                  typing.Tuple[str, pd.DataFrame, typing.Sequence[str]]],
-              num_cols: int = 3) -> plt.figure:
-    """This function creates a matplotlib figure containing a number of plots.
-
-    The plots created are logarithmic and the scaling is adjusted to be
-    sensible for BER plots.
-
-    :param title: Title of the figure
-    :param data: Sequence of tuples. Each tuple corresponds to a new plot and
-    is of the following form: [graph_title, pd.Dataframe, [line_label_1,
-    line_label2, ...]]. Each dataframe is assumed to have an "SNR" column
-    that is used as the x axis.
-    :param num_cols: Number of columns in which the graphs should be
-    arranged in the resulting figure
-    :return: Matplotlib figure
-    """
-    # Determine layout and create figure
-
-    num_graphs = len(data)
-    num_rows = _get_num_rows(num_graphs, num_cols)
-
-    fig, axes = plt.subplots(num_rows, num_cols,
-                             figsize=(num_cols * 4, num_rows * 4),
-                             squeeze=False)
-    fig.suptitle(title)
-
-    fig.subplots_adjust(left=0.1,
-                        bottom=0.1,
-                        right=0.9,
-                        top=0.9,
-                        wspace=0.3,
-                        hspace=0.4)
-
-    axes = list(chain.from_iterable(axes))[
-           :num_graphs]  # Flatten the 2d axes array
-
-    # Populate axes
-
-    for axis, (graph_title, df, labels) in zip(axes, data):
-        column_names = [column for column in df.columns.values.tolist() if
-                        not column == "SNR"]
-
-        for column, label in zip(column_names, labels):
-            sns.lineplot(ax=axis, data=df, x="SNR", y=column, label=label)
-
-            axis.set_title(graph_title)
-            axis.set(yscale="log")
-            axis.set_xlabel("SNR")
-            axis.set_ylabel("BER")
-            axis.set_yticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 10e0])
-            axis.legend()
-
-    return fig