diff --git a/cmake-build-debug/_deps/doctest-src b/cmake-build-debug/_deps/doctest-src
new file mode 160000
index 0000000..b7c21ec
--- /dev/null
+++ b/cmake-build-debug/_deps/doctest-src
@@ -0,0 +1 @@
+Subproject commit b7c21ec5ceeadb4951b00396fc1e4642dd347e5f
diff --git a/examples/_deps/doctest-src b/examples/_deps/doctest-src
new file mode 160000
index 0000000..b7c21ec
--- /dev/null
+++ b/examples/_deps/doctest-src
@@ -0,0 +1 @@
+Subproject commit b7c21ec5ceeadb4951b00396fc1e4642dd347e5f
diff --git a/proto/BTCS.ipynb b/proto/BTCS.ipynb
index dc8ff0b..85e3bb2 100644
--- a/proto/BTCS.ipynb
+++ b/proto/BTCS.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,12 +46,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -68,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,8 +138,13 @@
     "    sv = np.zeros(alpha_y.shape[1])\n",
     "\n",
     "    # inner\n",
+    "    # TODO: iterate over columns of alpha_y alpha_y[i_sv, row_index+ -1 .. 0 .. 1]\n",
+    "    # TODO: error with current indices: i_sv starts from 0 --> i_alpha = -1 and i_alpha-1 = -2\n",
     "    for i_sv in range(0, sv.shape[0]):\n",
+    "        print(i_sv)\n",
     "        i_alpha = i_sv-1\n",
+    "        print(i_alpha)\n",
+    "        print(concentrations[i_alpha-1,col_index])\n",
     "        sv[i_sv] = s_y * alpha_interblock(alpha_y[i_alpha+1,col_index],alpha_y[i_alpha,col_index])*concentrations[i_alpha+1,col_index] \\\n",
     "                + (1 - s_y *(alpha_interblock(alpha_y[i_alpha+1,col_index], alpha_y[i_alpha,col_index])) \\\n",
     "                    + alpha_interblock(alpha_y[i_alpha,col_index], alpha_y[i_alpha-1,col_index]))*concentrations[i_alpha,col_index] \\\n",
@@ -168,25 +175,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "[[0.56726545 0.41188268 0.11335637 0.29221913 0.5365537 ]\n",
+      " [0.8120275  0.96249867 0.18743648 0.30176834 0.45374814]\n",
+      " [0.17014599 0.26796332 0.4616269  0.23477139 0.71348435]\n",
+      " [0.00654629 0.61879048 0.22742861 0.25794781 0.91561469]\n",
+      " [0.04155862 0.46501565 0.8463777  0.81575064 0.1985415 ]]\n",
       "Test A:\n",
-      "[[ 2.70902018 -0.77802779  0.          0.          0.        ]\n",
-      " [-0.34610519  2.12413297 -0.77802779  0.          0.        ]\n",
-      " [ 0.         -0.77802779  3.74324914 -1.96522135  0.        ]\n",
-      " [ 0.          0.         -1.96522135  4.21016215 -1.2449408 ]\n",
-      " [ 0.          0.          0.          0.43089455  5.13559523]]\n",
+      "[[ 7.77257437 -1.49738434  0.          0.          0.        ]\n",
+      " [-2.78866756  5.2860519  -1.49738434  0.          0.        ]\n",
+      " [ 0.         -1.49738434  2.94508723 -0.44770289  0.        ]\n",
+      " [ 0.          0.         -0.44770289  1.90753379 -0.4598309 ]\n",
+      " [ 0.          0.          0.          1.69958064  4.50889084]]\n",
+      "0\n",
+      "-1\n",
+      "0.618790482597486\n",
+      "1\n",
+      "0\n",
+      "0.4650156531117613\n",
+      "2\n",
+      "1\n",
+      "0.41188268496900426\n",
+      "3\n",
+      "2\n",
+      "0.9624986706480608\n",
+      "4\n",
+      "3\n",
+      "0.26796331918576755\n",
       "\n",
       "TestB:\n",
-      "[2.45811954 1.70379428 2.70189936 0.92506681 4.0009449 ]\n",
+      "[6.33240747 1.20531698 0.62203484 1.97054078 6.92562211]\n",
       "\n",
       "Result\n",
-      "[1.38151174 1.65087116 1.70267339 1.21472714 0.67714168]\n"
+      "[1.01442248 1.03665212 0.96541206 1.49411867 0.97279954]\n"
      ]
     }
    ],
@@ -195,6 +222,7 @@
     "col = 5\n",
     "\n",
     "testConcentrations = np.random.random((row,col))\n",
+    "print(testConcentrations)\n",
     "testAlpha = np.random.random((row,col))\n",
     "testA = create_coeff_matrix(testAlpha, 1, 3)\n",
     "print(\"Test A:\")\n",
@@ -217,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -257,7 +285,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.9.7"
   },
   "orig_nbformat": 4
  },