diff --git a/.github/workflows/ruff.yml b/.github/workflows/ruff.yml
index 91b7dba4..2cf1ed92 100644
--- a/.github/workflows/ruff.yml
+++ b/.github/workflows/ruff.yml
@@ -6,4 +6,6 @@ jobs:
     continue-on-error: true
     steps:
       - uses: actions/checkout@v3
-      - uses: astral-sh/ruff-action@v1
+      - uses: astral-sh/ruff-action@v3
+        with:
+          args: check --verbose
diff --git a/examples/visualize_mapgen/README.md b/examples/visualize_mapgen/README.md
new file mode 100644
index 00000000..ae4493c7
--- /dev/null
+++ b/examples/visualize_mapgen/README.md
@@ -0,0 +1,7 @@
+# README 
+
+This a file which aides visualization of the geometric objects in mapgen.
+
+So far there is:
+
+* [`make_cart_rect`](https://github.com/djps/k-wave-python/blob/mapgen_plotting/kwave/utils/mapgen.py#L2591)
\ No newline at end of file
diff --git a/examples/visualize_mapgen/visualize_mapgen.ipynb b/examples/visualize_mapgen/visualize_mapgen.ipynb
new file mode 100644
index 00000000..c8c46ef9
--- /dev/null
+++ b/examples/visualize_mapgen/visualize_mapgen.ipynb
@@ -0,0 +1,820 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "2fe1152f",
+      "metadata": {
+        "vscode": {
+          "languageId": "plaintext"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2fe1152f",
+        "outputId": "5ab27206-c063-433c-b83a-8b251e9cc663"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting git+https://github.com/djps/k-wave-python.git@mapgen_plotting\n",
+            "  Cloning https://github.com/djps/k-wave-python.git (to revision mapgen_plotting) to /tmp/pip-req-build-u_zwwah3\n",
+            "  Running command git clone --filter=blob:none --quiet https://github.com/djps/k-wave-python.git /tmp/pip-req-build-u_zwwah3\n",
+            "  Running command git checkout -b mapgen_plotting --track origin/mapgen_plotting\n",
+            "  Switched to a new branch 'mapgen_plotting'\n",
+            "  Branch 'mapgen_plotting' set up to track remote branch 'mapgen_plotting' from 'origin'.\n",
+            "  Resolved https://github.com/djps/k-wave-python.git to commit ef367ed71c8151b7fe096e4d5530ca895a4212c9\n",
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "Collecting beartype==0.20.2 (from k-Wave-python==0.4.1)\n",
+            "  Downloading beartype-0.20.2-py3-none-any.whl.metadata (33 kB)\n",
+            "Collecting deepdiff==8.4.2 (from k-Wave-python==0.4.1)\n",
+            "  Downloading deepdiff-8.4.2-py3-none-any.whl.metadata (7.1 kB)\n",
+            "Collecting deprecated>=1.2.14 (from k-Wave-python==0.4.1)\n",
+            "  Downloading Deprecated-1.2.18-py2.py3-none-any.whl.metadata (5.7 kB)\n",
+            "Requirement already satisfied: h5py==3.13.0 in /usr/local/lib/python3.11/dist-packages (from k-Wave-python==0.4.1) (3.13.0)\n",
+            "Collecting jaxtyping==0.3.2 (from k-Wave-python==0.4.1)\n",
+            "  Downloading jaxtyping-0.3.2-py3-none-any.whl.metadata (7.0 kB)\n",
+            "Collecting matplotlib==3.10.3 (from k-Wave-python==0.4.1)\n",
+            "  Downloading matplotlib-3.10.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n",
+            "Requirement already satisfied: numpy<2.3.0,>=1.22.2 in /usr/local/lib/python3.11/dist-packages (from k-Wave-python==0.4.1) (2.0.2)\n",
+            "Requirement already satisfied: opencv-python==4.11.0.86 in /usr/local/lib/python3.11/dist-packages (from k-Wave-python==0.4.1) (4.11.0.86)\n",
+            "Collecting scipy==1.15.2 (from k-Wave-python==0.4.1)\n",
+            "  Downloading scipy-1.15.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.0/62.0 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting orderly-set<6,>=5.3.0 (from deepdiff==8.4.2->k-Wave-python==0.4.1)\n",
+            "  Downloading orderly_set-5.4.1-py3-none-any.whl.metadata (6.2 kB)\n",
+            "Collecting wadler-lindig>=0.1.3 (from jaxtyping==0.3.2->k-Wave-python==0.4.1)\n",
+            "  Downloading wadler_lindig-0.1.6-py3-none-any.whl.metadata (17 kB)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib==3.10.3->k-Wave-python==0.4.1) (1.3.2)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib==3.10.3->k-Wave-python==0.4.1) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib==3.10.3->k-Wave-python==0.4.1) (4.58.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib==3.10.3->k-Wave-python==0.4.1) (1.4.8)\n",
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+            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib==3.10.3->k-Wave-python==0.4.1) (11.2.1)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib==3.10.3->k-Wave-python==0.4.1) (3.2.3)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib==3.10.3->k-Wave-python==0.4.1) (2.9.0.post0)\n",
+            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.11/dist-packages (from deprecated>=1.2.14->k-Wave-python==0.4.1) (1.17.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib==3.10.3->k-Wave-python==0.4.1) (1.17.0)\n",
+            "Downloading beartype-0.20.2-py3-none-any.whl (1.2 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading deepdiff-8.4.2-py3-none-any.whl (87 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m87.6/87.6 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading jaxtyping-0.3.2-py3-none-any.whl (55 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.4/55.4 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading matplotlib-3.10.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.6 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.6/8.6 MB\u001b[0m \u001b[31m66.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading scipy-1.15.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (37.6 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m37.6/37.6 MB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading Deprecated-1.2.18-py2.py3-none-any.whl (10.0 kB)\n",
+            "Downloading orderly_set-5.4.1-py3-none-any.whl (12 kB)\n",
+            "Downloading wadler_lindig-0.1.6-py3-none-any.whl (20 kB)\n",
+            "Building wheels for collected packages: k-Wave-python\n",
+            "  Building wheel for k-Wave-python (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for k-Wave-python: filename=k_wave_python-0.4.1-py3-none-any.whl size=194021 sha256=c6327f08c633d31ccdafa50d73c9e01da21ea4a49f617f24f57746e0f23e23f0\n",
+            "  Stored in directory: /tmp/pip-ephem-wheel-cache-_xjzfwln/wheels/24/0b/ba/62ad594910e20122f5c9e36f6ab093dc6d5ba08dd5952e16cf\n",
+            "Successfully built k-Wave-python\n",
+            "Installing collected packages: wadler-lindig, scipy, orderly-set, deprecated, beartype, matplotlib, jaxtyping, deepdiff, k-Wave-python\n",
+            "  Attempting uninstall: scipy\n",
+            "    Found existing installation: scipy 1.15.3\n",
+            "    Uninstalling scipy-1.15.3:\n",
+            "      Successfully uninstalled scipy-1.15.3\n",
+            "  Attempting uninstall: matplotlib\n",
+            "    Found existing installation: matplotlib 3.10.0\n",
+            "    Uninstalling matplotlib-3.10.0:\n",
+            "      Successfully uninstalled matplotlib-3.10.0\n",
+            "Successfully installed beartype-0.20.2 deepdiff-8.4.2 deprecated-1.2.18 jaxtyping-0.3.2 k-Wave-python-0.4.1 matplotlib-3.10.3 orderly-set-5.4.1 scipy-1.15.2 wadler-lindig-0.1.6\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install git+https://github.com/djps/k-wave-python.git@mapgen_plotting"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "3b16593a",
+      "metadata": {
+        "vscode": {
+          "languageId": "plaintext"
+        },
+        "id": "3b16593a"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "from kwave.utils.mapgen import make_cart_rect, make_cart_arc, make_cart_disc, make_cart_bowl, make_cart_spherical_segment, make_cart_circle, make_cart_sphere"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Todo:\n",
+        "\n",
+        "- [ ] better typechecking on all make_carts: flexible: List, Vector, np.ndarray.\n",
+        "- [ ] consistent test names\n",
+        "- [x] `make_cart_rect`\n",
+        "- [x] `make_cart_disc`\n",
+        "- [x] `make_cart_arc`\n",
+        "- [x] `make_cart_bowl`\n",
+        "- [x] `make_cart_sphere`\n",
+        "- [x] `make_cart_circle`\n",
+        "- [x] `make_cart_spherical_segment`"
+      ],
+      "metadata": {
+        "id": "8RXwfELgrzan"
+      },
+      "id": "8RXwfELgrzan"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4092e0ad",
+      "metadata": {
+        "id": "4092e0ad"
+      },
+      "source": [
+        "# Cart Rect\n",
+        "\n",
+        "This takes the data from one of the test examples."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "099ac32c",
+      "metadata": {
+        "vscode": {
+          "languageId": "plaintext"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 831
+        },
+        "id": "099ac32c",
+        "outputId": "2b1b7843-5709-4d98-8a8d-437cf4ec937a"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "rect_pos = [20, 20, 30]\n",
+        "Lx = 10\n",
+        "Ly = 10\n",
+        "theta = [70, 50, 20]\n",
+        "num_points = 500\n",
+        "data_3d = make_cart_rect(rect_pos, Lx, Ly, theta=theta, num_points=num_points, plot_rect=True)\n",
+        "\n",
+        "rect_pos = [-20, -30]\n",
+        "Lx = 15\n",
+        "Ly = 30\n",
+        "theta = -5.3\n",
+        "num_points = 72\n",
+        "data_2d = make_cart_rect(rect_pos, Lx, Ly, theta=theta, num_points=num_points, plot_rect=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Make_Cart_Arc"
+      ],
+      "metadata": {
+        "id": "wJHt1r8xOvtF"
+      },
+      "id": "wJHt1r8xOvtF"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "arc_pos = np.array([0.0, 0.0])\n",
+        "radius = 5.0\n",
+        "diameter = 7.0\n",
+        "focus_pos = np.array([2.0, 5.0])\n",
+        "num_points = 50\n",
+        "arc = make_cart_arc(arc_pos=arc_pos, radius=radius, diameter=diameter, focus_pos=focus_pos, num_points=num_points, plot_arc = False)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 878
+        },
+        "id": "Ctt5nlzIOzNl",
+        "outputId": "9b1af50f-f896-4559-c62e-a12c00ea7eb0"
+      },
+      "id": "Ctt5nlzIOzNl",
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "ValueError",
+          "evalue": "'c' argument has 2 elements, which is inconsistent with 'x' and 'y' with size 50.",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36m_parse_scatter_color_args\u001b[0;34m(c, edgecolors, kwargs, xsize, get_next_color_func)\u001b[0m\n\u001b[1;32m   4724\u001b[0m                 f\"with 'x' and 'y' with size {xsize}.\")\n\u001b[0;32m-> 4725\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4726\u001b[0m         \u001b[0mc_is_mapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m  \u001b[0;31m# Unless proven otherwise below.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mto_rgba_array\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    513\u001b[0m             \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 514\u001b[0;31m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    515\u001b[0m         \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    513\u001b[0m             \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 514\u001b[0;31m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    515\u001b[0m         \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    315\u001b[0m         \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mrgba\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Suppress exception chaining of cache lookup failure.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    317\u001b[0m         \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_to_rgba_no_colorcycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36m_to_rgba_no_colorcycle\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    399\u001b[0m     \u001b[0;31m# tuple color.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 400\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    401\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Invalid RGBA argument: {orig_c!r}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mValueError\u001b[0m: Invalid RGBA argument: np.float64(0.0)",
+            "\nThe above exception was the direct cause of the following exception:\n",
+            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-19-e1746c72588b>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mfocus_pos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mnum_points\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0marc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_cart_arc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marc_pos\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marc_pos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mradius\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiameter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdiameter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfocus_pos\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfocus_pos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_points\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_points\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot_arc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m<@beartype(kwave.utils.mapgen.make_cart_arc) at 0x7c681d819a80>\u001b[0m in \u001b[0;36mmake_cart_arc\u001b[0;34m(__beartype_object_928484656, __beartype_get_violation, __beartype_conf, __beartype_object_136786613216640, __beartype_object_928679952, __beartype_object_928681712, __beartype_check_meta, __beartype_func, *args, **kwargs)\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/kwave/utils/mapgen.py\u001b[0m in \u001b[0;36mmake_cart_arc\u001b[0;34m(arc_pos, radius, diameter, focus_pos, num_points, plot_arc)\u001b[0m\n\u001b[1;32m   3015\u001b[0m         \u001b[0;31m# Select suitable axis scaling factor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3016\u001b[0m         \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscale_SI\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3017\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3018\u001b[0m         \u001b[0;31m# Create the figure\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3019\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    451\u001b[0m                 \u001b[0;34m\"parameter will become keyword-only in %(removal)s.\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    452\u001b[0m                 name=name, obj_type=f\"parameter of {func.__name__}()\")\n\u001b[0;32m--> 453\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m     \u001b[0;31m# Don't modify *func*'s signature, as boilerplate.py needs it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, colorizer, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m   3937\u001b[0m     \u001b[0mvmin\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3938\u001b[0m     \u001b[0mvmax\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3939\u001b[0;31m     \u001b[0malpha\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3940\u001b[0m     \u001b[0mlinewidths\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3941\u001b[0m     \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    451\u001b[0m                 \u001b[0;34m\"parameter will become keyword-only in %(removal)s.\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    452\u001b[0m                 name=name, obj_type=f\"parameter of {func.__name__}()\")\n\u001b[0;32m--> 453\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m     \u001b[0;31m# Don't modify *func*'s signature, as boilerplate.py needs it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1519\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1521\u001b[0;31m             return func(\n\u001b[0m\u001b[1;32m   1522\u001b[0m                 \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1523\u001b[0m                 \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, colorizer, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m   4916\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0medgecolors\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4917\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'edgecolors'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0medgecolors\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4918\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mlinewidths\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4919\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'linewidths'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlinewidths\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4920\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36m_parse_scatter_color_args\u001b[0;34m(c, edgecolors, kwargs, xsize, get_next_color_func)\u001b[0m\n\u001b[1;32m   4729\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# First, does 'c' look suitable for value-mapping?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4730\u001b[0m                 \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4731\u001b[0;31m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4732\u001b[0m                 \u001b[0;32mpass\u001b[0m  \u001b[0;31m# Failed to convert to float array; must be color specs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4733\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mValueError\u001b[0m: 'c' argument has 2 elements, which is inconsistent with 'x' and 'y' with size 50."
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Make Cart Disc"
+      ],
+      "metadata": {
+        "id": "tSI9x4dPT_jB"
+      },
+      "id": "tSI9x4dPT_jB"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "disc_pos = np.array([20, 20])\n",
+        "radius = 1.0\n",
+        "focus_pos = [70, 50]\n",
+        "num_points = 5\n",
+        "plot_disc = True\n",
+        "use_spiral = False\n",
+        "disc2 = make_cart_disc(disc_pos=disc_pos, radius=radius, focus_pos=np.asarray(focus_pos), num_points=num_points, plot_disc=False, use_spiral=use_spiral)\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "from kwave.utils.data import scale_SI\n",
+        "\n",
+        "print(np.shape(disc2))\n",
+        "\n",
+        "_, scale, prefix, unit = scale_SI(np.max(disc2))\n",
+        "\n",
+        "# create the figure\n",
+        "fig = plt.figure()\n",
+        "\n",
+        "cmap = plt.get_cmap('viridis', np.shape(disc2)[1])\n",
+        "ax = fig.add_subplot(111, )\n",
+        "ax.scatter(disc2[0, :] * scale, disc2[1, :] * scale, marker='.',\n",
+        "          c=np.arange(np.shape(disc2)[1]), cmap=cmap, alpha=0.9, edgecolor=None)\n",
+        "ax.set_xlabel(f\"[{prefix}m]\")\n",
+        "ax.set_ylabel(f\"[{prefix}m]\")\n",
+        "ax.axis(\"equal\")\n",
+        "ax.grid(True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 466
+        },
+        "id": "KEyZNRrgUCJn",
+        "outputId": "325ca5b4-c24c-474f-adef-7f6b4c3b9799"
+      },
+      "id": "KEyZNRrgUCJn",
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(2, 8)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "disc_pos = np.array([20, 20, 20])\n",
+        "radius = 3.0\n",
+        "focus_pos = np.array([30, 50, 10])\n",
+        "num_points = 12\n",
+        "plot_disc = True\n",
+        "use_spiral = False\n",
+        "disc = make_cart_disc(disc_pos=disc_pos, radius=radius, focus_pos=focus_pos, num_points=num_points, plot_disc=False, use_spiral=use_spiral)"
+      ],
+      "metadata": {
+        "id": "mP6CXKBTSM9S"
+      },
+      "id": "mP6CXKBTSM9S",
+      "execution_count": 28,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "from kwave.utils.data import scale_SI\n",
+        "\n",
+        "print(np.shape(disc))\n",
+        "\n",
+        "_, scale, prefix, unit = scale_SI(np.max(disc))\n",
+        "\n",
+        "# create the figure\n",
+        "fig = plt.figure()\n",
+        "\n",
+        "cmap = plt.get_cmap('viridis', np.shape(disc)[1])\n",
+        "ax = fig.add_subplot(111, projection=\"3d\")\n",
+        "ax.scatter(disc[0, :] * scale, disc[1, :] * scale, disc[2, :] * scale, marker='.',\n",
+        "          c=np.arange(np.shape(disc)[1]), cmap=cmap, alpha=0.9, edgecolor=None)\n",
+        "ax.set_xlabel(f\"[{prefix}m]\")\n",
+        "ax.set_ylabel(f\"[{prefix}m]\")\n",
+        "ax.set_zlabel(f\"[{prefix}m]\")\n",
+        "ax.axis(\"equal\")\n",
+        "ax.grid(True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 594
+        },
+        "id": "8MfAWMM-t6mu",
+        "outputId": "06443151-dfdc-4ac6-9fa8-6d404c4d247c"
+      },
+      "id": "8MfAWMM-t6mu",
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(3, 8)\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "AttributeError",
+          "evalue": "'Axes3D' object has no attribute 'box'",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-30-8e0a9b2f932e>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"equal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;31mAttributeError\u001b[0m: 'Axes3D' object has no attribute 'box'"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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vf6NZRKQqio+PF9NZBQaReAQE/m7O3Nwcenp6gjrsPcHXVhu3tVZTU0OXBgPBWkwglUgkiIuLg1QqRWVlpUtraH5+HkNDQ1AoFC5EJDSz01giHj5I2y0uLg5lZWXIzMxc4fHHVSymp6cjLi5OJKIoQyQegYBhGDgcDjrT6e/vx/z8PLZv306380MJXyqeYFtr7q4Z6xXPauC2hjZu3OjR7JQ4bpODMJqIdC5TOMAwDN398ZRFRBSLiYmJLoan7ohITGcNL0TiiTL4kdQGgwHt7e2Ii4tDc3Nz2BYbV6t4QtFacwdyTU8f4lgkHm/37MnsVKfT0QiCaJudxnLFQ0CIhw9vWUTj4+MwGAxISkpyISKFQuESEy6VSsVQvBBDJJ4oggyqR0ZGUFZWhsuXL2NgYAAbN25EeXl5WHcrSHXBP3RC2VrzdE3A/WG9Hj7I3sxOBwYGYLVaXexlSARBOLGWiYcP/utvt9tdpNvusohIcq6Yzho6iMQTJZAqx2q1Ynh4GMvLy1hcXERjY2NEYqDJh5R76HBba6uZjAaC9dBq8xfezE6Jzxk3Hjw1NTXkB9x6Ih4+FAoFcnJykJOTA8D3LCI+EZHWHNdnLtZf03BCJJ4Ig7+bYzAYAFz54Ozbty9i/X7yoSAfWG5rraqqKixP2b58ENcb8fDhyexUp9NhYmICAKi1TKjMTtcz8fCxWhaRxWJxISJ+FpGYzuobROKJIPi2NyMjIxgdHQUAbN++PaJDZvIhdTqdGBwcDEtrjY/VBA2x/MEMx737YnZK4sHJ//ydCa42c4sVhMv2h59FZLFYKBF5yyIS01m9QySeCIFhGNhsNvp/Ozo6YLVasWfPHly8eDHiT/rkoLl06RIkEklYWmuerunt67FY8UTqnt2ZnRLF1szMDPr7+12kw5mZmas+zKwF4mEYJmJ+cwkJCatmESmVSlqVikTkHiLxhBn8SGqNRoOuri7k5ubSSGoyvIwk5ufnAQBpaWmorq6OyBufSyw6nQ4OhwOZmZkrfM9E+AapVEpJBnCVDk9MTPhkdkpe71g++EgVHY2fwZcsIi4RkRndeo8JF4knjODb3gwMDFCPM24yZ7CGnf6AYRj09fVhenoaAMKunuOCtNp6e3tx+fJlyOVyWK1WOjwn5CwiMPClw57MTskOkVKpXBMVj1DI09csIn4EBDcmXK/XQyKRIDc3d00TkUg8YQB/N4eoxUhLi7+IGSniMZlMUKlUAIDm5ma89dZbET3oHQ4HtFot4uLisHv3bsTFxcFqtUKn09EDUq/XY3l5mR6OQrebETL4ZqcWi4W+xtyIagBYWlpCenp6TB5w0ax4vMGfLCJSkWq1Wjq346az/vSnP8U111yD5ubmKP9UoYFIPCEG3/ZmZmYGvb292LBhA7Zs2eL2wyGTycJOPLOzs+jq6kJRUREqKyvpUlykiEej0WB2dhZJSUnYu3cvfZ0SExNRVFSEoqIi9PT00KdGYgBJ7GYIEQk1oC0WyDEhIQEFBQUubSG1Wo3FxUV0dXW5HIKxZHbqdDpjQjXmKYtIr9djYWEBY2NjcDqdSExMREJCgksExPPPP4/S0lK/iOfYsWM4efIk+vr6kJiYiObmZjz22GOorKykf+eZZ57BiRMn0NraSoUrq0WtPPLII/j2t7/t8t8qKyvR19fn872JxBNCcNNBHQ4Hent7odVqsWPHDron4A7hrHi4rTW+ai0SlRbLshgeHsbo6CiV/8pkMjgcjhV/VyqVIi4uDqWlpSgtLXWxm+EGtHHtZiK95b9WQAg+Pz8fw8PDuOqqq6jRKVmm5M6QiGJOiId7JIPsQgluFlFJSQlYlkVbWxvkcjl0Oh1GRkbwwgsvYHR0FBMTE9BoNH5J38+fP48jR45g165dcDgceOihh3DjjTfSzxFwpQty4MABHDhwAA8++KDP915dXY2//OUv9M/+fg7FT20IwI+kXl5eRkdHB33KWM0UMlwEQFprnlRr4VaR2Ww2tLe3w2w2Y+/evXTYSq7NB/9+uHYz5eXlLjMLslORlpZGiYjsVEQasbwHw53xcA9B8j7W6XSYm5vDwMCAi+tzZmamYKrPUO3wRBvEESE7OxuFhYVgGAbx8fF4+eWX8e677+IrX/kKvvvd7+K6667D7bffjo9+9KNev9+ZM2dc/nz8+HHk5uaipaUF11xzDQDg/vvvBwCcO3fOr3uVy+VBrV6IxBMk+Ls5ExMTGBoawqZNm7Bp0yafDqRwEI+71hof/kYj+IOFhQWoVCoolUo0NTVBoVAEncfDn1mQLX+dToepqSkwDIP09HRKVuJ8aHUQ0uS/Tlyz07KyshVmp729vS4eZxkZGVGLB18rxANcaRuSn0UqlWLv3r3Ys2cPfvvb3+L06dNgWRZvvvkmpqam/P7ei4uLABASZ5TBwUEUFhYiISEBTU1NOHbsGEpKSnz+9yLxBAF+JHVXVxcMBgN27txJJa6+IJTE43Q60d/fj+np6VWdrcMx42FZFuPj4xgcHERFRQVKS0tdkke9wV+S4G/5k345d7mSVENCekIXEnx1puabnfI9zrq6uuhGf2ZmJpRKZcTaoGuNeNy9bkajERkZGdi2bRuuuuoqv78vwzC4//77sW/fPtTU1AR1j3v27MHx48dRWVmJmZkZfPvb38bVV1+Nrq4upKam+vQ9ROIJAPzdHL1ej87OTmRkZGDfvn1+P/mFiniMRqOLem61hdBQVzwOhwNdXV3Q6/VuyZdvEsonvWBaf+5aRYuLi9DpdC5P6OTwDPV8KFYrq0DbhO48zog6kWz0R8rsdK0RD/9ncTqdsFgsQcWSHDlyBF1dXXj77beDvUUcPHiQ/v+1tbXYs2cPSktL8fvf/x6f+tSnfPoeIvH4Cf5uzsjICMbHx1FVVYXi4uKAPsShIB5fWmvurhuqimd5eRltbW10ruWuuohk9DV/uZI8oet0OgwNDcFsNtODMTMzM2rzoWgjVPOpuLi4FRv97sxOSQUaSrPTtUY8/AgS4ufoazXBx9GjR/Hyyy/jwoULKC4uDvoe+UhPT8eWLVswNDTk878RiccPcCOprVYrOjo64HA4sHfv3oDfFEBwxONPa42PUFU8ly9fRk9PDzZu3IjNmzd7PFCi6U7Nf0K3WCz0Cb2zs5POhwgR+WO+GctLr+ESRvDboFzF3Pj4OAC4vN7BzOPWOvEYjUYA8Dv2nmVZ3HfffTh16hTOnTuHsrKykN0nFwaDAcPDw/iXf/kXn/+NSDw+gLubA1yxm+nu7kZBQQGqqqqCDkkLlHj8ba25u24wh6bT6URvby/m5uZWlYwDka14VkNCQgIKCwtdDkZCRCMjI3SJjzyhCy2uOlSIhCKPu7+yYcMGF7NTjUYTtNnpWiEe0sLnnycmkwkJCQl+t4aPHDmCEydO4PTp00hNTcXs7CwAQKlU0td3dnYWs7OztFrp7Oyk7Woyz/vgBz+I2267DUePHgUA/Nu//RtuueUWlJaWYnp6Gt/61rcgk8nw8Y9/3Od7E4lnFXBl0qS6mJ2dDamTcyDEMzMzg+7ubr9aa3wEU/Hwpdq+HhRCzOPhHozc+RA3rprEJZP5ULQUXKFGNKTg/pidEuL3Zna6VoiHfBbdtdoCqQiffvppAMB1113n8t+fffZZ3H333QCAn/70py7LoERmzf07w8PD0Gg09O9MTU3h4x//OLRaLXJycnDVVVfh3XffXfXBkwuReDyAv5tjNBrR0dEBuVyOffv2hTSS2h/icTqd6Ovrw+zsrN+tNXfXDeSgn5+fR0dHh9/ZPb602oQA7nxo06ZNcDgcLp5nZrPZxfNMbLUFB3dmp9x46u7ubhez04yMDJen/7VCPMQo2B3xBCIs8OV9+cgjj+CRRx7x+nfGxsZc/vy73/3O73vhQyQeN+Dv5ly+fBn9/f0oLS3F5s2bQ/4m95V4jEYjVCoVpFIpmpqago4x8LfCYBgGg4ODmJiYQE1NDQoKCkJ+vXAd4gzLwu50Ij4AJZtcLl8xHyL7Q9PT07Db7YiPj6cLlrFiNQMIc+vfXTw4NxWUED8hIYfDIbifIRAQ4uH/LEajMabeU75AJB4euLs5TqcT3d3dWFhYQENDA3X9DTVIlK43kNZacXGxR8+3QK7ra6VlsVjQ3t4Ou92OpqYmvwedgGtF44tzQajQOjWD75y5gGWrDTdUluEr1zdDHsTrx/c86+7upq4KXKsZIt0W8nxICBXPauCngrozO1UoFBgbG6OKuVgkIoZhaFopFyaTKexZWZGGSDz/D/xI6sXFRXR0dCA1NTXskdQkOtcduK212tpa+uELBXyd8Wi1WrS3tyMrK4tmCAV6vUi32liWxbE//xUaowlyqRSv9gxhT0kRrt8SGoWPRCKBXC5HYmIiysvLwTAMlpaWoNPp6LwiISGBtuWiueHvDrFAPHzwib+/vx9GoxHLy8uYnJx0USjGUgXqTlgAXGm1BfKgJ2SIxIP3W2vk8B8bG8Pw8DC2bNnisnkfLnhyp+a21vwZ4PuK1YiAZVmMjo5ieHgYlZWV2LBhQ1CvRbRabUsWK2QSKeLlcphsdixarCG/BoFUKqV5K8D78wqdTkc3/Ml8iOwPBauKDAaxSDxckDTPtLQ0VFRUuDhYxJrZqSfiMRqNQS2PChHrnnjIbs4bb7yBuro6jIyMwGKxYM+ePVAqlRG5B3ctr5mZGXR1dXmNUwjFdT0d9Ha7HR0dHTAYDNi9e3dIXgsu8USq1SaRSPCPdVvx25ZOGG025Kem4Opy3z2lggV/XmG1Wul8qLu7Gw6Hw2WfJdJP57FOPMCVFhWpwt05WCwtLUGv168wOxVa1IYn4jGZTGLFs1bAz81hWRYqlQq5ubloaGiIqN0+t+XFba3V1dWFtLXm7bpcLC4uQqVSISUlBU1NTSFrM0ZL1fbppnrUFeVBb7JgV2khMpNCWzkCvt97fHw83fAnIYFkf2hsbMzl6TwzMzPkVS4fa4V4PD2YcStQvtkpidoQitmpO7scIHBVm5CxLomH5OUQ25vBwUE4HA6UlZW5hCRFCqTVFu7WGh/8iodlWUxOTqK/vx/l5eUoKysL+aHEvR7/e4dLXCCRSLC7tCjk35cgGH85klC5YcMGt1EECQkJLk/noT4U1wrx+PozuIva4JudchVz6enpEWuFiq22NQp3kdQdHR0AQA0kowGpVAqz2Yx33nknrK01PrgVj8PhQE9PDzQaTdgUfEJyLhAi+FEE3H0W/qFIHKCDPRT9ObSFCqIGCwR8KyXSCnVndhpuTz9vxFNUFL4Hp2hg3RAP3/ZmZmYGPT09KC4uRmVlJd57771VJc3hgNPpxOXLl2EymVBfXx/W1hofpOIxGAxQqVRQKBQ+BdcFilhZIBUKPM2HiIzYbrdDqVTSQzEQ4821UvGEigy4rVDA1ey0q6vLZSYXarNTbzMeseKJQfB3c/r6+qBWq11mKL7s0oQaBoMB7e3tYBgGycnJESUd4MpBv7y8jIsXL6KkpAQVFRVhrbR8+YCu54pnNfDnQ2azGTqdDjqdDhMTEwDg0pbzRb21VognXD+DN7PTsbExSCQSl/lQMGan3ioeUVwQQ+Dv5pCDPj4+Hvv27XN5svckaQ4Xpqen0d3djZKSEmRkZGBgYCBi1waufFh1Oh2Wl5exY8eOoKx3fAWpeEhuj06nc1m0jJZXWyxCIpEgKSkJSUlJKC4upvMhrnrLF7+ztUI8kWpNc81OyZmi0+mgVqsxNDTkYnbq7/KwuMezBsC3vZmcnMTg4CDKyspQXl6+4sMmk8kiUvFwHZ1JxaXVaiNKemazGSqVChaLBXl5eREhHeDKB9fpdOLixYuIj49HVVUVlpaWaFBbfHw8JBIJtFptRIe6wUIIhzd3PrRx40Y4nU66P0T8zlJSUigJkddXCPceLIKZ8QQDqVRKzU7Ja06k29zlYW5F5E0h6nQ63Uq7TSZTULErQsSaJB6GYWCz2ah6raurC8vLy14jqSNBPGSWIpfLXVRroYy+Xg1qtRodHR3Iy8tDdnY2zGZzRK4LAHq9HiaTCZs2baKKouzsbGzatAl2u5264JKhbnp6Oq2GYmX7XCiQyWTIysqiIhGbzUb3h/r6+mCz2WgqKFkniNXXVygCCZlMtqrZKYkHJ+TPXdvw1moTLXMEDH4k9cLCAjo6OpCeno7m5mavTxvhPvy5rTX+LCUSxMOyLIaGhjA2NoZt27ahqKgIIyMjESE8rrloXFwctmzZsuK6CoUCSqWSiiy48wuy30JIKDMzUzBLf7GCuLg4Wt2S+ZBer8fU1BRMJhPeeuutFftDQjjMfYFQ3am9mZ0ODg7CYrG4SLfdEQ+ZK4kVj0DhKZLaV6uXcFU8voSlhZt4bDYb2tvbYTabXdJSIzFTsVqtaG9vh81mw7Zt27zG45Lfkbv5BfE/I/k4ZOmPtI5ipS0nBHBfX5vNBpPJhA0bNtBZxeDgIOLi4uhrm5mZGVavwmARrVabv/BmdtrT0wObzQaz2QyHw+GimPN3j+fYsWM4efIkzZFqbm7GY4895rKj+Mwzz+DEiRNobW2ls0Fi8+QNP/7xj/H444/TBfennnoKu3fv9vu1WBPEw61yLBYLOjs7YbPZ/IqklslksNvtIb0vT601PsKpqNPr9VCpVLTq45b2wSaQroaFhQV67YaGBiwvLwe0x8PdPidtOdI2GhgYgNVqhVKppNVQKCWuax0kFoE/q1hcXIROp6Pb/aRFRILwhHTQC6XV5i/4Zqd/+9vfkJaWhuXlZUxMTOCHP/whTCYTlpaWMD097XNld/78eRw5cgS7du2Cw+HAQw89hBtvvBE9PT2UwEwmEw4cOIADBw7gwQcf9Ol+/+///g8PPPAAfvrTn2LPnj344Q9/iP3796O/v99vRW5ME4+nSOq8vDy/XZRDffh7a625u3ao++wsy2J8fByDg4OoqKhwa3YaTALpatcmDgj8a4cigVShULg8OXJtZ8bHx6MSSxCLBx/gPo+Hu90PvD8f4i5V8veHotnqEmqrzR+Q909ubi6ysrLAsiwSEhLwyiuv4J133sEnPvEJJCcn4/rrr8c999yDG2+80eP3OnPmjMufjx8/jtzcXLS0tNCE0fvvvx8AcO7cOZ/v8YknnsC9996LT37ykwCupJe+8sor+OUvf4mvf/3rfvy0MUw8/EjqwcFBTE9Po7q62u+AMiB0cmpfWmt8kA9NqIjH4XCgs7MTi4uLXgUV4ah4nE4nenp6oFar0djY6OIGsdrPFujP7k5WzI0lSExMpAcpf6AbCsSyBNyX9xx3PgSAzt+I3xnLsi5tz2B2WQLBWiAewNWrTSKRYPfu3di8eTOefPJJjI2NYWBgAGfPnoXBYPDr+y4uLgJAUM4sNpsNLS0tLtWRVCrFDTfcgIsXL/r9/WKOePiR1MT2hvibBar+CMWMh9ta4+8JrXZtwLNJoD9YXl5GW1sb7e16682HuuIxmUwuXnP8n3+164Vi5uTOdoa05chAV2zLvY9AHnYSExNRVFSEoqIi6nzB3WVRKBQRc38mnYK1Qjz8FqbRaAQApKenY9++fdi3b59f35NhGNx///3Yt28fampqAr43jUYDp9O5YvUiLy8PfX19fn+/mCIe/m7O9PQ0+vr6QrJ1H2yr7fLly+jp6QkoHpv83WBJgNzDxo0bsXnz5lUPlFBWPESmXVBQgKqqKrc/fzScC/ix1e62/blD9HAbs4YCdseV96lCHvycJdhDmxtDUFpa6tb9OTk52WV/KJQVJ/nMrFXiMZlMSExMDHimduTIEXR1deHtt98OxS2GDDFDPHzbm56eHuh0OtTX11O5YjAItNVG7mV+ft7n1hof5EAOlHgCae+R6wZLdizLYmRkBCMjI1Sm7e160fZq4z6tc9tys7OzGBgYQGJiosvTeiTjMXzBHy/04FevtAAA/uWmRhy6dltQ3y/U+zvu3J/JfIhUnGlpaS4VZzCksVaIh3Ry+ARDIhEC+R0dPXoUL7/8Mi5cuIDi4uKg7i87OxsymQxzc3Mu/31ubo762vkDYX2q3IBve7O0tISOjg4kJSVh3759ISvjA2m1cc01/Wmt8SGRSAKWVJP2lkQi8TtKIdiKhxsWt2fPHqSlpXn9+0Jzp/bUltPr9RgeHobZbA7pIRks5nQG/M9Lf4fDwQAS4Nk//h17ajYgPyvwHY9wK8L4QhCyP6TT6TA1NQWGYVz2h/ydD60V4iFnj7tWm78GoSzL4r777sOpU6dw7tw5lJUFH/MeFxeHxsZGnD17FocPHwZw5bU/e/Ysjh496vf3EzTx8Ftr4+PjGB4exubNm7Fx48aQP6n5QzzBtNbcIRDimZubQ2dnJ4qKilBZWen3PQRT8ZBZUnJyMpqbm33KifGl4onmoN5dW44ckpOTkwDgopYjJB+prX+j2QaGYWmLzckwMJhsQBAJFpF2LOCbbpKYaq1Wi+HhYcjlcpfW52oPluT9G+tzutWIx5+f78iRIzhx4gROnz6N1NRUzM7OAgCUSiV9z87OzmJ2dpbu1XV2dtLUViJC+OAHP4jbbruNEssDDzyAu+66Czt37sTu3bvxwx/+EEajkarc/IFgiYfs5pAWW2dnJ0wmE3bt2uXTopO/8PXgD0VrLZjrA65OADU1NQGp+Mg1AznoiVTck++dJwih1eYP+Ieku5C2zMxMWK3WiFialOano7aiAKr+aQBAXUUBygrdKxZ9RTStctzFVJP9IeLfl5yc7LI/xG99EkWb0N47/sLpdNLOBxeBVDxPP/00AOC6665z+e/PPvss7r77bgBXpNDf/va36deIzJr7d4iFFcFHP/pRqNVqfPOb38Ts7Cx27NiBM2fOBOT1KDji4e/m6HQ6dHZ2IjMz0+cn60DgS8UTqtaaO/hKPBaLBe3t7bDb7WhqagrKtdbfiodhGPT19WFmZiZg0hVSq80fSCQSlyVL4sOl0+lgMBjoQD2cbTmZTIpvfvoG/L3nSvW1a9sGyGTBXUNIHm3c2G8ANB1Up9NhaGiItj65oWxrRUrtyX0hEGdqXz5DjzzyCB555BGvf2dsbGzFfzt69GhArTU+BEU8XJk0wzAYGhrC5OQktm7diqKiorB+QFYjnlC31vjwhXi0Wi3a29uRnZ3t94Ksp2v6etBbLBaoVCo4nU40NTUF9IS/2hJptFtt/oDrw2Wz2Wh1RGYXZLeFO7sIBeIUMuyr2xiS7wUIi3j44KeDWiwWuj/U2dkJhmGQkpJCW3aBDuGFgPUUew0IhHj4uzkWiwUdHR1gGCbop3pfQQ5+/gfR4XCgt7c35K01T9d3B65yrKqqCsXFxSH5gPl60Ot0OrS3tyMrKwvV1dUBSzt9IZ5YhUKhQGFh4Yq23Pz8PAYHBxEfH0+roYyMjLBV7v5CyMTDR0JCgstrbDQacfnyZSwvL+PSpUs0C4e8xpFwrAgV1lP6KCAA4mFZFiaTCWNjY9i0aRPm5ubQ09ODwsJCVFZWRswTilyHW/KGs7XGhyfisdls6OzshMFgwO7du6FUKsN+TQKu7Y6vZqve4M6yh3vwxVLFwwX/nvltOafTSdVyo6Oj6O7uRmpqKiWitLS0qLWLYol4uCChbNnZ2dDr9di9ezdtdxIjWeJYQSpPoUnjuVhP6aNAlImHVDlWq5X2cOfn57F9+/aIhZMRcN0DZDIZpqam0Nvbi40bN6K8vDzsB4M7ElhcXIRKpUJKSkpY5lveDnqHw4Hu7m7odDqvtjv+Xg94/7Bzd+jFIvGsBplM5mKPT1yJyfySSIq5arlIkUGsEg8BmfFw50ObNm1yK40nZJ+RkUGziIQCT64lazF9FIgS8fB3c6xWK4Ar7O7vLkqoQH7pNpsNfX190Gg0IVtO9fX6hHi4Jpvl5eUoKysLy+HgSVxgNBrR1tYGhUKB5ubmkO1K8YnH09fXOviuxFzLmUi35dYK8fDBl8ZzyX56eppGDxDCj/Z8yOl0uq3ITCZTxM6gSCIqxEMikBmGwdTUFPr7+wEAtbW1UbMskUgkkEgkaGlpQUJCgluvsXCCWPaQSkOr1a4w2QzHNfmu2PPz8+jo6EBxcTG2bNkS0qfCtSQuCBXcWc4QJdfo6Ci6urpclFyhflJfq8TDB5/sjUYjJaLR0VEaNEhe50jPhzxVPKK4IMRwOBzo6OigDsqXLl2K6qFDlEhZWVnYtm1bxMtwqVQKs9mMd999l1Ya4X7z84lgcHAQ4+PjQe0G+XM9d4hV4gnV4c2PrLZardRbrqurCwzDuESCB+sEvV6IhwsyH0pJScGGDRtcgga5jubc/aFwi0G8zXjWWvooEEXi6e/vB8uy2LdvH+Li4iCTyejuTiThcDjQ09MDjUYDhUKBoqKiqPR+rVYrNBoNSktLgzY89RXkGlarFV1dXTCbzWFVEa5lVVu4EB8f77Ytp9FoMDw8DIVC4dKW8zcpNNadnUOxx8MNGgTgsqM1MjICo9EY1qoTEOXUEcO2bVfMDclhI5fLw5bC6QnLy8tQqVSIj49Hc3Mz/va3v4U1gtodyFLm0tIS8vPzXeJpww3y2r/33ntQKpUrEkrDBZZlMTc3h5GREaSlpSErK4uKF2Kx4onUPXtqy5EAPL5azpcDMtYrnnAQJ3dHC3i/6tTr9eju7obD4aBVZ0ZGBlJSUoJ+DcWKJ0JQKBQuFU4o8nB8Bcuy1JKDGyEQyXsArniBqVQqsCyLvLy8iM+3ZmZmAABFRUU+xSgEC/L9R0ZGcPnyZWzcuBFms5nGV6emplLH6FB8mNc63LXlyNyiu7sbTqdz1bZcrBNPKDKsVgO/6uQm3pL5EHd/KJDPsdPpdFutGo3GiNgxRRqCEbZHqtXGba3xVWuRJB6SX5Ofn4+qqioMDAxErNpiGAY9PT3U4jzY/RxfQX6/arUae/fupR80iUQCk8mE2dlZLC0tobW11cVePzMz0+8W0npEfHw88vPzkZ+fTwfoOp2OGnCSthw5JOPi4mKeeCJtmSORSJCcnIzk5GQ6H+In3iYkJLgQkS/zIXcVD/kdihVPGBGJVhu/tcYf3keCeFiWxdDQEMbGxlzya4INovMV3CqrqakJFy5ciEiriLhZA0B9fT1SUlJgs9no15OSkpCfn4/x8XFcffXVWFxchFarxcTEBHp6emgLKSsrK6oLl7EC7gC9pKSEBrSRADzymlqtVhgMBmRkZMTkaxrtGZW7aA3S/iSqRP7+kLuWmievNnHGE2Lwn7LCWfF4aq3xEWgmjq+wWq3o6OiA2WzG3r17XZ5kpFIp7HZ72K4NXPF6U6lUyMvLw9atWyGTySIiYZ6dnUVnZyc2btyIkZERj24U5HfCN4vkKru4C5dZWVmCSQ0VetXArSCBK/tqOp0OfX19mJiYwOjoqEtbLtp7Lb5CaCah7uZDZJG1t7cXdrudRq9nZGTQ6HVvljniAmkYEa6Kh9taa2hooP1wdwhnxaPX66FSqZCRkYH6+voVQ/xwkh7LshgdHcXw8DC2bt3qkkYY7usSiXZtbS3y8vIwNjbm9x4Pv8fOjycg1ijkwxwpmyWCWBRExMXFIT8/H8PDw9i2bRsUCgWdW4yMjNBcHKG3Oj0tXgoF/PanyWSic7jx8XEAV6LXzWbzirPHZrPBbreLrbZwIhyHPre15ktaaTjaXVy/sy1btqCkpCSi1ZbD4UBnZycWFxfder2Fq+Ih6aRGo9FFou3teqs5G5C/w48nIB9kIlLgPrmLIgXvIK0qbluOm4szOTmJnp4epKSkuKjlIk3unhDtVps/4M6HiouL6XxIr9dDo9FgYGAA4+PjyMzMxMTEBG3D+1PxHDt2DCdPnqRedc3NzXjsscdc1LIWiwVf/vKX8bvf/Q5WqxX79+/HT37yE682ZXfffTd+9atfufy3/fv348yZM36+ClewJlttLMtiamoKfX19XltrfISa/Ox2O7q6urC4uLhqgF04iMdgMKCtrY06Mbh7ag0mhdTbdVtbW5GcnIympiaX4aqv8de+kgXXGoVlWZjNZjpQHxsbE0UKq8Dda81tdZaXl8Nms1FyJ+0iobTlhNZq8wfc+dDly5exdetWsCwLnU6H733ve/j73/8OAPjOd76DG2+8EVddddWqCrfz58/jyJEj2LVrFxwOBx566CHceOON6OnpobOiL33pS3jllVfwhz/8AUqlEkePHsXtt9+Ov/71r16/94EDB/Dss8/SPwdjpSWYikcul8NsNgf9fbiWM6u11viQyWQhO4SXlpagUqnoU8dqB16oiYfMVchCqqeDIdAUUk+Ym5tDR0eHR8L3peIJFBKJBElJSUhKSqJPlKJIwTt8Ifm4uDjk5eUhLy/PRU5M7GZkMpmLyWmovP18QSwTDxdOpxMKhQKpqanIysrCX/7yF5w/fx533HEH9Ho97r33XszOzuJ///d/8ZGPfMTj9+FXIMePH0dubi5aWlpwzTXXYHFxEf/zP/+DEydO4PrrrwdwJXV069atePfdd7F3716P35u0DUMBwRBPKKoNf1tr7u6BGJYGA+Js7U80dKiIh2EYDAwMYGpqis5VvCFUFQ9Xrbd9+3aPb1BfK55QwBeRArcaCkakEKvtPH/l1O7kxKQtR973ycnJ9DVNT08Pa1tuLREP/3VKSkpCeno6fvGLXwAAhoaGvHZN3GFxcREAqKikpaUFdrsdN9xwA/07VVVVKCkpwcWLF70Sz7lz55Cbm4uMjAxcf/31+O53v+vXgz0XgiKeQFtt3NaaP4c9H8HOeJxOJ3p7ezE3N+e3s3UoiMdqtaK9vR02mw1NTU0+yTBDUfGQec7QggZFleXIXCUsz5cZTzggdJFCNBDsHg+/LWe32ym59/X1uai4wjFzWwvEQwIo+e83rpRaIpGgoqLC7+97//33Y9++faipqQFwpRMSFxe3gsDy8vIwOzvr8XsdOHAAt99+O8rKyjA8PIyHHnoIBw8exMWLFwP6nAhmxhOoqi2Y1hofwbTajEYjVCoVZDJZQNEOwRLPwsIC2trakJGRgYaGBp+VPsFWPGSec86mx0tLU2D+PojakXw8fdUhJMpXLs55O3QiWTX4I1LIysryOseIRVUbQagXSBUKxYq2HHldx8bGqAt0qNpya4F4yLnnjniCMYE9cuQIurq68Pbbbwd9jx/72Mfo/799+3bU1taivLwc586dwwc/+EG/v19UKx5u2yWQVhtZSkxMTAyotcZHoO2+ubk5dHZ2oqioCJWVlQF9EAIlHm52T0VFBUpLS/16owZT8ZB5TuGGYvyptwdOhkWcTAaVZgZvz47jH4o3r/g3XKLz1HaLxkG+XkUK4XQucKfiIi7QZK8u2LbcWieeQHd4jh49ipdffhkXLlxwWZ/Iz8+HzWbDwsKCS9UzNzfn1/xm06ZNyM7OxtDQUOwRDxdyudznVluoWmt8+Es83HlKTU1NUIO3QIjH6XSiu7sbGo0m4OyeQCoe/jwnMycHsv4LYJ0smP93kMm8yKG93Qv5/tGEvyKFaN9voCD3HamDm+sCvWnTJtjtdloN9ff30yqTLAf70pZbK8QjlUpX/KwGg8Fv1wKWZXHffffh1KlTOHfuHMrKyly+3tjYCIVCgbNnz+KOO+4AcCUpYGJiAk1NTT5fZ2pqClqtNuD4FMEQj6+HPjeSOdjWGh/+HP4WiwXt7e2w2+0+z1NWu7Y/pGcymdDW1kZbe4Fm9/hb8XD3c7juCw9svwrfa78AJ8vg2oIyXF2w0e2/D6eqLVxYTaTgcDio64RQnBR8Afk9ROt1VygUyM3NRW5urkuVSZYrueabnsLZ1gLxeLLLCcS14MiRIzhx4gROnz6N1NRUOrdRKpVITEyEUqnEpz71KTzwwAPIzMxEWloa7rvvPjQ1NbkIC6qqqnDs2DHcdtttMBgM+Pa3v4077riDLh1/9atfxebNm7F///6AfmZBtdpWq3j4EuVQyzZ9JT+tVov29nZkZ2dj586dIRlC+0N6xGC0oKAAVVVVQX3w/FkgJfOcpKSkFfs5d2yqxnWFZTA6bNiQrPS6ABopVVu4wBcpXLp0CfHx8TEnUog28XDhrsokbbnp6WkazsZty8nl8jVBPA6HI2Q+bU8//TQA4LrrrnP5788++yzuvvtuAMCTTz4JqVSKO+64w2WBlIv+/n6qiJPJZOjo6MCvfvUrLCwsoLCwEDfeeCMeffTRgM9gwVQ8RFzgrufMnWOEsrXGx2rEw7IsRkZGMDIygqqqKhQXF4c0eXI14mFZFsPDwxgdHUV1dTUKCwuDvq6vhEfmWCUlJR73grISkpAF7wtusVjxeINEIqH+XIWFhUGJFCINIREPH57acnq9nr6uSqUSDocDJpOJep7FIrwZhPpb8fjy0JaQkIAf//jH+PGPf+zT90lMTMRrr73m132sBsEQD3nh+b8Eh8OBrq4u6PX6kLfW+PB2CNtsNnR2dsJgMGDPnj1IS0uL2LWB91tcob7+ajMeX/dz/LletOTUkUAsiRSETDx8cNtyAKhabmFhAf39/RgcHFy1LSdUeDIINRgMa9KnDRAQ8RD5L7fsDHdrjQ9PFc/i4iLa2tqQlpaG5ubmsOSvk1mLu4qPqPeSk5NDfn1vMx5P85xg4Y1YIuGWHSn4KlIgLtuRdlKIJeLhIykpCYmJiejv78euXbvo/hDJxHHXlhMqPIXZmUymkDkFCA1Rn/EQEFUHabeR1tqmTZuwadOmiHw4+MTDvY/Nmzdj48aNYbsP8sbjV3zT09Po7u4OW4vR00FPfN4SExNXzHPCcT0u1grx8BFJJwVfEMvEA7x//3K5HElJSS6ZOKTdOTg4CIvFgrS0NNruFFpbzpPD9lrN4gEEVPEAV95AVqsVAwMD0Ov1AUuEAwV5+mcYBgzDUPVcJO6DTzwMw6Cvrw8zMzPYsWMHclZxAwgU7lptvsxzgrneahVPrCHQXZhoOylw96liEeT++dUCt90JwEUtNzk5CQAubbloqxA9VTzB7PFEC9dddx3Onz8PAGhra8OOHTvc/j1BEY9EIoFKpUJKSkpEWmt8kA/28vIyOjo6aFJpJO6DSzwWiwUqlQpOpxNNTU1hzVznttpCPc9xB3fCEXf/bb0hlE4KvmItxF4DqxNnYmIiioqKUFRUBJZlqVpudnYWAwMDSEhIcCH4SLflPM14YpF4AODee+/Fd77zHa+WYYJotZGWls1mQ1FREWpqaqLygSC//L/97W8oLS3F5s2bI9ZzJz8v8bjKyspCdXV12KW4pOKx2+1UPBHKeY67662XGU8w8FWkQOZDgbRC1wrx+PMZlUgkbqOqdTodhoeHYTabaVsuMzMTqampYT8DvKWPxmKrjcTYe0PUKx6uai05ORk5OTlR+TAwDIPe3l4AV5anNmzYEPF7kEgk6OzspNePxOsglUphtVrx7rvvhnye4w6EWDQaDa0qs7KyaExBLB+E4YI7kQI5LMfHx9Hd3R2QSGEtEI+7jX9/wI+qNpvNtNKMVFuORCJwwbJszFY8XJw7dw4f+MAHcObMGXz9619HX18fmpqaoks8i4uLaGlpoaq19vb2sEVPe4PZbIZKpaJphmT4GykQNwaWZVFTU0OTByMBs9kMrVaLjRs3YsuWLRE5iNRqNTQaDa0oyXCdZVk4nU7Mz89DoVBEvNUaK+AabQKBixTWAvGE+v4TExORmJiIwsJCt3M3blsuPT09JA9pTqfTrfx7LcmpH3nkEfz3f/83kpKS8JGPfCS6xMOyLIqKiqhqLRzx16uBuADk5+ejqqoK58+fj+g9GI1GtLW1QaFQQKFQROwJh8xztFotsrKyXKJxwwWn0wmDwQCn04mdO3ciOTkZDMO4DNdbW1uhVqsxPj5ODSSzsrKgVCpjfkM9XPAkUiAzDE8ihbVAPOF8T7ibu/HbcsSzLxg5vLdWW6xXPATf/e53sW/fPgDApz71qegST0ZGhssL649RaLBgWRaDg4MYHx93cQGIJPnNz8+jo6MDxcXF2LJlCy5cuBDyGGp3cDgc6OjowPLyMgoLCyNi6WKxWNDW1gan04mNGzciPT2d+psB73/I5XI5tm7disTERDrT6O7uhtPppOaRWVlZgloQFNIB7o9IIS4uTjD3HQgibZfDb8tZLBZaaV6+fBkMw6xoy/ny+rojHtJqi8UZjzvU1tbS/z8vLy/6Mx4uInXok8A0q9W6YpAebBicL+CSHlc9Fur4a3cg+zkJCQloamrC+Ph4SFJXvWFxcRGtra3IyspCfHz8qqohlmVX5LoYDAZotVr6FJ+UlESrofT0dLEa8gBvIgW9Xg+GYdDT0xOUSCFaiLZPW0JCAgoLC1e05ebn5zE4OIj4+HiXStPTa+vOMsdiscDpdK6ZVhv3Z5dIJMJQtREEk0LqK/R6PVQqlcfAtGDC4HyBzWZDe3s7zGYzmpqaXCq+cJMeqbA2bNhA5zmhir72BLIASxZwySzNE9w9IUokEqSmpiI1NRUbN26kvl1arRa9vb1wOBz0STMrKyvqexlCBV+kQF6/uLi4oEQK0UK0iYcLfqXpdDppW250dJS+tu7acu4qHqPRCABrptXGh6AqHrJAGg6wLIuxsTEMDQ1hy5YtKCkpcXvIhbPqItY7SqUSzc3NK0gvXBUP11y0pqbGJUMjFNHXnq45MDCAyclJlwVY8poT0uPDFzk1307faDRCq9XSJ00y0yDVkFDdoaMNYnC6efOVwL5oOyn4CyERDx8ymYy2hQH3ry15WLLb7St+DqPRCIlEIrjXPFQQFPGEq+Kx2+3o6urC4uIidu3atSJvnItwVR1TU1Po7e1FeXk5ysrK3B664SAe7jzH3X5OOPZm7HY7rer27t3r8tQWassciUSClJQUpKSkoLS0lM40tFot+vr6YLfbXaqhcC7jxhr4s6lARQrRgpCJhw/+a2swGKDT6aBWq2G1Wmm7k1TsRErtzwzu2LFjOHnyJPr6+qhS+LHHHnMRDlksFnz5y1/G7373O5dIhLy8PI/fl2VZfOtb38LPf/5zLCwsYN++fXj66adRUVER8OshqFYbiUYIJYjRaFJSEpqbm1d1Aw51xUN66HNzc6u6a4e6zcef57j72UPdajMajWhtbUViYiL27t27oq8d7gVS/kzDZDJBq9VCo9FgaGiIymGzsrJCdngKSVzgD7zddzScFPxFLBEPF9zWcWlpKc6dO4eNGzfCbDZjZGQEH/3oR5GWlgan04m33nrL42eXj/Pnz+PIkSPYtWsXHA4HHnroIdx4443o6emhIoUvfelLeOWVV/CHP/wBSqUSR48exe23346//vWvHr/v9773PfzoRz/Cr371K5SVleHhhx/G/v370dPTs6rI57rrrlvxeb777ruFV/GE8tAnVYY/BpuhPPzJfhAANDc3r1o2h7LicTfP8XTNUFU8arUa7e3tXq8ZSa82iUSC5ORkJCcno6SkBE6nk1ZDAwMDsNlsUCqV9EkzKSkpJgkkUPhDmO5EClqtls4w5HJ50E4K/iJWiYcL4g1JKp2KigpcuHABP/rRj/Dcc8/hIx/5CIxGIz7wgQ/gsccew7Zt2zx+rzNnzrj8+fjx48jNzUVLSwuuueYaLC4u4n/+539w4sQJXH/99QCuBMRt3boV7777rksCKff+fvjDH+Lf//3fcejQIQDAr3/9a+Tl5eHFF1/Exz72sRX/5ic/+Ql+8Ytf4OLFi9i+fbvbe4068fibQuoLnE4nenp6MD8/j/r6eq+eQXyEivy0Wi1UKhXy8vKwdetWn56sQ0E83uY57hCKioc7P1stoC6a7tQymYzKYbmHp1arxcjICOLi4lyqISFb6YcCgVZqXJHChg0bQuak4C/WAvGQzx73fCgtLcV1112Hd999FyqVCp2dnfjzn//s92I7SRAli8YtLS2w2+244YYb6N+pqqpCSUkJLl686JZ4RkdHMTs76/JvlEol9uzZg4sXL64gnt/+9rcwm80AgJKSEo/3JqhPVihabUajESqVCjKZDPv27fN73yPYw59lWYyOjmJ4eBhbt25FcXFxxK7Nnef4GhYXbMXjdDrR3d0NrVaL3bt3Q6lUev37XOIJVFwQCvAPT6JC0mq1dDmQWw0JKTk0VAjV5v9qTgosy4bFdmYtEA8579yp2lJSUiCVSlFXV4e6ujq/vi/DMLj//vuxb98+1NTUAABmZ2cRFxe3Ysadl5eH2dlZt9+H/Hf+DMjTv/HVdUVQxBNstTE7O4uuri66kBnImzKYe3A4HOjs7MTi4qJPhzAfwRAPma14m+e4QzAVD1kKBYCmpiafSJ5LLO4IJlqHO1+FxK2GRkdHoVAoXFpJ/GooFkkpXLOpSIkU1hLx8H8Og8EQ1PLokSNH0NXVhbfffjuo+wsXok483IMoUOcChmEwMDCAqakp1NTUBGXnL5PJYLPZ/P533EG+LyIGdwiUeLjznIqKCr8+jIFWPAsLC2hrawvIRTsWguASExNRXFyM4uJiOJ1OmhxKdjLS0tIoUQnhfgNBJEQRvooUCKH7U1muFeKRyWQrfuZgnKmPHj2Kl19+GRcuXHDpuOTn58Nms2FhYcGl6pmbm/N4ZpL/Pjc359K2n5ub85i14wuiTjxckGrDnw8EP7smWIuJQCqe2dlZdHZ2orS0NKjgNH+Jx995jjsE0tq6fPkyenp6UFFRgdLSUr9+3liMRSARBKSVxN3+Hx8fB8MwmJqaAsuyMbX9Hw01njeRwsjIiF8ihbVAPO5cC4DAsnhYlsV9992HU6dO4dy5cygrK3P5emNjIxQKBc6ePYs77rgDANDf34+JiQk0NTW5/Z5lZWXIz8/H2bNnKdEsLS3hvffew+c//3m/7o8LwREPy7I+fyC0Wi3a29uRk5ODbdu2hUQa68/hz620amtrvWrhfb22r6QXyDzH0zV9/XlZlkV/fz+mpqYCTkVdCwmk3GAxhmHw3nvvQS6XY3x8HD09PXSwLsSYZS6iLQMPVqTAMEzMkLwneDIINRgMfhPPkSNHcOLECZw+fRqpqal0BqNUKpGYmAilUolPfepTeOCBB+jred9996GpqclFWFBVVYVjx47htttug0Qiwf3334/vfve7qKiooHLqwsJCHD58OOCfO+rEw33jk765w+Hw2qpiWRYjIyMYGRnxe4C/GnyteIjfm81mC0mlBVwhAa5xpicEOs9xB19nPNyl0GB+Xl+uJ7SKxxukUinkcjny8/ORm5sLq9VKZ0OTk5OQSCQuT/DB/K5CDRIDIhT4K1JYCxWPt/RRf1VsTz/9NIAruzNcPPvss7j77rsBAE8++SSkUinuuOMOlwVSLvr7+6kiDgC++tWvwmg04jOf+QwWFhZw1VVX4cyZM0EZ9UadeLjgehd5gs1mQ0dHB0wmU1BP+p7gC/GQ+YYnv7dA4Uv1wXe0DvaD58uMx2AwoLW1FcnJyW6XQv1BLLba/EF8fDw1jmQYBktLS9BqtZiYmEBPTw9NtxRC8F20K57VsJpIQSqVIiUlBWlpaTFrjeSJeMxms98P1L58bhISEvDjH/8YP/7xj33+PhKJBN/5znfwne98x6/78QZBEQ/J5PEkMFhYWIBKpUJaWlrYkjK9Hf4koru/vz+g+Uaw1yZV3mq7Mv5gtYPel6XQUF5PyAehN3iyQEpPT0d6ejrKy8vpE7xWq8XU1BQAUBLKysqKeDUkdOLhwp1Ioa2tjbZ/ucvA/ooUogmn0+n24TFYVZvQISjiAdzv8rAsi4mJCQwMDFCX43C9qTxVPGRfRaPRoLGxkbYDQglPxENk2ktLSyGv8jxd05+lUH+w2h4PuXYswdf75T/Bk2qIOGwQ92JSDYW7jRRLxMOHXC6HQqFATk4OCgsLgxIpRBNOp9Ntx2QtxF57Q9SJx100Avfgdzgc6Orqgl6vD9uB7+36wBVpY1tbG2QyGZqbm8MWQuZOXEASSuPj44Oe57iDuwrE36XQYK/nz9fXCiQSCZRKJZRKJTZt2gSbzUarIe48g1RD4YgBj2XiAd5XtfkiUiAtTqHFPXiqeNZS+qg7RJ14+ODu8pDdmPj4eDQ3N4flw8cH//An0dgFBQWoqqoK6xuWX32QNleo5jm+XNNisaC1tRUSicTnpVB/sRZbbcEiLi4O+fn5yM/Pp/MMrVaL6elp9Pf3hyUGfK0QDx/RclIIBN7EBWKrLYIgFQcJEAt2NyaQ6zMM47IjE8pWk6/XDsc8xx24FQYRTWRnZ6O6ujosRMe9nl6vh9lsRnZ2tku7YT1UPN7AnWeUlZXBbreHJQZ8rRIPH746KUQjv8lb7PVaSR91h6gTD/+NL5VKMTk5CYPBgLq6OuTm5kb0fgjxtba2wmAwhEU55wmk2lKpVFhcXIzItYm8OZil0ECuNzw8jJGREcTHx6O3t5cOhp1OZ9jjv8OBcB7g4YoBj3XiCUQO7slJQavVRkWk4HQ63XZyxBlPBGEymbC4uAi5XI6mpqaohHYRZ1WWZdHc3BzRoaTVaoXRaERcXFzAtjuBore3128n70BAhupLS0vYtWsXEhISXNRei4uLMBqNMBqN68Yl2h+sFgNOgu+4oWKeEOvE42k+4g9C6aQQCMRWW5QxPz+Pzs5OxMfHIz8/PyqkQ9p7AFBbWxtR0lGr1ejr64NMJsPOnTsjMvy02+3o7OwEAOzZsyfspb3FYsHExAScTieuuuoqyGQy2O12FyeA9vZ2xMfHQyqVYmhoCBaLBenp6cjOzhZsgmg0W4PBxIALbYHUX4R6gTQaIgV3ljnk9yhWPGHGwMAAxsfHUV1djYWFhbBET3sDwzDo6+vDzMwM6urq0NraGrF2D3eeU1ZWhsnJyYgcBmQplBzk4R6wLi0tobW1FfHx8VAoFIiPj3e7ryWVSpGcnIwNGzYAAE0QJXEF8fHx9Gk+VpcGwwV/Y8A9+YTFCsJNnJEQKTgcjhW/A5PJBJZlxRlPOGEwGKBWq9HU1ISUlBQYDAafbGNCBb7JaFJSkl+eacGAG6OwZ88eAMD4+HjYr0vUciUlJSgrK8PZs2fDSrTERLW8vBxyuRzz8/Me/y5fTs3PzHF3kPrSVgoExgUTACA5XXhVli9YLQZcKpUiKSkJSqUyZDHgkUSkLXPciRS4s7ZARAruyN9kuvK+EyueMCI1NRXNzc201yyTyWCxWCJybZ1OB5VKRVVc5A0QyghqTyD7Odx5jsFgCOt1uSF1RC1HrheOdhG3miMmqsTFmWVZOJ1OKiYg+xje7oWbILplyxZ6kJK2UlJSEiWhYCXHp586gxd+8AoA4PAXD+D2B24O+HsJAe5iwNvb2+F0OmM2BjyaXm185WGgIgV3Mx6DwQCZTBaR9ZFoIerEI5FIXH4hoYq/9gaWZTE+Po7BwUFUVlZiw4YNK+4hnBUPqTiKiopQWVlJPzzhJDyn00kXcblLoasd9qG4Hl+dR0gHeN8rjvyZYRgXZZung4V/kDocjhWSY64djT8f4vlxDZ5//GU4HU5AApz64avYe2gnCss9u48L/ZDmgxxsycnJKC0tXTFUF3oMOFk7EMqMyheRAiEhrkjBHfEQYYFQfrZwQFjvJoQm/tobuE4Iu3btWhEDC4SPeFbbzyGHcKjVRmQpVCqVoqmpyeUQJsQfSsKzWq0uS6j8Q99isUCv1yM9PZ0eaAzDYHx8HDqdDgUFBfThQyKR0GrI2wdRLpe7DNmJ5HhmZoYuYBISWm0obDVbwbIsZIorBwLjZGA1Wj3+/VjdOyIHdyzGgJPXXIiHsz8iBbvd7jZ9dC232QCBEA+3rx/Oioff3vL0FByOGY8vsdjkDRjKoa9er0dbWxtyc3Oxbds2tx/UUBIPERFkZma6tC8JoSqVSqSmpqK9vR0SiQTZ2dnIzMyEXq+nPnhpaWlgGIb+j/u7kEql9H+e4E5yTAQKZCjszZyzaEsBGm6sxaUz7QBY1H+wBiXVoYveEAo8PeAEGwMeCaxWEQsJ3kQKTqcTbW1tLlEPJpPJb/XmhQsX8Pjjj6OlpQUzMzM4deqUS17O3Nwcvva1r+H111/HwsICrrnmGjz11FOoqKjw+D2PHz+OT37yky7/LT4+PiSjEEEQDxfhqnjm5ubQ2dnpk/0McRAIFUwmE1pbW1fdzwk18RDzyS1btqCkpMTjU2qg8dd8cEUEZWVlLm08QiIJCQnYvn073eeZm5tDX18fnE4n0tLSoNfrIZPJkJKSArlcTlsq5N+zLOt3NaRQKFzsaLjmnH19fSuC26RSKf71Z59G99v9YFkW1VdVQiYT/gHnL3ytrLkx4OTp3VMMeEpKSkSqoVgiHj6ISCE/Px9zc3Oorq7G8vIyxsfHceuttyItLQ0sy+LMmTO49tprfSIho9GIuro63HPPPbj99ttdvsayLA4fPgyFQoHTp08jLS0NTzzxBG644Qb09PR43RdKS0tDf38//XOofreCI55Qt7lYlsXg4CDGx8exfft2j9ni4boH4vVWWFjoMs9xBy7xBAOGYdDf34/p6Wk0NDTQJ1dPCNaY052IgPs1MrPhEoREIkFcXBy0Wi3S09OxZcsWLCwsQKPR0KfqrKwsur9DeuKkAiJkFEg1xDfn5Ae3kUN0674KwboahwJ84hkdnMeLJ96Dk2Fx60d2Ykv1Sqsm/tM7PwacxISH2xWavJ+i3fILBuS9q1QqkZ2djbKyMgwMDOCb3/wmXnvtNXzhC1/AzMwMrr76ahw/fhxFRUUev9fBgwdx8OBBt18bHBzEu+++i66uLlRXVwO4EhqXn5+P5557Dp/+9Kc9fl+JROLTmekvBEE84Wq12Ww2tLe3w2KxULm2LwgF8bhTkK0G8iEK5trkZ7ZarT67PwQjavBFRMBXrQFXWoDt7e0oKChARUUFDfUiT9Wk9TYwMACLxYLMzEyqaCM/E6mACBEFUg3FxcVRiSw3uG1sbIwGtxEC9DbbiMUDkEs8JqMVj3/zNBZ0RgDAQM80Hn/mX6DM8L49z48BX1xcpCQUzhjwtZI+CsClu5GZmYkdO3ZgdnYWZ86cweDgIF577bWAYuYJrNYr80mup59UKkV8fDzefvttr8RjMBhQWloKhmHQ0NCA//zP/6TkFQwEQTxchKrVtri4iLa2NiiVSjQ1NfnVhw5WXUYEDAsLC37FCpBDMtBr85NCff2ZA614iIgAwAoRgTfSmZ6epi1AsijKhVQqpYdVZWUljEYjNBoN1Go13ZcgJJSRkUFbl8FWQ/zgNovFQquh8fFxqkwSqtLLX3CJRz23hOVFM+Lj5ZBIJDAZrJi5vLAq8XAhlUqRkZFBI5vDGQO+VojH3YMRCYGTSCTYsmULtmzZEtR1qqqqUFJSggcffBA/+9nPkJycjCeffBJTU1OYmZnx+O8qKyvxy1/+ErW1tVhcXMT3v/99NDc3o7u72+90VD4E98kh85Vg3lhktsGfNfhzD4GSn6/zHE8IdN5CIrFLSkr8dvMORFxARAQZGRmoqalZISLgyl25s56hoSFMTU1hx44dq7YACYhsmmzj63Q6aDQadHd3w+FwUDLIzs6mT3XcaogrVABA72m1aighIcHlaZ6v9EpPT6fGprGobOMST16BElk5KVDPLQEskJ6ZjOJS334/nuApBnxycnJFNeRvDPhaIR5PPm2hVLUpFAqcPHkSn/rUp5CZmQmZTIYbbrgBBw8e9Pq+bWpqQlNTE/1zc3Mztm7dip/97Gd49NFHg7onQRAP9w1HniIDMQBkGAY9PT2Ym5vzabbhCYESjz/znFBdmztfqampQUFBgd/X9Jfs5ubm0NHRgU2bNmHTpk1uRQSA644WacktLy9j9+7dARsgupNNazQaTE9Po6+vDykpKbQaUiqV9IPN3Q8i90nu0ZeWHHe2UVFRAZPJRGcbVqsVvb29yMnJodVQLLgAcIknITEOX//P2/HqyVYwDIv9h3cgJTV0WUyhjgFfC8TjSUQUDp+2xsZG6npvs9mQk5ODPXv2YOfOnT5/D4VCgfr6egwNDQV9P4IgHi7IL8LpdPo1mDSbzVCpVACuMHMw9in+yqm585xt27Z5HQKuBn+qD6fTic7OTiwsLAQVoeDrNX0REbjbryC2RDKZDLt37w6Z6zZXNl1WVkaFAhqNhr4XSCWUnZ3tIlDgVkP+tuSA9618iouL8c4776CwsBBWq5W6AITTyidU4IsL8ovS8cn7ro/ItT3FgF++fNmnGPC1QDzeKp5AH5pXA2n7Dw4O4tKlS35VLuS8uemmm4K+D8ERD3ny9EdgoNVqoVKpkJeXh61btwb9tElck31BoPMcT/B1xmM2m9HW1uZ2KTSQa65W8ZA4bJ1O51FEQA4y7mG2tLQElUp1RSW2dWtYDwuuUIBlWSwuLkKj0dClPaIeys7ORkpKisdqyF+BArFPyczMdPFEU6vV1CGaa2wqlANTKLEIgcSAr3XiKS0t9et7GQwGl0pkdHQUKpUKmZmZKCkpwR/+8Afk5OSgpKQEnZ2d+Nd//VccPnwYN954I/03d955J4qKinDs2DEAwHe+8x3s3bsXmzdvxsLCAh5//HGMj497FSP4CkEQD//N76vAgFtpbN26NeiBF4GvfnHBznM8XXs14vFlKdQfrCYu8EVE4I505ubm0N3djU2bNoU1XM4dJBIJbe1s3rwZFosFGo2GyrXlcjklIa7slzsP8rUa4h7g7qx8iEKvp6cnZOmhoQCRJAsNnmLAuS4UhHximYA8EY/JZPK7FX3p0iV84AMfoH9+4IEHAAB33XUXjh8/jpmZGTzwwAOYm5tDQUEB7rzzTjz88MMu32NiYsLltdTr9bj33nsxOzuLjIwMNDY24p133sG2bdv8ujd3EATx8OHLnMMXJ4BA4UurTaPRoL29Pah5jqdreyMeX5dCQ3VNX0QE7kw+x8bGMDo6ipqamoinyLpDQkKCyxIkIYPBwUGYzWZkZGTQGQ350Pu6vOoNfA8vfnqoP1Y+oYZQKh5v8BQDPjU1BZPJhLfeekswRO4vPBFPIJY51113ndeHxy9+8Yv44he/6PV7nDt3zuXPTz75JJ588km/7sNXCJZ4vLXaiGw4MTExLEmd3qqOUM5z3METCfi7FOoPPM14fBURcA9gIvDQ6XTYuXNnxGLD/QFfrm0ymWg15E6u7W15leu0vdrTtzsrH35LyZ8Be7CIBeLhg8SAkxyb8vLykMSARwOhrHhiDYIgHn9abcSWpbS01G/ZsK/wVHGFep7jDu6IJ5ClUH+vyX1a4pIr3+3Bm4iA3CfDMNizZ0/M2LonJSWhpKTExeWaK9fmLq9y5drkYYBYAZGHJV/l2uQQzcvLc2kpcQfsgcqNfUEsEg8BUYRxiZzrUO5vDHg04Em5u9bTRwGBEA8f7g5+hmEwMDCAqampFYqqSFzfZDKhra0Ncrk86GG+N/CJZ3l5Ga2trUhNTfVrKdQfcGc8vogI3C2FGgwGqFQqpKWluZiDxho8ybVnZmbQ19eH5ORk5OTkICMjAxMTEzCZTJRkPcm1CQH5IlAgLSWulc/U1BRdvuTPpIKBkGIF/IW7+RT/d+dPDHg04K7iIfe9ltNHAYESj1wud2m1Wa1WqFQq2O12NDU1hb0M5R/+4ZrnrHbt+fl5tLe3Y+PGjdi8eXPYnk7JNa1WK9ra2sCyLPbu3evSL/dGOhqNBp2dndiwYQPKy8tj9imaD75c2263Q6PRYH5+HmNjYwCAnJwcLCwsuLTGQrG86snKh1jRhMKYM9YrntWI3F0MuE6no0FtZAGYVEORfi28tdrEiicK4FYcCwsLaGtro6qKSNiUkOuTIfnQ0FBY5jnuQIQNw8PDGBkZ8dnYNBhIJBKYzWZcvHhxhYgAcFV68UlncnISAwMD2LZtW0DLq7EEhUIBpVKJkZERZGdno6SkZEXGSnZ2NnJycrzKtQNZXuUvX3KtfLgxBv7EFMQ68fhTsawWAx4fH+9ihxSJasjpdLqd45EguLUMQRAP/81P9mgmJibQ39+PioqKiMpxibihvb09rPMcT5iZmYHD4QhqKdQfWK1WzM/Po7y83C8RwcDAAGZnZ9HY2Og2UG+tgfj/FRQUYMuWLbT9ReTa5CAbGxvzKtcOdnmVb0VD9pVGRkZc9pVWi7COdeIJ9N75knen00ljqwcGBmC1Wl2qoXDFgLureBiGEWc80YJUKoVarYbdbkdjYyO1YI8U7HY7bDYbHeZHakhuNpuh0Wggk8kicl0iIlhYWEBeXh7Ky8tdvuZJRGC329HZ2QmLxYI9e/YIbmgbDqjVanR2dmLz5s0oKSlZ8XW+r5s7uTYhouTkZBflZDDLq1xjzoqKCpfQNhJh7elJPtaJJ1RViUwmo78bALQaCncMuLufwWi84g4uzngiDJPJRB1Tm5ubI67LJ/McANi1a1fEhq9kKTQ+Ph6ZmZlhJx2GYdDV1QWtVoucnBwXpZyn/Rzgyu9HpVIhISEBu3fvjnmHZl8wOTmJwcFBVFdX+yRq8SbXJm0d0pJLT08PanmVD25oGzfCmlj5cJ/kY514wpX1E6kYcCIJ58JkMgGA2GqLBKg1+/8z2UxJSYFCoYgo6XDnOZs3b0Z/f3/EPpSTk5Po6+ujh1SwQXCrgSsiaGpqwsjICK1sfMnQyc/PXzXFdS2AuGlfvnwZDQ0NAbcTuXJtp9MJnU4HtVqN7u5u2O12Fz85d+7agWYNcWc/3GqIECDLshgfH0deXp7gd174iJRjgacYcJ1OF3QMuKeKR6FQxMwqQqAQBPGQD/jo6Ciqq6vhdDoxNzcXsetzw8x2796NpKQk9Pf3w+l0hvWJnmEY9PX1YWZmhi6FDg4OhiwIzx2Wl5fR0tKC9PR0bN++HTKZjC6QehMRrJahs9bAMAy6u7uxuLiIXbt2hewJVCaTrXAy4Mu1ue7aocoakkgkLk/ydrsdb731FhiGoTsv3OVVoTsARMsqJ5Qx4O5mPNwsnrUMQRCP0+nE0tIS9u7di9TUVExPT4f18OXC3X4O+VCHs/Kw2WxQqVSw2WwuS6H+OmP7AyLPLisrc5E9SyQSOJ1Oel2+/c3w8DAmJyf9ytCJZdjtdrS3t8PhcGDXrl1he/p0J9cmFUl7eztYlkVWVha18gmlXJs8UG3evBlxcXF052Vubo46AJADVKlUCq4aEoJHGz8GnAhMiNJxtRhwd8SzHhRtgECIRy6Xo7GxkbZ7QpVCuhrIB7ygoABVVVX0jUz+b7jugSyFpqWloaGhwaWqCjb91B1WcyKQSCR0UTEnJ4eKBUKVoRNLsFgsaGtrQ0JCAnbs2BHRGZZCoXAxx1xaWoJarV4h187OzkZqampQcm3yWSMkxd15sdvtVBzR3d0Np9PpUg0JoQ0kBOLhgy8wWS0G3BvxiBVPFBBMAqgv4M5z3LlaSySSsN0D8T/ztBTqizu1P+CKCPiycHJIFRUVQSaT0afdlJQUZGRkQKPRQKFQhDRDR8hYXl5GW1sbsrOzXR5EogFuVMDmzZthtVqpQGFsbMxFiZWVleWXXJv8PXIdPhQKhYsDALHymZ6eRn9/P1JSUsJq5eMLhEg8XHiKAdfpdDQGnFj8xMXF0c9XIBXPhQsX8Pjjj6OlpQUzMzM4deoUDh8+TL8+NzeHr33ta3j99dexsLCAa665Bk899RQqKiq8ft8//OEPePjhhzE2NoaKigo89thjIcniAQREPFzbFr5zQShBnuJ1Op3X/ZxQt7y4IWrelkJDWfHwRQSenAgSEhJQVlZGrVqmpqYwOjpKB9qDg4O03RNtm5FwQavV0geCjRs3Cu6JMz4+fkUMt0ajwfDwMDo7O13k2klJSatmDZH39mo5TO6sfIgfGlF/cpdXI/WAInTi4YO/e7WwsACVSoWZmRkMDQ0hKSkJL774IqRSqd/EYzQaUVdXh3vuuQe33367y9dYlsXhw4ehUChw+vRppKWl4YknnsANN9yAnp4ej9d655138PGPfxzHjh3Dhz70IZw4cQKHDx9Ga2srampqAn4dCARDPFyEq9rgznOam5u9tgxCWXlwzUVXWwoNFfG4ExEQeBMRLCwsYGxsDOXl5diwYQMWFxehVqvpYh2JD8jJyRH8ANpXEOHE1q1bUVhYGO3bWRXc2cKWLVs8yrXduWuT/01PT9P3uM1m81muzc/KIVY+k5OTK4xNU1NTw0bgsUY8XEilUrqn09jYCIZhMDw8jIGBAbz11ltwOp342Mc+hgMHDuDAgQOrOpccPHgQBw8edPu1wcFBvPvuu+jq6kJ1dTUA4Omnn0Z+fj6ee+45j6Fu//Vf/4UDBw7gK1/5CgDg0UcfxZ///Gf893//N376058G+qNTCJp4QrlnQFJK+fOc1e4hWJjNZrS2tlKyW+2JMBTE40lEwLXwJ9daLUOHHHCVlZUwGo1Qq9WYm5ujgVyEhKLVcgkGZPY1Pj4e08IJd3JtjUaD3t5e2Gy2Fe7a4+PjmJiYwI4dOxAXFxfw8io/OZRr5TMxMRGwlY8viGXiAd6vOGUyGeRyObZu3YqTJ0/i+9//Pi5cuICqqio8/fTT+NznPgeNRhOwk4HVagUAl4dEqVSK+Ph4vP322x6J5+LFizRMjmD//v148cUXA7oPPgRDPNxDi7xBQyFnXm2e4wmhIB6dTucSye3LByUY4uH+rKvFGXDTQn3N0CE2IyRLhjxlt7a2QiqVIiErGZlZWSjL2SD4xVIiZddoNNi5c+ea2RTny7WNRiM0Gg1mZ2fR398PuVwOhmFQVVXlsrsTiuVVd1Y+XKlxqBYvyf3GOvG4CxI0m80oLS3FI488gkceeQRLS0tB2edUVVWhpKQEDz74IH72s58hOTkZTz75JKampuiivjvMzs6uWJbOy8vD7OxswPfChSBPB9IWCpZ4uPOcXbt2+bUAGGzlwV0KdWexEurrkr0TjUbjVkRA+vz8p1huhs7u3bt9bp8pFAoX9+QTo6/idf1ZMFMsqoeLcGPiLuTm5iI7O1twljokvdZisfj1M8cauGq1kpISdHZ2Qq/XIysrCwMDA+jv73dZXnUn1w50eZU7XN+8eTPMZjOdDZHFS66Vj7+f87VAPO7mpXxxQbBejQqFAidPnsSnPvUpZGZmQiaT4YYbbsDBgwdXne+FE4IkHvLGdjgcAUs3/ZnnuEOgFQ93KTQQn7lAiMdms6GtrQ1Op9OriMBThk5qauoKR2p/oLEv4C9LfwcrBaSQojdpBh9Uyla05MhSZDRbckRwIZfLsXPnzrDZrggJTqcT7e3tdGcsPj6ezmc0Gg0mJyddohZycnLcyrUDXV4FrixeEnEE14ZmaGgIFosF6enpVKHnS0TBWiUek8mEnJyckF6rsbERKpUKi4uLsNlsyMnJwZ49e7Bz506P/yY/P3/FEv/c3FzInPIFQzz+pJCuBn/nOe4QCPGQpVCSGxRIUqi/xEN2gpRK5QoRgTfSISquUGTo2BkHWABSiRQSAAzLIqcgB2XlRXQpUq1Wo62tDRKJhJJQVlZWRFtyRqMRbW1tUCqVqK6ujumDy1fY7Xb6ujc2NlKi5c5nuFELarUaExMTkEqlPsm1A1le5dvQEFNO4ofGjSjwFNgW68TjyeTUYDBg06ZNYbkm6YIMDg7i0qVLePTRRz3+3aamJpw9exb3338//W9//vOf0dTUFJJ7EQzx8BHIwR/oPMcdAiUAd0uh4bpuICIC4P0MnVCpuAoTcrA3vQYX9Z1gATSmV6Ek8Uo2D3cpkvT91Wq1iwyYzCTC2ZIj8tWioqKwhuoJCVarFa2trUhISEBtba3Xitad3Jcr1yYVCdddGwg+awhYacpJIgr6+vrcxleT93csE4+3isdfObXBYMDQ0BD98+joKFQqFTIzM1FSUoI//OEPyMnJoe3Wf/3Xf8Xhw4dx44030n9z5513oqioCMeOHQMA/Ou//iuuvfZa/OAHP8DNN9+M3/3ud7h06RKeeeaZAH9iVwiWePzd5QlmnuMO/hAfWQrlE0Ag8IV4uARbU1PjEsDGz9DhiwjCkaEjkUjw2Y234/qcXWBZFhUpJZBJVh4K3L4/kQGr1Woq105KSqIkFMqW3NzcHLq7u1FRUbEufOaAKwdYa2sr0tPTsW3bNr8Oab5cm8R1ECLyJtcONmuIuxhL3iPc+OqkpCS6lBnL8Dbj8VdMcOnSJXzgAx+gfyZqtLvuugvHjx/HzMwMHnjgAczNzaGgoAB33nknHn74YZfvQapcgubmZpw4cQL//u//joceeggVFRV48cUXQ7LDAwASNpoTJg4YhoHdbqd/vnjxIsrKynzqKZrNZrS1tUEqlaK+vj4klh49PT2QyWSorKz0+HeIj9no6Chqa2t9ssxfDSaTCW+99Rb279/v9utcEUFDQ4PPIgJuhk59fb3gBv5cnzKNRgMANDogmJbc+Pg4tQoKde9cqCDVN3ERD2V1x5VrazSaFXJt7vuKXw1xFZW+VkNckE1/IunneqEJxcrHV0xNTUGn06G2ttblv19//fX40pe+hE984hNRurPIQLAVD0kBXQ1knpOfn++zZNkXrOZcQJRRXHPTUF2XVC38nyVQEQEh5oSEBOzatUuQA3W+T5m7zXwyG/JldsayLAYGBqjII5IJstGEXq+HSqUKmwODJ7k2EZKQqpXMZ0KZNSSXy5GbmwulUom5uTns2LEDer0eMzMzVMTCtfIRcivOV1XbWoVgiWc1cQHJEhkcHAx6nuMO3oiPKOYUCgWamppCahPC3avgfnACFRGQ2UYsZehIJBKXVE2ymc9vyRGVHP9ncjqd6O7upuamgYg8YhEkJXXLli0h/zy4A1euTXa7SDXU2dkJhmG8yrUJCfkr1yZt5LS0NCiVSnptIlDo7OwEy7Iu1ZDQvAbJHg8XhMjXyk6ZNwiWeLzNWEI9z/Hn+jqdDm1tbUEp5ryBb+IIXBERdHR0oLS01GUwTtoXnuxvZmZm0NPTE/MZOtzNfIfDQdVXxCuMq74CAJVKBZZlsWvXLsEdOOEC+V1XV1eHTPLqLxQKBfLy8pCXl7dCrs13105LSwtYru3uve7O2Zs4rvf29rrk5ITTysdXhFJcEIsQDPG4c2l2V3Fw5zmB7Of4CnettomJCfT396OqqipsBzmXePwREayXDB25XO5yuBGV3OjoKLq6umjgWU1NzbohnYmJCQwNDQnqd+1Jrq3RaFbItblZNfzlVb5Qhnwu/bHysdlstBoiztBcK59otJ6dTqfb6wYiLohFCIZ4+HDXagvXPMcduCahJKVxbm4uoKVQf8Bdnh0cHPTqRECGtdzXgZuhs2vXrjX9JpZIJEhPT0d6ejry8vLQ2tqK5ORkSKVSvPfee0hKSqICBSGGmQUL4ng+OTkZVDR3JBCsXJtbDdlsNvoZ8dXYlOuyQaqhsbExl8VZcu1IVENOp3OFY4bT6YTZbF7Tn1kCwRKPTCajBnfceU44qw3+9cmbvK2tDQ6HA01NTRFRg0kkEnR0dACAXyICi8WC9vZ2SKXSdZOhA7w/29i0aRNKS0vpocRP8+Sq5IQosPAHLMuiv78f8/Pz2LlzZ0wdVqvJtePi4jxWQxaLBUNDQ8jMzAxoeVUqldKHlfLycpoaSgLb5HJ5UFY+vsJTCBwAccYTSbhzLjAajXRQrNVqwzbPcQeZTAabzYZ33nkH6enpaGxsjMiW/fLyMhiGgUKhQENDwwoRAWk/8klnaWkJKpUKWVlZYa8GhYSpqSn09/evmG24a8lpNBrakktPT6cChVjrqRNJ/dLSEnbt2iU4aby/SExMxIYNG1wWSDUaDfr6+lzk2qmpqfR3V11d7fIQFujyKj81lFj5DA8Pw2w2Iz09nRJRUlJSyKohb8QTSw8RgUIwezzAFbkwuZ2JiQnMzs7ScnrHjh0RNXMcGBjAyMgIKioqsGnTpoiU32RgzrIsdu7cSRflVhMRzM/Po6urC2VlZYIMMQsH+HMsf5YKyRO2Wq2GTqdDYmIirYa4js1CBNd3raGhYU1XtVy59vz8PBYXFyGXy1FUVITs7OwV7tp8IiLwVa7Nh8lkosamer0ecXFxLtVQMKGIly5dwoYNG1x2/wYHB9Hc3AyTySTo92AoIJiKhw+z2Qy9Xo/i4uKIPsGzLIuhoSGMjY0hLi4O5eXlEbkmaSVWV1djcHCQfnBWExG4y9BZ6yAxDnq9PqA5FvcJm7uUyJUAk2pISC054rsmlUpdfNfWKohcWyKRYGJiAkVFRcjMzKSSaYZhkJmZSdun8fHxLrNZd8mrgVj5FBcXu1j5DAwMwGazrbDy8QfuvNoMBkPEZkzRhuCIhxzC4+PjiI+Pp6l5kQB3KXT79u3o7e0N+zXJIapWq2krcXh4eIW6B3AVERDBg1ar9Zqhs9bgcDjQ3t4Ou92O3bt3B61qJEuJubm5VIarVqsxPj5OM2TIwmQoWy3+wmKxoK2tDYmJiSv2uNYyDAYDWlpaUFBQgIqKCkgkEiqZXl5ehlqt9ijXDuXyKtfKh2VZauWjVqsxODiIxMREF2PT1b6fp1bbetk5ExTxMAyDzs5OaLVabNmyBZOTkxG7NvG2iouLQ1NTE6xWa1jit7nw5ERAJKOeWmskQ8fpdK7pPBk+yOEbHx+PnTt3hnzmxpXhbt68GRaLBWq12sWjjJBQJFtywfiuxTJIfHtxcfEKD0SJRIK0tDSkpaWhvLwcNpuNChSIXJso1dy5awe6vEr+HglFJLtlpBrq7e2Fw+FwqYbcfT49EQ+p8NY6BEM8LMvi73//OxiGQVNTE8xmM0ZHRyNybXcxCg6HI+gIam8gT3JpaWnYvn27yyFKru8upZBY+weboRNrMBgMaGtrQ2ZmZsRarwkJCS5Db6KS47fkwrkZH07fNSFjaWkJra2tKCkp8SkmIC4ubkX6KVdMQipXX+TaBP5Y+XhKfR0YGHBr5eOJeGJN6BIoBEM8EokEVVVVSElJgVQqhd1uD3vFAXheCpXJZB4904IFERHwnQiAKx+E+Ph49Pb2Qq1WIzc3l5pkhjJDJ5ag0+nQ3t5OD6Fo/NwymWxFS06j0axoyYVyFyTcvmtCxcLCAtra2qhYxl9wXdArKir8kmsHmzXkyUaIb+XjdDpXnG/rZXkUEBDxAEB6ejr9RZM9GpZlw/KB4y6FchVkBNz47VARD19EwM3C4Wbo1NTU0P41WbBLSkqCyWRCRUUFSktLQ3I/sQBiBVNVVYWioqJo3w6AlVv5FouFquS4LTkSGxDI+yfSvmtCgV6vR1tbW0gjLDzJtfv7+2G1WpGRkUFVjYmJiSHNGuLbCJHPNXBF2ZaamorMzExMTk5iaWnJ7xnPhQsX8Pjjj6OlpQUzMzM4deoUDh8+TL9uMBjw9a9/HS+++CK0Wi3KysrwxS9+EZ/73Oc8fs/jx4/jk5/8pMt/i4+Ph8Vi8evevEFQxMMlGLlcTn/ZoW4n8Wcr7hQp5A3lydrCX7gTERDwRQQymcxlya27uxvz8/NITk7G4OAgZmdnaWm/VnvCXMVeXV0dsrOzo31LHpGQkIDi4mKqfiIque7ubjidTrdGmd5AyLampiYkURuxAq1Wi/b29rCSrTuRAN+Alnyd7669WtYQ+f89gcylEhISMD4+jqamJiwsLKCrqwt33XUX7HY7cnNz8dxzz+HGG2/0yf7IaDSirq4O99xzD26//fYVX3/ggQfwxhtv4De/+Q02btyI119/HV/4whdQWFiIW2+91eP3TUtLQ39/v8u9hxKCIh4uuBVHKImH9I7T09O9qoPIE00o5jxc94O9e/e6EJ23/RyHw4GOjg5YLBYapU2GqMSfLC4uDrm5uTGxg+IrWJZFX18f3cqPJcUePzaAq7wi9izk6+5ackL0XYsESIVXVVUVklRcX8AVCZSWlrq4XXR2dq54aCBybWBlNeSPQIGQVkJCArXyGRsbw2c/+1kMDg7ie9/7Hv7lX/4FV111Fd544w2vn+mDBw/i4MGDHr/+zjvv4K677sJ1110HAPjMZz6Dn/3sZ/jb3/7mlXiIejBcECzxkBfb4XCEbHA7OztLrVV8mRUEEr/Nh8FgQGtrK1JTU1e4HwSSocMdonKfrsnAOzs722UuFGtwOp3o7OyEyWTC7t27Y3orn6+8Ii05jUaDkZERmuRJHhpGR0cxNTW1rvKDgCsL0J2dnVF11gZWul0sLy9Do9Hg8uXL6O3tRUpKCv19BSPXJg/T3M87eYAsKCjAU089hcuXL6O1tTXoB8nm5ma89NJLuOeee1BYWIhz585hYGAATz75pNd/ZzAYUFpaCoZh0NDQgP/8z/8M6WqLoE4mvlxytUweX8FdCq2rq/N50ZK7jBYIiIigpKSE7iAQcN+ogWbo8J+ul5aWMD8/T+dCZLkuJycnJiTXNpsNKpUKEolEsIF1wcBdS06j0aC7u5saX5aVlcU02fqL2dlZdHd3Y/v27YJagOY+NBCHa/LQ0NraColE4hLH4Y9c2+FweAyBIw70xMYnWDz11FP4zGc+g+LiYsjlckilUvz85z/HNddc4/HfVFZW4pe//CVqa2uxuLiI73//+2hubkZ3d3fIWqCCIh4+QlFxkHaVwWDwOyl0tRRST/BVRECuwc/Q6e3t9Xu4yh14k/A0tVqN2dlZ9Pf3IyUlhbbkhDgXIjLxtLQ01NTUrImWoTeQh4asrCzYbDYsLS0hNzcXarUaIyMjtCWXnZ0tyN9XKDA9PY2+vj7U1tYKPpbcF7k2IaKUlBSXlhy/GrJYLJSAuNVQOFRtTz31FN5991289NJLKC0txYULF3DkyBEUFhbihhtucPtvmpqa0NTURP/c3NyMrVu34mc/+xkeffTRkNyX4InHl/hrTyCLd/Hx8di7d6/fLbtAiI+ICObn51cVERB5Jvka8R6rq6sLur+flJSE0tJSlJaWusyFxsbGoFAokJOTg9zcXEHMhUiFV1hYuKIyXMvg+q7t2bOHvj+tViv9fY2MjCAuLo6SUGZmZtR/X6HA1NQUBgYGQvJejzT4cm1+C5V4uuXk5CAzM5O2vBmGgclkwvDwMJXlc6uhxcXFkO7xmM1mPPTQQzh16hRuvvlmAEBtbS1UKhW+//3veyQePhQKBerr6zE0NBSyexMU8bhzqA604iFLoYWFhaisrAzow+ov8ZBWkd1uX6GW8zbPCXeGji9zIXKwRXouRAxON2/ejJKSkoheO5rg+q7xXRji4+Npq4XIf9VqNXp7e2G321cMvGMNk5OTGBwcRH19vV/mrkIFv4XqSa6dmpqK7u5u5ObmorKy0uVBVKfT4eLFiwHtLXmC3W6H3W5fcfb5O0Igc9ebbropZPcmKOLhI5CKg2VZTExMYGBgAFu3bg2qJymVSn3+BRERQUpKChoaGnwWEVitVqhUqohl6HiaC42MjKCrqyuicyFyAK0ng1PAP981vvzXYDBArVbTgTfxJxNqC5WP8fFxjIyMCD64LlB4kmvPzc2hv78fcrkcEokEOp2OyrUXFhbw4Q9/GLt378Z3v/tdv65nMBhcKpHR0VGoVCpkZmaipKQE1157Lb7yla8gMTERpaWlOH/+PH7961/jiSeeoP/mzjvvRFFREY4dOwYA+M53voO9e/di8+bNWFhYwOOPP47x8XF8+tOfDs2LBIETj1wu96vVxm1zuVsK9Re+El+gIgKSoZOZmRkVDy5f5kKkJRfKQ42IPS5fvrxmDyBPMJlMaGlpCcj6RyKRIDU1Fampqdi0aRNtyWk0GtpCJSQUrG1/ODAyMoKJiQk0NjbGlEQ+UBC5tlwux9TUFPLz85GbmwuNRoOuri588YtfRF5eHqamplBaWoo//elPfj/sXbp0CR/4wAfonx944AEAwF133YXjx4/jd7/7HR588EH80z/9E3Q6HUpLS/Ef//EfLgukxNuOQK/X495778Xs7CwyMjLQ2NiId955B9u2bQvyFXkfgsrjYVkWNpuN/lmlUkGpVKKsrGzVf0sqB6fTifr6+pAog9ra2pCRkeGx/PVHRMCd5wDCz9DhzoW0Wi2dC5FDLVCSZBgGXV1dWFpaQn19/brxpgLe913jOi2HCgzDUJWcWq2GzWZziXeIZkuOzC+JVHw9JGwSWK1W6slYXV1Nf+cMw+CNN97A5z//eRgMBroIetNNN+Ho0aNrfnFY0BWPr+ICX5dC/YU3VZu36oqfocMXEZB2Q3V1tWDfYJ7mQl1dXQHPhex2u4ur9loOMeMj3L5rUqmUtngqKythNBpdWnKpqan0d5aamhqxBx2WZTE4OIiZmZmYi+gOFjabje7wcUkHuNJufeKJJ1BeXo4//elPsFgseO211/DKK69ExKMy2hB0xdPb20vNQz1hZmYGXV1dKC8vR1lZWUg/UF1dXYiPj0dFRYXLf+eKCBoaGjyKCPjby9wMnR07dsRku4GbWTM/Pw+TyeTTXIgsxCYmJqK2tlZwbaBwItq+a/zqVS6XUxLKzMwM2++CZVn09/djfn4ejY2N66q6tdvtuHTpEpKTk1esB1gsFnz0ox+FwWDAa6+9FpPnQLAQNPEMDAzAbre73ZglT1ITExOora0Ny3DaHfFxRQS1tbU+iwhsNhs6OjrgcDgiHuMdTpC5kFqtxsLCgtu5EGkxETXPWpAD+4rp6Wn09vYKxneNYRiqktNoNLBarfTBITs7O2TvS5ZlXYIK19NSrN1uR0tLCxWPcN/vVqsV//RP/wS1Wo3XX399Taj6AoGgWm38akUmk8FsNq/4e/yl0HCV72TJi0Cj0UClUrkVEXgjHW6GTn19/Zp62ve0LzQ+Pg6FQoHU1FTqihvqilToEKLvGglIy8rKotkxarUaMzMz6Ovrow8OwbTkWJZFd3c3FhcXsWvXrjXzkOUL7HY73R3kk47NZsNdd92F6elpnD17dt2SDiAw4gGukA8pwtzt8QS7FOoPZDIZbDabi0R727ZtLlYWRERA7DH4pEMydIqLi1dk76w18OdCQ0NDmJiYgFwux/j4OIxGI93WX2t2OFzwh+lC9V3jZseUlZWtSPEk0mB/WnJEPGIwGLBz586Y3DMKFA6HA21tbYiLi0NdXZ0L6TgcDnz605/GyMgI3nzzTcE8iEQLgmq1AaAHPXBlu3lmZga7du0CEJqlUH8wOjqKhYUFxMXFYW5ubsXCmzcRAXBlT4XsE0XKcVcIYFmWSmfr6uqQkZFB50JqtRpGoxEZGRnUwmctPRETZ221Wo2GhoaYHaaTlhypYH1pyZHoepPJhMbGxnUlHiGkI5PJUFdX50LSDocDn/3sZ9He3o433ngjqkaoQoGgiWd2dhajo6PYu3cvlS0HuxTqD0ZHRzEyMoKEhASPIgJyr1wSZFkWAwMDmJmZoQfvegFXQOHp4PVlLhSLIE/7y8vLK94vsQxunLNarcbi4iL9nWVnZyMtLQ0Mw6CjowNWqxUNDQ3rinScTifa2togkUiwY8cOF9JxOp04evQo3nnnHZw7d04wYYbRhuCIx2630wpCrVajr68PGRkZmJ+fj6jFhsFgwN/+9jewLItrr73WrYiApKPyM3Q6OzthNpuxY8cOvxMFYxlk9ma1WlFfX+9TJROufaFIg+u7ttYPXrvd7vI7I+1lmUyGxsbGNVXBrgan0wmVSkXjA7ikwzAM7r//frzxxht4880311Vy8GoQNPHMzc1BpVIhNTUVDQ0NEXtDExFBZmYmrFari1Prahk6KpWKDhbX8hyDD6vVira2NigUCtTW1gb0sxOfq/n5eajVajAMg6ysLJovJNTXk+u7tmPHjpjMQQoUNpsNLS0t1BOMeJPFUhxHoCAPG2Rpnft7ZxgGX/3qV/Hyyy/j3Llz2LRpUxTvVHgQLPEsLi7SN/QNN9wQMSXY+Pg4FREoFAoMDg5i3759LiKCYDJ01iIMBgN1eQiV9Q93X4g7FyIHmlDaWBaLBa2trUhKSgrp8rI7SJy9UJj/A2A1cCoOwxn/WSCKbUlCuDKZjLaYiEpOo9FgYWEBycnJ9HeWlpYWs21UPhiGQXt7O93l45PON77xDTz//PM4d+7cij1AEQIkHofDgampKXR1dWHDhg0YGxvDgQMHwn5dMpvgigg0Gg16enpw9dVXexURBJqhsxZANvI3bNiA8vLysB0snuZCkd7E58JoNKK1tTUg37VAEGf4KCTMOIAr17En/n9gFJ4DvcIJIhuOi4vzuBBMWnLkf8RdgR8XEGvgz7O4lTjLsvj2t7+NX//61zh37pzX5ff1DMH95gcHBzEyMoK6ujqkpaVhbGyMVhjhgs1mQ3t7O22rkadpYh/uTURAMnRqa2uRnZ0dtnsUIubm5tDd3R2RjXzuvhA50Obn5+m+UKTnQktLS2hrawuL75pbsCwkrBqADEACACMk7Hx4r+kBpL1GXCg8vd4KhQIFBQUoKCgAwzBYWFiARqPB4OAgzGazi0pOKBXsaiDKPYvFgsbGxhWkc+zYMTz77LN48803RdLxAsERT3p6Ol0KJcubTqczbIcJ14lg7969Lk9hUqkUdrsdy8vLK56qnU4nuru7sbS0FJYMHSGD7DUNDw9j+/btEU+P5B9oxEeuu7sbTqfTxRwzHHMhUuURg9eIQCKBU34AMvspAEawkgww8qZV/1moQUwvU1JS/EqKlUqlyMzMRGZmJrZs2UJVciQugLTksrOzoVQqBdmSI6pFIhfnk84TTzyBp59+GmfPnkVNTU0U71T4EGSrjSyNsiyL1157Dddee21YnoiIiGDDhg3YsmXLCicCm82Grq4uaLVaJCQkIDc3F7m5uYiPj0d7ezukUinq6urWtIKJD+K/NTc3hx07dghqOTISc6Go+q6xDkgdZyFhtGAU14KVRlaaa7FY0NLSAqVSGdIYD7vdDq1WS2dDEomEklBWVpYgWnIsy1Kp/M6dO10+8yzL4qmnnsL3vvc9vPbaa3TvUIRnCI54nE6ni03Nn//8ZzQ1NYW8ouCKCPjaen6GjtPphFarpWorp9OJpKQkVFZWrpkoYl9AklINBkNM7KmYzWZqZhqKuZDQfNciCbPZ7JIjFK6KhAiLCAkRE1oyG4rGe45lWfT09GBxcRGNjY0ubgwsy+JnP/sZvvOd7+DVV191UcCK8AzBE88bb7wRUtsRhmHQ19eH2dlZv50I5ufn0dnZifz8fEgkEir5JcuPWVlZa8qHjQviyC2RSGKyyuPunmg0Gr/nQuPj4xgeHsaOHTuQmZkZobsWBkh4XU5ODiorKyPaBiOiEo1GA71ej6SkJPp7i0RLjpid6vX6FRZALMvi2WefxUMPPYSXX34Z11wTHaFHLELwxHPhwgVUV1eHxNvIbrdDpVJRNQp3uXM1JwJ3GTosy2JxcRHz8/OYn5+H1WqlT2Y5OTmC3TvxFyaTCW1tbbSvH+vkyp0LkQrW01yI67tWX18vqNZiJGA0GnHp0qXIiSi8wG6309+bRqMBAPp5C0dLjtgfEYdt7k4Sy7L4zW9+g3/7t3/DSy+95JICKmJ1CI54GIaB3W6nf/7rX/+KioqKoGMPjEYjWlpakJycjLq6uhVOBES9xt/P8TVDh9iKEBIyGAxrwo9scXHRZT9JiEPfYOBtLpSdnY2xsTFoNJp1lycDvJ+YWlRUFFapfCAgD33k92YymVx+b8E6hpBZplqtXhHrwLIsfv/73+O+++7DCy+8gP379wf746w7CJ543n33XZSUlARlsknMRYuLi92KCMKRoWM2mykJLS4uIi0tjbbkYuUAI4P08vLymLf7YFgHJJCtenhy50J6vR4SiQTFxcUoLCyM2r5QNEBSfUtKSmJi695kMtFWKrcll52djfT0dL9+b8RrkaQL8+dKJ0+exGc/+1n8/ve/x8033xzqH8Utjh07hpMnT6Kvrw+JiYlobm7GY489hsrKSgCATqfDt771Lbz++uuYmJhATk4ODh8+jEcffVSQVbrgiefSpUvIy8sLeDFzYmIC/f39bs1F+SICTxk6wbaXbDYbPcy0Wi2SkpJoJSTUbe6pqSkMDAwIOp7bFzgYM/6+8H3MW1qRLM/HnoxvIFXhXY1GPOdsNhuKioqoS7NcLqcPD7HkI+cvFhcX0draGlm5eAjhcDhcVHIAaCz4atZLLMtiaGiIRnXzK6c//vGPuOeee/Cb3/wGt912W1h/Di4OHDiAj33sY9i1axccDgceeughdHV1oaenB8nJyejq6sK3vvUt3H333di2bRvGx8fxuc99DrW1tXj++ecjdp++QnDEw08hJVYs/n4AiIhgZmYGDQ0NK0QEpNIBVooIwpmh43A46PIjd8idm5uL9PT0qB9m3KXYHTt2xLyz9sDy8+haPg4JZGBZB3ITGnBV1qMe/z4RURB7e9KS5c+FHA4HnS+Ea18oGiA7SuXl5SgpKYn27QQNbktOo9HQVir53fGJZWhoCJcvX8bOnTtXdCZeffVV3HnnnXj22WfxkY98JJI/xgqo1Wrk5ubi/PnzHkUNf/jDH/DP//zPMBqNgpCkcyGsu3EDmUzmIjbwBVwRQVNTk1cRAZ90pqamaIUUjgwduVyO/Px85Ofn08OMqOVYlqXChGgo5BiGQU9PD/R6PXbv3h0zLUFvsDJLAAAZ4uGAA1an3uPfJb5rycnJK6pcYveSnZ2NqqoqLC8vY35+HmNjY+ju7hakj5y/0Ol0UKlU0dlRChMkEgnS09ORnp6OiooK2kolDgpJSUmUhHQ6nUfSOXv2LO666y4888wz+PCHPxyln+Z9LC4uAoBXhSVp8QuNdIAYqHh6enogk8loL3M1BCoiiHaGDl8hZ7PZqDNzJJ6o7XY7Ojo6YLfbUV9fv2aSIxdsQ3hL+xDsjAFSiQJ1ys+hLPngir8XjO8aOczIfCE5OZm2UmNlLqTRaNDR0YGqqqp1E1pIWnLEQcHpdCI7OxsFBQUuLbkLFy7gwx/+MH70ox/h7rvvjvrvk2EY3HrrrVhYWMDbb7/t9u8QQcw///M/4z/+4z8ifIerQ3DEA1yx5SDo7++H0+nEtm3bVv13RERQVFS0Yt/Am4hAaBk6LMvCYDBQEjIajdTXijgnhBIWiwVtbW2Ij49HbW2tIJ+QgsGyYwoaaxdS5UXIjt++4utkkF5UVBR0a5W/L0TmQsQYM9qtVHcgFXd1dfW6TMccGxvD6OgoKisr6d7Q+fPn8cc//hFbt27FSy+9hCeeeAL33ntv1EkHAD7/+c/j1Vdfxdtvv+22Ml1aWsI//MM/IDMzEy+99JIg28CCJ57h4WEYjUbU1tZ6/TeBighIhg5x2RXiL4l8GLgKOfJEHWw7bHl5GW1tbbSFJMSDMZzQ6XRob28PyyA9FuZCc3Nz6Orqwvbt24NeWYhFEM/BxsZGl1WJsbEx/OAHP8CvfvUrSCQSlJWV4ZZbbsHhw4dx9dVXR+1+jx49itOnT+PChQsoKytb8fXl5WXs378fSUlJePnllwW7xiFI4uHGX4+NjUGv16O+vt7t3+WKCOrr6116nquJCBYWFtDe3o7c3FxUVlbGxKFrtVopCel0OqqQy83N9butQ0QUpaWlKCsrE8TTXCQxPz+Prq4uVFZWhj2SmGVZLC8v09+dEPKFSJxHNIxehYDJyUkMDQ2hoaFhheS4tbUVt9xyCx5++GHce++9OHv2LF5++WXYbDb8+te/jvi9siyL++67D6dOnfKY8bO0tIT9+/cjPj4ef/rTn6LeufEGwRPP5OQkZmdn3Rrv+eNE4ClDZ/PmzTGr3nGnkCOVUEZGhlcimZmZQU9PT9hEFELH9PQ0+vr6UFNTE5UnfXdzIdJKjcRc6PLly+jv70ddXV1IXEFiDVNTUxgcHER9fT3S09NdvtbR0YGbbroJX/3qV/G1r31NEA9kX/jCF3DixAmcPn3aZd6tVCqRmJiIpaUl3HjjjTCZTDh16pRLJyQnJ0dwbiOCJ56ZmRmMj49j7969Ln+HDIOTkpLcigjIPEcikaywvxkZGcHExAS2b9++ZjJ0nE4nbevMz1/JaSEHWWZmJn3jsSyLsbExjI2Noba2dl0eOsT+qK6uThC+a5GeC01OTmJwcHBd+s4B75Mu36sRuCJmOnjwII4ePYpvfvObgiAdAB7v49lnn8Xdd9+Nc+fOebTtGR0dFdw+liCJh8RfA1faISR+miBQEQHJ0FlcXER9ff2azdBhWRYLCwtUnGC326kUWKfTQafTob6+HqmpqdG+1YgiFnzXGIaBXq+nTuihngsR0nX3pL8eQCpdd6Tb39+PgwcP4lOf+hS++93vCoZ01iIETzxarRZdXV249tprAQQuIrBarVCpVOsuQ4co5GZnZzE5OQmn04mMjAzk5+cjJydnzcimVwNxGdZqtWhoaIiJHSV3c6H09HTaTvV3LjQ6OoqxsTG3M431ANJed9deHBoawsGDB/Hxj38c3/ve92Ji3hvLELxuVi6X0wqmv78f09PTaGxs9Coi4JPO8vIyVCoVMjIyQhpgFQuQSCSIi4uDTqdDWloaKioqoNfr6ZOfUqmkLTkhDyODAYkrNhqN2LVrl2CVPnxIJBKkpaUhLS0N5eXlLnOhgYEBOhdazXqJtJcnJyexc+fOdVfpAlfUe729vW7by2NjY/jQhz6EO+64QySdCEGQFQ83hdRgMOCdd95BZmYmLBaLWxGBtwwdYnRJ5LLrrXwmnnNKpRLV1dUuHyq+Qo4sPubm5iIlJWVNvFYOhwPt7e1wOByor69fM5Uudy6k1Wohk8nczoWI9xh5YFur7WVvIHtKtbW1K9R7k5OT2L9/Pw4cOICf/OQnIulECIInHp1Oh7/97W/Izs7Gjh07/BIRuMvQWU9YWFigs7DVFiPJQUaMTOPi4lw85GKRhGw2G9ra2iCXy1cIUNYSyFyIVEPcmR7xKVuPsQ7AlQfPjo4Ot3tKMzMz2L9/P6699lo888wzglN+rWUImni0Wi3a2trgcDjwwQ9+cEVAlycRAcnQ0Wg02LFjx7rsZ5MdlYqKCr+dvYlCjgy4JRKJi4dcLDwVcn3Xtm/fHhP3HAqQudD8/DympqZgt9uhVCrpTC9WfeQCgUajQXt7u9uo8tnZWRw8eBC7d+/G8ePHRdKJMARLPGNjY+jr60NlZSV6enpw3XXX0d68N9Kx2+20teJvhs5awcTEBIaGhkKyo8IwDBYWFmhLjjxNEw85IVYRRGqflZWFrVu3xmS1FgxYlqVmr9XV1VSgwN0XEnIkRyig1WrR3t6Obdu2rbABUqvVuOmmm1BTU4Pf/va3gnwPr3UIkngGBwfpcldmZiZef/117Nu3D0lJSS4zHXcZOiqVij7lrrenGK7RaTjkwtyn6fn5eZhMJhoZnZubK4j5SSh912IRDMOgu7sby8vLaGhocHnw8nUuFOsgLttbt25FQUHBiq/ddNNNKC8vx+9//3tB2BatRwiSeAwGA+x2OxURnD17Fjt37kRKSopHEQHx3ApHhk4sgOwoLS0trRBghAtGo5FWQktLS1AqlVScEI2WTjh912IBRL1nMpnQ2Njo9UHA3Vwokm7o4YJer0dbW5tbl+2FhQXccsstKCgowMmTJwXxoLReIUjicTqdLhk8586dQ3V1NV144z+ZhTtDR+gg1kEsy2LHjh1R+UBZLBaXyOhIK+Qi6bsmRDidTnR0dFD7KH/eA9x9IbVaDYPBgPT0dFrJxspcaGFhAa2trW7fA0tLSzh06BDS09Nx+vTpddmCFxIESTzc+GuWZfHXv/4VSUlJKC4uRmZmJj3Eop2hIwSYzWa0tbUhKSlJMO1FrkJOo9EgPj6ekpBSqQw5CUXbdy3acDqdLpLxYKsVTz5yQp4LkbjuzZs3rxDTGAwG3H777YiLi8Mrr7wSM0S6liFo4mFZFg6HA8vLy7h8+TLUajVYlqXtgKmpKVgsFkFk6EQDS0tLaGtrQ15e3grrIKGAqBMJCRGFHPGQC3auIDTftUjD4XDQare+vj7kg3K73U5/f2QuRMQlQpkLEdJxF9dtMplwxx13AABeeeWVdbnHJEQIknicTidsNtsKEQHLsnTrfnZ2FgCQm5tLEwOF8CGIFEhi5KZNm1BaWipI0uGDKOSIOIEkPubm5iIrK8uvQ5MsRl6+fBkNDQ0uWSrrBXa7HW1tbZDJZNixY0fYq11PcyFSDUVjLrS0tISWlhb6OeDCbDbjox/9KEwmE86cObMu3yNChSCJ5xe/+AUWFxdx6623oqCgwIVQSIZOTk4OCgoKoFarMTc3B7vdTp+ks7OzBdFyChcuX76Mvr6+mE6MZFkWS0tLdC5kNpuRmZlJfci8zShi0Xct1LDb7WhtbYVCoUBdXV3E3+/clFz+XCgnJyciHYjl5WW0tLRg48aNK8QkVqsVn/jEJ6DVavH666+vS0NUIUOQxPOb3/wGTz/9NN577z3s3r0bhw4dwqFDh3DmzBn88Y9/xI9//GNs2LDBZdazvLyMubk5zM/Pw2KxIDs7G3l5eYLdNQkE3EiHtdZaMhqNtBJaXl72aIbJ9V3jy4XXC2w2G1pbW5GQkIDa2lpBVPpms5nO9SIxFzIYDLh06RJKSkqwadMml6/ZbDb8y7/8C6ampnD27Nk19TlZKxAk8QBXDtnp6WmcPHkSzz//PN566y1IJBLccccdePjhh7Fp0ya3b2buk9jc3Bx9ks7Ly4taOyAUIG4MJNJgLfeq+Qq5lJQUOlMYGhqC0+lcU75r/sBqtaKlpQUpKSmoqakRBOnwQeZCJF9IKpXSbkRGRkbQ1ZnRaMSlS5dQXFyM8vLyFde+5557MDAwgDfeeCNiyarHjh3DyZMn0dfXh8TERDQ3N+Oxxx5zCW175plncOLECbS2tmJ5eRl6vX7dVmKCJR4Cs9mMT37yk3jnnXdw11134d1338X58+exbds2HDp0CIcPH8aWLVs8PlGRJ+m5uTkYDAbazhHKwqMvcDgc6OjogM1mW3duDHa7HWq1GrOzs9BqtZBKpSgqKkJ+fn5YFHJChsViQUtLC5RKZcy4rHubC2VnZ/v9GSSkU1RUhPLycpffv8PhwGc/+1m0t7fjzTffjKg/44EDB/Cxj30Mu3btgsPhwEMPPYSuri709PTQVvAPf/hDWCwWAMCDDz4oEk+0b8IbHnroIZw/fx6nTp1Cbm4uWJaFTqfDiy++iJMnT+Ivf/kLKioqcOutt+K2227D1q1bPX4gTSYTbecsLS0hPT2dVkJCPcwtFgtUKhXi4uJQW1u7ZtqG/oD4riUlJSE/P59u30ulUvoQkZGRERMHcaAwm81oaWmh0R6xSLjBzoVMJhMuXbqE/Px8VFRUrAh5PHLkCN59912cO3cu6vt8arUaubm5OH/+PK655hqXr5G0UJF4BAyj0QiZTOaWGFiWxeLiIl566SWcPHkSr732GjZs2IBDhw7htttu89r/tlgstBJaXFyM+ta9OxgMBrS1tSEzM9Mroa5lePJd4yd1chVya01cYjKZ0NLSguzsbFRVVcUk6bgDaamq1WrodDokJSXRuR5/LmQ2m3Hp0iXk5uau6HAwDIP7778fb775Jt58880VkupoYGhoCBUVFejs7ERNTY3L10TiiQHi8QdLS0t45ZVXcPLkSbz66qvIycmh7bidO3d6PLitViuthPR6PVJTU5Gbm4u8vLyo7QcR+xcyPF0rh40/8NV3jSjkyO/QYrG42L/ESkvVHYxGI1paWpCXl+e1pRzr8DQXysnJQXJyMlpbW5Gdnb1iX41hGHz1q1/FK6+8gnPnzqGsrCyKP8X793TrrbdiYWEBb7/99oqvi8SzxoiHC6PRiDNnzuCFF17AK6+8AqVSiVtvvRWHDh3C3r17PT4R22w2OtjWarVITk5GXl4etX6JBGZnZ9Hd3Y2qqqp1af8CBOe7ZjAY6O+Qq5DLzc0VbEvVHQwGA1paWtzOM9YyuHOh+fl5WK1WJCQkYNOmTS5Se4Zh8I1vfAMvvPAC3nzzTVRUVET5zq/g85//PF599VW8/fbbKC4uXvF1kXjWMPFwYTab8ec//xkvvPAC/vjHPyI+Ph633HILbrvtNuzbt8/j3IRYv8zNzUGr1SIxMZFWQuHwH+OG19XW1iI7Ozuk3z9WQHzX3Bk9+gvSUp2fn8fCwgJVyEXyQSIQkGrPnVx4vcBqteLvf/87UlJSkJqaSudCzz77LCoqKqDRaPDqq6/izTffRFVVVbRvFwBw9OhRnD59GhcuXPBYfYnEs06IhwubzYY33ngDzz//PE6fPg2JRIKbb74Zt912G6655hqPbRmHw+HiPxYXF0croVDsKbAsi/7+fszNzaG+vn7dbllfvnwZ/f39YfFds9lsLimrCQkJlISE5EFGLGDWq8s28L5sPC0tDdXV1fR3Yzab8dRTT+EXv/gFLl++jM2bN+PDH/4wDh06hF27dkVtDsqyLO677z6cOnUK586d81p9icSzDomHC4fDgfPnz+P555/Hiy++CKvViptvvhmHDx/GBz7wAY9tGa7/mFqthlwupwdYIDHRTqeT2tnX19cLRtwQaYyNjWF0dDQiy7FOp9PlQYJk00RbIbewsIC2tja3vmPrBTabzWVXift5YlkWP/jBD/Bf//VfOH36NKanp3H69Gn86U9/ws9//nP84z/+Y1Tu+Qtf+AJOnDiB06dPu+zuKJVK+nmenZ3F7OwsLl26hHvvvRcXLlxAamoqSkpK1t2S67omHi6cTifefvttvPDCCzh16hSWlpZw8OBBHD58GDfccINHkQHDMC4kJJFIaDsuPT191QPMZrNBpVJBIpFgx44dMbvgGgyi7bvGVcjNz8+DYRhKQllZWRFTyJEAsy1btridDawH2O12XLp0CcnJySsWZFmWxY9+9CM8/vjjeP3117Fz506Xf8eybNSEJJ4eNp999lncfffdAIBHHnkE3/72t73+nfUCkXjcgGEYvPvuu5SE5ufnsX//fhw6dAgHDhzwOBvgH2DESduTk6/JZEJrayttJ6wlCbCvEJrvGpHok9+h1WqlCrlwOl8Q09dQzLViFXa7HS0tLUhMTMT27dtXkM5Pf/pTPProozhz5gz27t0bxTsVESxE4lkFDMOgtbUVzz//PE6ePImpqSnccMMNOHToEG666SaPswGWZbGwsED945xOp8tTNNnRKSwsXLEMt14gdN81lmVdPOQMBgMyMjIoCYXqftVqNTo6OrBt27YVUc3rBcT0NC4uDnV1dStI55e//CW+8Y1v4JVXXsHVV18dxTsVEQqIxOMHyEH5/PPP49SpUxgcHMT111+PQ4cO4UMf+hAyMjI8ktDS0hIlIavVCpZlUVBQgKqqqnVZ6TgcDrS3t8PpdEYtNdVfkIA0opAj+165ubkBV2pzc3Po6upCTU1NRC1ehASHw4HW1lbI5XLs2LFjBen87//+L77yla/gj3/8I6677rro3aiIkEEkngBBWkSEhLq7u3HNNdfg8OHDuOWWW5Cdne2WhCYmJjA4OIjs7GwYDAbqpE2eoteDJY7NZkNbWxvkcjnq6upi8mfm7nvpdDokJibSitZXhdzMzAx6e3uxffv2iJlZCg1OpxOtra2QSqUrMoVYlsX//d//4Ytf/CJOnjyJG2+8MYp3KiKUEIknBCDDcUJCbW1taG5uxuHDh3HrrbciPz8fDMPgF7/4BbZs2YL6+nqkp6fTVg6phIxGI7KysmLeSdsbiO+akN2V/YXD4XBJWZXJZC4qR3c/I5GN19XVISsrKwp3HX04nU60tbUBAOrr61dU/idPnsRnP/tZ/P73v8fNN98cjVsUESaIxBNikCXQF154ASdPnsR7772HXbt2YWFhATqdDufPn/cok+U7aWdkZNBdoVhoRa0GT75rawkMw0Cn01GVI8uyLimrMpkMk5OTGBwcxI4dO9adjJbA6XRCpVKBYRg0NDSsIJ0//vGPuOeee/Db3/4Whw8fjs5NiggbROIJI1iWRU9PDw4fPozZ2VmYzWY0NDTQYLuysjKPh6/ZbKYkRJy0Y9H2hWBxcRFtbW00Q2Utkg4f7hRySUlJMJlMqKurW7fOFE6nE+3t7XA4HGhoaFjRan311Vdx55134vjx4/jwhz8cpbsUEU6IxBNGTE1N4cCBAygrK8Nzzz0Hg8GAU6dO4YUXXsD58+dRU1NDScibASTf9iUtLY1WQrGwbEp81zZt2oTS0tJo305UQJwpLl++jISEBJjN5rAo5IQOhmHQ3t4Ou93ulnTOnj2Lj3/843jmmWfwiU98Ikp3KSLcEIknjNDpdHjqqafwjW98w+UDxrIstFotTp8+jRdeeAFnz55FRUUFjXPw1oay2Wy0EiLpnISEor0D4w7z8/Po7OzE1q1b1+1+Coksn5ycRGNjI1JTU2lFOz8/j8XFRaSlpVESEuLvMRRgGAYdHR2wWq1oaGhYMcO8cOECPvzhD+O///u/ceedd66Lqni9QiSeKIPs+5BModdffx0lJSWUhPiLdFyQdE5iYpqcnExdE5KTk6P+wQ2n71qsgAhPpqen0djY6Hb5mDxMqNVqaLVamkuTm5uL1NTUqP8eQwGyimA2m9HY2LiCdP7617/ijjvuwPe//33ce++9a+JnFuEZIvEIDEtLS3j55Zdx8uRJnDlzBrm5uTRTqLGx0SMJORwOKu/VaDRISEiglVA0Dq9I+q4JFSzLYmBgAHNzc2hsbPSpkuGb0SoUCirT9sWCSYhgGAZdXV0wGo1obGxcIZR57733cPjwYfzHf/wHjhw5IpLOOoBIPAKG0WjEq6++SjOFMjIyaKbQnj17PC6eEgPMubk56qRNnqCVSmVYP9jcJ/z17LLNsiz6+vqg0WjQ2NgYUKAg8QEkDxQAKAllZmbGxOIxy7Lo7u7G0tISdu7cuYJ0Wltbccstt+Cb3/wm7r//fpF01glE4okRmM1mvP7663jhhRfw8ssvIyEhgWYKNTc3e1zC5Dtpkx0TYmIayg86wzDo7e2FTqcThO9atEDUjHq9Ho2NjSERgJCWLJkL2e12l5RVIe58kddhYWEBO3fuRHx8vMvX29vbcfPNN+NrX/savvrVr4qks44gEk8Mwmaz4S9/+QteeOEFvPTSS5BIJPjQhz6E2267DVdffbXHnR/ujsn8/Dx10g5FFIDT6aTtFCH6rkUKDMOgu7sby8vLYXsdWJaFwWCgv0ej0YjMzEwqTuAf8NEAcfYg5Mt/Hbq7u3Hw4EF88YtfxMMPPyySzjpDRIjnwoULePzxx9HS0oKZmRmcOnXKZSnMYDDg61//Ol588UVotVqUlZXhi1/8Ij73uc+F+9ZiHna73SVTyGaz4UMf+hAOHTqE66+/3uMhxDCMi4kpy7IuJqb+kBDXd62+vl6QT9+RABmgm0wmNDQ0RIwATCYTJaGlpSWqkMvNzQ2oxRcsSJtRq9Vi586dK0inr68PBw8exL333otHH31UJJ11iIgQz6uvvoq//vWvaGxsxO23376CeD7zmc/gjTfewC9+8Qts3LgRr7/+Or7whS/g5MmTuPXWW8N9e2sGTqcTb731Fo1zMBgMLplCnlo+/DaOw+HwOY+G+K4pFArU1tbGpO9aKECkwhaLBQ0NDVFzmrBarS4eckTpSKK+w33Ik30ltVqNnTt3rnjPDQ4O4uDBg/inf/onPPbYYzEplhARPCLeapNIJCuIp6amBh/96Efx8MMP0//W2NiIgwcP4rvf/W4kb2/NwOl0umQKaTQamim0f/9+j5lCxEmb7ArZbDZq+ZKdne1CLGazGa2trUhNTV0zvmuBgGzik6VIoVR8drsdGo0GarWaKuSCScpdDSzLYnBwEHNzc25JZ3R0FAcOHMDtt9+OJ598ct2+X0QIhHg+85nPoK2tDS+++CIKCwtx7tw53HrrrXjllVdwzTXXRPL21iQYhkFLSws1MZ2amsI//MM/4NChQzh48CCUSqXbf0dmCaQdZzabqYlpUlIS2tvbkZ2dvWZ913wBMbpkWRb19fWCrficTqeLhxwQWoUcUTPOzMxg586dK1p8ExMTOHDgAA4ePIgf//jHESWdY8eO4eTJk+jr60NiYiKam5vx2GOPuURUWywWfPnLX8bvfvc7WK1W7N+/Hz/5yU/WbVRFuCEI4rFarfjMZz6DX//615DL5ZBKpfj5z3+OO++8M5K3ti5AWkKEhIaHh2mm0M033+wxUwgAHWjPzMzAZDIhMTERGzduXDMmpv7C4XCgra2NxpYLlXT4IPM90pKz2+0eq1pfQaLLd+7cuULNOD09jQMHDuC6667Dz372s4jLwA8cOICPfexj2LVrFxwOBx566CF0dXWhp6eH3uvnP/95vPLKKzh+/DiUSiWOHj0KqVSKv/71rxG91/UCQRDP97//ffz85z/H97//fZSWluLChQt48MEHcerUKdxwww2RvL11BW6m0MmTJ9HT04Nrr70Whw8fxoc+9CG3mULEd624uBhyuRzz8/NYXl6mvmO5ubmCUFWFGyQxU6FQoK6uLiZ2atyBZVksLy/T+Z7JZPJbIce1A+K3cGdnZ3Hw4EHs2bMHzz77rCBeJ7VajdzcXJw/fx7XXHMNFhcXkZOTgxMnTuAf//EfAVwRQGzduhUXL14UY7bDgKgTj9lshlKpxKlTp1wyNz796U9jamoKZ86cieTtrVtwM4VOnjwJlUqFffv24dChQzRT6Ne//jUWFhbw4Q9/2MV3je87plQqqWvCWpRV22w2tLa2Ij4+fkVMc6zDaDTSSmhpaQlKpZI+ULgTp4yOjmJ8fBw7d+5cQTpqtRo33XQTtm/fjt/85jeCqQiHhoZQUVGBzs5O1NTU4I033sAHP/hB6PV6pKen079XWlqK+++/H1/60peid7NrFFF/J9jtdtjt9hUfXplMBoZhonRX6w8SiQQVFRV48MEH8fWvfx1jY2N44YUX8Ic//AFf+cpXUFJSgsnJSfznf/4nCgoKXP5tYmIiSktLUVpaCovFQv3jBgYGqLQ3Ly8vJpy0V4PVakVrayuSkpK8+ujFKpKTk5GcnIyNGzfS36Varcbg4OAKhdz4+DjGx8fdVjparRa33HILqqqq8L//+7+CIR2GYXD//fdj3759qKmpAXClKouLi3MhHQDIy8vD7OxsFO5y7SMi7waDwYChoSH659HRUahUKmRmZqKkpATXXnstvvKVr9AD7Pz58/j1r3+NJ554IhK3J4IHiUSCsrIy/Nu//Ru+/OUv46GHHsIPf/hDVFVV4cEHH8Tzzz9P4xw2btzo0o5LSEjAhg0bsGHDBmp+OT8/j6GhIcE7aa8Gi8WClpYWpKWlobq6es2RDh/c3yVRyM3Pz2NsbAwymQwOhwNbt25dQToLCws4dOgQSktL8dxzzwlG5QcAR44cQVdXF95+++1o38q6RkRabefOncMHPvCBFf/9rrvuwvHjxzE7O4sHH3wQr7/+OnQ6HUpLS/GZz3wGX/rSl9atWkooePjhh/HMM8/gtddeQ11dHWZnZ2mm0IULF7B9+3ZKQhUVFR5/X1wnbZ1Oh8TEREpCkdgvCRZmsxktLS3IyMjAtm3bBH+/4cT4+DiGhoaQmZmJxcVFSCQSaLVaOJ1OXHPNNfjoRz+KzMxMvPjii4JqtR49ehSnT5/GhQsXUFZWRv+72GqLPETLHBFe8ec//xkbN25ERUWFy39nWRYajYZmCr3xxhvYsmULddL2JrEmDszExDQhIYG244QYA2AymdDS0oLs7GxUVVUJ7v4iiampKQwODqK+vh7p6elUIXf8+HH813/9F3Q6HbKzs/GDH/wAhw4dQmpqarRvGSzL4r777sOpU6dw7ty5Fe9lIi547rnncMcddwAA+vv7UVVVJYoLwgSReEQEDeJ8cPr0aZoptHHjRkpC3mYhxEmb7JeQJce8vLywO2n7AqPRiJaWFuTl5XlNiV0PIPlK9fX1yMjIcPmayWTC7bffjqWlJfzDP/wDXnnlFYyMjOCf//mf8Ytf/CJKd3wFX/jCF3DixAmcPn3aZXdHqVTSuePnP/95/OlPf8Lx48eRlpaG++67DwDwzjvvROWe1zpE4hERciwuLrpkCuXn5+PWW2/FbbfdhoaGBq8kpNPpMDc35+KkTUxMI33oGwwGtLS0oLCwEJs3b17XpDM9PY2+vj7s2LFjRb6S2WzGRz7yEZjNZpw5c4ZGYfT392NsbAz79++Pxi1TePq9Pfvss7j77rsBvL9A+txzz7kskObn50fwTtcPROIREVYYDAaaKfSnP/2JZgodPnwYu3fv9rjXwTAM9Ho9dU2QSCTIyclBXl5e0E7avmB5eRktLS3YsGEDNm3atK5JZ2ZmBr29vairq0NWVpbL16xWKz7+8Y9Dp9Ph/2/v3qOavM84gH9fMGCCBEuJXMRw8YKI4jKrtIAae5TCHBWt9azUTc7QTYSK8+hp8Wwt3uqYrZehLbZrSzdHsVzFCxdRAqNQPQFRrFpoYUflFkGIkoggefdHT94ZAxYFEkKezzmcg3mT+PAPX35vfr/nKSgo0NsZRkhfKHiIwajVap2ZQgKBAKGhoQgLC3viTCHt5wja/nEajYZbCT1tJ+2BUCqVqKyshLu7u86H0OaopaUF3333HXx9feHg4KBzrbu7G7/97W/R0NCAwsJCs500S54eBQ8xiq6uLpw9exaZmZk4fvw4LC0tdWYK9bcFl2VZKJVKbiX08OFDnXYvgz0Z39HRgYsXL2Ly5MkQi8WDei9Tp1AoUF1dDV9fX4hEIp1rPT09+P3vf4/a2lqcO3dOL5QIeZLRfRBhiJSUlCA0NBQuLi5gGAbZ2dk611taWhAREQEXFxcIBAIEBwejtrbWOMWaiLFjx2Lp0qX47LPP0NTUhKNHj4LH42Ht2rXw9PREVFQU8vLy8ODBA53XMQyD8ePHw8vLC4GBgdywtdraWshkMly6dAnNzc14+PDhU9d0584dVFZWYsqUKWYfOrdv30Z1dTVmzZqlFzoPHz7EH//4R1y7dg1nzpwxi9CRSqVgGAYMw6CqquqZ3yciIoJ7n8d/j5gTCp4BUKlUmD17Ng4fPqx3jWVZhIWFoa6uDsePH8fFixfh5uaGxYsXQ6VSGaFa08Pj8bBkyRIkJSXh1q1byMjIgK2tLTZu3AgPDw+sXbsWJ06cwP3793VexzAM7OzsMHXqVAQEBGDevHkYN24c6urqUFxcjKqqKjQ2NqKnp+dna2hra0NVVRW8vLwwadKk4fpRTUJraysuX76MmTNnYsKECTrXent7ERMTg8rKShQWFppV9+Z169ahqamJ63jwLA4ePIimpqYhrMo00a22p/R4r7mamhp4eXnhypUr8PHxAfDTZxJOTk54//33sXbtWiNWa9p6e3tRXl7OzRRqa2vDK6+8grCwMLzyyitP7H6gUqm423GdnZ1c48u+Omlr/7r39vbWawdkbtra2nDp0iXMmDFDb0eXRqNBbGwsZDIZioqKzGpVKJVK8Ytf/AIHDhwYkvfrq1myOaEVzyBpbwU9ekLbwsIC1tbW1JZjkCwtLREYGIj9+/ejrq4OZ8+ehaenJ7Zv3w53d3e88cYbOHbsGO7evav3WhsbG3h6euLFF1+Ev78/7O3t0djYiJKSElRUVODmzZt48OABWlpacPnyZfj4+Jh96Gg7j3t7e/cZOlu3bsXZs2dRWFhoVqHTF5lMBoZhkJ+fD4lEAj6fj5dffhkKhQK5ubnw9vaGUChEeHg41Gq1scsdcSh4Bmn69OkQi8WIi4tDe3s7uru7kZCQgFu3btGSeghZWFhg3rx5+Nvf/obvv/8epaWlmDlzJvbu3Qt3d3e8/vrrOHr0KNrb2/H4Il4gEMDd3R3z5s1DYGAgRCIRmpubUVJSgsuXL8PJyYk7e2Ku2tvbUVVVhenTp+sFsEajwbZt23Dy5EkUFhaa/U6/R8XHx+PQoUMoKyvDzZs3sWrVKhw4cAApKSk4deoUCgoKkJiYaOwyRxwKnkHi8XjIzMxETU0N7O3tIRAIUFRUhJCQkFHfRNJYLCwsIJFIsHv3bnz33XeoqKjAvHnzcPjwYXh4eGD58uVITk5Ga2urXgiNHTsWYrEYEydOhIWFBVxdXdHV1YVvvvkG58+fx3//+1+z+wtVu5Nv2rRpOuMugJ9CJz4+HmlpaSgsLMSUKVOMVOXItGvXLgQEBEAikSAyMhLFxcX4+OOPIZFIMH/+fKxcuRJFRUXGLnPEGRm9yk3cnDlzUFVVBaVSie7ubohEIvj5+eGFF14wdmmjHsMw8PHxgY+PD959913U1tYiPT0dn3/+OWJjYxEYGMjNFHJ0dATDMPjqq6/g4OCAOXPmcGdPuru7uSam2k7aj44AGK2USiUuXryIqVOnwtXVVecay7LYs2cP/vnPf6KoqEin3Qz5ia+vL/e9diS8p6enzmMXLlwwRmkjGgXPELKzswMA1NbWQi6XY+fOnUauyLwwDINp06Zh27ZtiIuLQ319PTIyMnDs2DFs2bIFL730Evh8PkpLS3Hq1CmdA49WVlaYOHEiJk6cyHXSVigUqK+vB5/P5/rHmUIn7YG6e/cuKisrMXnyZL2dfCzL4oMPPkBSUhLOnTvHbZwhuh49b8YwjN75M4ZhaK5YHyh4BuDn5gmlpaVBJBJBLBajuroasbGxCAsLQ1BQkBGrNm8Mw8DT0xNbt27Fli1bcPPmTURHRyMvLw8ajQbvvPMOwsLCuLkxj4YJj8eDi4sLXFxcuE7aCoUCFy5cgLW1NTfOQSgUmmwIaVsCeXp66m0UYFkWf//733Hw4EEUFBRg9uzZRqqSjFYUPAMgl8t15glt3rwZwP/nCTU1NWHz5s1oaWmBs7Mzfve73+Evf/mLscolfUhOTkZ5eTnOnz8PJycnZGVlITMzE++++y5mzZrFhdDjzUDHjBkDJycnODk5obe3F21tbWhpaUFlZSXGjBkzojppD5Q2dNzd3eHm5qZzjWVZJCUlISEhAXl5eXS7mAwLCp4BkEqleh9SP2rjxo3YuHGjASsiT+Phw4e4du0aZDIZd/gvOjoaGzZsQGtrKxdCu3btwvTp07nBdo/PFHq0W7ZGo0FbWxsUCgWqqqrAMAy3Eho/fvyI3Vii7bgtFov1dqexLIvPP/8cO3bswKlTp2gODRk2dICUEPz0S7e9vR05OTnIyMjAmTNn4OHhwc0UmjlzZr9h8mgn7du3b4NlWS6g7O3tR0wIqVQqyOVyuLq6YvLkyTrXWJbFv/71L2zduhUnTpyAVCo1TpEjFB0gHVoUPIT0QTtTKCMjA/n5+XB2duZmCkkkkn7DRBtgCoUCCoUCvb293DgHe3v7QTcxfVZqtRpyuRzOzs56txNZlkVqaipiY2ORnZ2NxYsXG6XGkUwqlaKsrAxWVlYoLy/HrFmznul91q9fj6NHj0KlUlHwGLsIQkayzs5OnD59mpsp9PzzzyM0NBTLly/H3Llz+w0TbSdt7TiHnp4eODg4wNHRcUg6aQ+UNnScnJwwdepUvc+iMjIyEBUVha+//hq/+tWvDFKTqWloaOB6BYrFYr22SwOlUCi4ThvOzs5PbPs0mlHwEPIU1Go18vPzkZGRgVOnTkEgEHCD7V566aV+ZwqxLIt79+5x/eO6urq4cQ4ikajf1w3W/fv3IZfLMWHChD5Hd+fk5CAyMhIpKSlYtmzZsNTQn5KSEuzduxcVFRVoamrSWwG0tLTg7bffRkFBATo6OrBgwQIkJiZi6tSpBq2TDD0KHkKeUVdXFwoLC7mZQmPGjOFWQoGBgU+cKfRoE1OVSoXnn38ejo6OEIlE/b7uWeqTy+VwcHCAl5eXXuicPn0aa9aswZdffomVK1cOyf/5NHJzc/HNN99gzpw5WLFihU7wsCwLf39/8Hg8fPjhhxAKhdi3bx/y8vJw9epVs10pjBYUPGZgz549yMzMxPXr18Hn8+Hv74+EhASdk+jamfOpqak6M+fNqe39YPT09KCoqAgZGRnIzs5Gb28vfv3rX2PZsmWQSqWwtrbu97UqlYq7HfdznbQHShs69vb2ervzAKCwsBDh4eH49NNP8cYbbzzT/zGUqOu7eRkZ223IsCouLkZ0dDS+/fZbnDlzBj09PQgKCtKZF/SnP/0JJ06cQFpaGoqLi9HY2IgVK1YYsWrTwuPxEBQUhCNHjqChoQHp6emwsbFBTEwMPDw8sG7dOpw8eVJvphDwUydtDw8PvPjiiwgICNDppC2Xy3Hjxg10dXUNuJYHDx6goqICzz33XJ+hU1xcjPDwcHz00Uf4zW9+M+iffThQ1/fRjVY8Zuj27duYMGECiouLsWDBAiiVSohEIqSkpHC3XK5fvw5vb2+Ul5fTeY5B6O3tRVlZGTdTqL29nZspFBQU9MRbRl1dXdxKSKlUQigUcmeF+Hx+n6/p7u6GXC6HUCiEj4+PXuiUlpbitddew/79+xEZGTliDr0+vuLp6enBlClT4OfnhyNHjsDGxgb79+/HO++8g6CgIOTn5xu3YDIotOIxQ0qlEgC4XmUVFRXo6enR2UarHfdQXl5ulBpHC0tLS8yfPx8HDhxAfX09zpw5A3d3d7z33ntwd3dHeHg4vv766z5nCmk7ac+dOxfz58+Hi4sLWltbuU7a9fX1OqvW7u5uVFRUwNbWts/QOX/+PF5//XX89a9/HVGh0xfq+j66UecCM6PRaLBp0yYEBARwp/ibm5thZWWF8ePH6zzX0dERzc3NRqhydLKwsICfnx/8/PyQkJCAqqoqpKenIyEhAevXr8fixYuxbNkyLF26VK8Fj7W1NVxdXeHq6sp10lYoFPjxxx9hY2MDBwcHKBSKfkOnoqICK1aswPbt27Fhw4YRHTpa1PV99KI/HcxMdHQ0rly5gtTUVGOXYtYsLCzwy1/+Eu+//z6uXr0KuVyOF154AYmJiXB3d8eKFSvw5Zdf9jlTSNtJWyKRYOHChZg0aRJu3boFtVqNe/fuoa6uDh0dHVxX5EuXLmHZsmWIi4tDbGysSYTOo+zs7CASibiu74be9k2GHgWPGYmJicHJkydRVFSkM3vFyckJ3d3d6Ojo0Hl+S0uL3ghkMvQYhsHMmTMRHx+PS5cuobq6GgsXLsQ//vEPTJ48GaGhofj000/R0tLSZ8/AhoYGjB8/HgsXLsSUKVOgVqvxySefYNq0aQgPD0dwcDA2bdqErVu3jqjQ6ezsRFVVFaqqqgD8v+v7jRs3AABpaWmQyWSoq6vD8ePHsWTJEur6PlqwZNTTaDRsdHQ06+LiwtbU1Ohd7+joYHk8Hpuens49dv36dRYAW15ebshSySM0Gg37ww8/sAkJCayfnx9raWnJzp8/n927dy9bU1PD3rx5k120aBGbnp7O3rt3j1WpVNxXS0sLu337dtbKyoq1trZmJ02axMbGxrL/+c9/2N7eXmP/aCzLsmxRURELQO9rzZo1LMuy7MGDB1lXV1eWx+OxYrGY/fOf/8w+ePDAuEWTIUG72szAhg0bkJKSguPHj+uc3bGzs+N2R0VFReH06dNITk6GUCjEW2+9BQAoKyszSs1EF8uyuHHjBjIzM5GZmYmysjLw+Xw899xzyMrK0ts2XVtbi5CQEKxevRo7duyATCZDeno6Tp8+jatXr+p9nkeIIVHwmIH+bq988cUXiIiIAPD/A6RfffWVzgFSutU28qhUKixZsgStra1wdnZGWVkZfH19uZlClpaWCA4OxmuvvYZ9+/bp7ALTaDS0K4wYHQUPISakt7cXISEh6OrqQm5uLgQCARQKBbKzs5GZmYlz586BYRi8+eab+OyzzyhkyIhEwUOIiUlNTcXSpUtha2ur8zjLsrhz5w527tyJDz/80GgjGAj5ORQ8hBBCDIrW4YQQQgyKgocQQohBUfAQQggxKAoeYjL27NmDuXPnwtbWFhMmTEBYWBi+//57ned88sknkEqlEAqFYBhGrxsDIcT4KHiIyRjIXCG1Wo3g4GBs27bNiJUSQp6EdrURk/X4XKFHyWQyLFq0CO3t7XRKn5ARhlY8JkAqlYJhGDAMwzVUfBYRERHc+2RnZw9Zfcby+FwhQohpoOAxEevWrUNTUxM3Q+dZHDx4EE1NTUNYlfH0NVeIEGIaaBCciRAIBIPum2ZnZwc7O7shqsi4tHOFSktLjV0KIeQp0YrHBMlkMjAMg/z8fEgkEvD5fLz88stQKBTIzc2Ft7c3hEIhwsPDoVarjV3ukOtvrhAhxDTQiseExcfH49ChQxAIBFi1ahVWrVoFa2trpKSkoLOzE8uXL0diYiLefvttY5c6JFiWxVtvvYWsrCzIZDJ4eHgYuyRCyDOg4DFhu3btQkBAAAAgMjIScXFx+PHHH+Hp6QkAWLlyJYqKikZN8ERHR3NzhWxtbdHc3AxAd65Qc3Mzmpub8cMPPwAAqqurYWtrC7FYTJsQCBkh6FabCfP19eW+d3R0hEAg4EJH+5hCoTBGacPi448/hlKphFQqhbOzM/d17Ngx7jlJSUmQSCRYt24dAGDBggWQSCTIyckxVtmEkMfQiseE8Xg87nuGYXT+rX1Mo9EYuqxhM5AjZ/Hx8YiPjx/+Ygghz4xWPIQQTklJCUJDQ+Hi4tLnea/Ozk7ExMTA1dUVfD4fM2bMQFJSknGKJSaLgocQwlGpVJg9ezYOHz7c5/XNmzcjLy8PR48exbVr17Bp0ybExMTQrUzyVOhWGyGEExISgpCQkH6vl5WVYc2aNZBKpQCAP/zhDzhy5AguXLiAV1991UBVElNHKx4TJJVKwbKsTg+yiIgIvU7M8fHxg2qxQ8jj/P39kZOTg4aGBrAsi6KiItTU1CAoKMjYpRETQsFjIj766COMGzcO1dXVz/we69evx7hx44awKmJuEhMTMWPGDLi6usLKygrBwcE4fPiwXpNWQp6EbrWZgH//+9+4f/8+AEAsFj/z++zYsQNbtmwBADg7Ow9JbcS8JCYm4ttvv0VOTg7c3NxQUlKC6OhouLi4YPHixcYuj5gIGotAiBHs2bMHmZmZuH79Ovh8Pvz9/ZGQkAAvLy8AwJ07d/Dee++hoKAAN27cgEgkQlhYGHbu3GmwfnsMwyArKwthYWEAgPv378POzg5ZWVlYunQp97y1a9fi1q1byMvLM0hdxPTRrTZCjODnhto1NjaisbERH3zwAa5cuYLk5GTk5eUhMjLSaDX39PSgp6cHFha6vzYsLS1H1XkxMvxoxUPICPCkoXZaaWlpWL16NVQqFcaMGZ675J2dnVy7IYlEgn379mHRokWwt7eHWCyGVCpFa2srDh06BDc3NxQXFyMqKgr79u1DVFTUsNRERh/6jIeQEWAgQ+2USiWEQuGwhQ4AyOVyLFq0iPv35s2bAQBr1qxBcnIyUlNTERcXhzfffBN37tyBm5sbdu/ejfXr1w9bTWT0oRUPIUam0Wjw6quvoqOjo9/5Qq2trZgzZw5Wr16N3bt3G7hCQoYWBQ8hRhYVFYXc3FyUlpb2OV/o7t27WLJkCezt7ZGTk6PXk48QU0O32ggxIu1Qu5KSkj5D5969ewgODoatrS2ysrIodMioQLvaCDEClmURExODrKwsnDt3rs+hdnfv3kVQUBCsrKyQk5ODsWPHGqFSQoYe3WojxAg2bNjADbXTnt0B/j/UThs6arUaWVlZsLGx4Z4jEolgaWlpjLIJGRIUPIQYAcMwfT7+xRdfICIiAjKZTGd32aPq6+vh7u4+jNURMrwoeAghhBgUfcZDCCHEoCh4CCGEGBQFDyGEEIOi4CGEEGJQFDyEEEIMioKHEEKIQVHwEEIIMSgKHkIIIQZFwUMIIcSgKHgIIYQYFAUPIYQQg6LgIYQQYlD/A2tplvsFOimLAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "BdAy2oLAuIie"
+      },
+      "id": "BdAy2oLAuIie",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Make Cart Bowl"
+      ],
+      "metadata": {
+        "id": "sX6T_OEhGcUw"
+      },
+      "id": "sX6T_OEhGcUw"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "bowl_pos = np.array([20, 20, 30])\n",
+        "radius = 1.0\n",
+        "diameter = 2.0\n",
+        "focus_pos= np.array([70, 50, 20])\n",
+        "num_points = 200\n",
+        "plot_bowl = True\n",
+        "make_cart_bowl(bowl_pos, radius, diameter, focus_pos, num_points, plot_bowl)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "4f_tlsxFGeFj",
+        "outputId": "f536f9b3-ab5d-4326-a28f-0ccd05d47d15"
+      },
+      "id": "4f_tlsxFGeFj",
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
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+              "        29.9598679 , 30.23222647, 29.65276326, 30.24370002, 29.96186023,\n",
+              "        29.75434916, 30.36867901, 29.65252721, 30.08502704, 30.18322065,\n",
+              "        29.57630583, 30.38691007, 29.79660808, 29.8361488 , 30.3891329 ,\n",
+              "        29.5132606 , 30.25196947, 30.04823117, 29.58585171, 30.48689652,\n",
+              "        29.61126347, 29.9900497 , 30.32245495, 29.43221468, 30.41857026,\n",
+              "        29.85603199, 29.68089284, 30.51648297, 29.44560153, 30.1848828 ,\n",
+              "        30.17507017, 29.43058657, 30.54753088, 29.64038038, 29.84868561,\n",
+              "        30.46105964, 29.33370737, 30.38492477, 29.96585128, 29.51597161,\n",
+              "        30.60890454, 29.43796126, 30.06479277, 30.32025509, 29.30046446,\n",
+              "        30.55372005, 29.72396095, 29.68154701, 30.58369309, 29.28373759,\n",
+              "        30.29637457, 30.10780998, 29.35833106, 30.65905324, 29.48471288,\n",
+              "        29.90717279, 30.46642302, 29.20599426, 30.50694937, 29.8495181 ,\n",
+              "        29.50558738, 30.67762786, 29.28439941, 30.16234922, 30.26596578,\n",
+              "        29.22215059, 30.66177132, 29.57946738, 29.72650461, 30.59858585,\n",
+              "        29.15491299, 30.41064879, 30.00436849, 29.33605093, 30.73301018,\n",
+              "        29.33508929, 29.99348357, 30.42529946, 29.11895473, 30.61501152,\n",
+              "        29.71381506, 29.53716144, 30.70398046, 29.15170573, 30.27089063,\n",
+              "        30.17515216, 29.18652051, 30.74316294, 29.43212586, 29.8018077 ,\n",
+              "        30.57181295, 29.05733808, 30.52006526, 29.87732316, 29.35316872,\n",
+              "        30.77239389, 29.19741869, 30.0962921 , 30.34818428, 29.06850833,\n",
+              "        30.70480296, 29.56887606, 29.60032135, 30.6930291 , 29.04267485,\n",
+              "        30.38146325, 30.05799351, 29.18762456, 30.79654853, 29.28969088,\n",
+              "        29.8975655 , 30.5100937 , 28.99096991, 30.61810517, 29.73616116,\n",
+              "        29.40232329, 30.77857292, 29.07694203, 30.20664349, 30.24293651,\n",
+              "        29.05203716, 30.77239902, 29.42298786, 29.6868699 , 30.64854702,\n",
+              "        28.95988837, 30.4865848 , 29.92285218, 29.22064872, 30.82069847,\n",
+              "        29.15870977, 30.0054335 , 30.41914672, 28.95570009, 30.69927426,\n",
+              "        29.58902104, 29.47704835, 30.75295365, 28.97800028, 30.31679333,\n",
+              "        30.11659447, 29.06690502, 30.81468927, 29.28331814, 29.78938753,\n",
+              "        30.57430354, 28.90517611, 30.5798356 , 29.77732719, 29.28081516,\n",
+              "        30.81508324, 29.04469989, 30.11784068, 30.3046081 , 28.95077057,\n",
+              "        30.75908981, 29.44323818, 29.5710225 , 30.69753552, 28.9039359 ,\n",
+              "        30.41984375, 29.97597356, 29.10995035, 30.82954662, 29.15613711,\n",
+              "        29.90076966, 30.47451128, 28.87940804, 30.65574586, 29.62859967,\n",
+              "        29.36300033, 30.78009645, 28.95218062, 30.22774382, 30.17234646,\n",
+              "        28.97455367, 30.79410535, 29.30550782, 29.67781887, 30.6151152 ,\n",
+              "        28.85703133, 30.50964693, 29.82785617, 29.17730664, 30.81590937,\n",
+              "        29.0468624 , 30.014091  , 30.35397606, 28.88240478, 30.70978872]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "duFaxqUyHODp"
+      },
+      "id": "duFaxqUyHODp",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Make Cart Sphere"
+      ],
+      "metadata": {
+        "id": "HHUKDKqiIEnu"
+      },
+      "id": "HHUKDKqiIEnu"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "radius=5.0\n",
+        "num_points = 100\n",
+        "center =np.array([1,1,8])\n",
+        "plot_sphere = False\n",
+        "sphere = make_cart_sphere(radius, num_points, center, plot_sphere)"
+      ],
+      "metadata": {
+        "id": "4kcF-AEpIGVi"
+      },
+      "id": "4kcF-AEpIGVi",
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from kwave.utils.data import scale_SI\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "[x_sc, scale, prefix, _] = scale_SI(np.max(sphere))\n",
+        "\n",
+        "cmap = plt.get_cmap('viridis', np.shape(sphere)[1])\n",
+        "\n",
+        "# create the figure\n",
+        "plt.figure()\n",
+        "ax = plt.axes(projection=\"3d\")\n",
+        "ax.scatter(sphere[0, :] * scale, sphere[1, :] * scale, sphere[2, :] * scale, marker='.', c=np.arange(np.shape(sphere)[1]), cmap=cmap, alpha=0.9, edgecolor=None)\n",
+        "ax.set_xlabel(f\"[{prefix} m]\")\n",
+        "ax.set_ylabel(f\"[{prefix} m]\")\n",
+        "ax.set_zlabel(f\"[{prefix} m]\")\n",
+        "ax.axis(\"auto\")\n",
+        "ax.grid()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 416
+        },
+        "id": "maW29nFuJQ9G",
+        "outputId": "01398046-97c9-431c-afd9-56eeb535a337"
+      },
+      "id": "maW29nFuJQ9G",
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Make Cart Circle"
+      ],
+      "metadata": {
+        "id": "LafnQxUBHOp7"
+      },
+      "id": "LafnQxUBHOp7"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "radiu = 5.0\n",
+        "num_points = 100\n",
+        "center = np.array([1.0, 1.0])\n",
+        "arc_angle = 2.0*np.pi\n",
+        "plot_circle = True\n",
+        "circle = make_cart_circle(radius, num_points, center, arc_angle, plot_circle=plot_circle)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 245
+        },
+        "id": "M3bK7ecGHTqL",
+        "outputId": "87769d78-1245-4496-ce6c-aea9a5ef691b"
+      },
+      "id": "M3bK7ecGHTqL",
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "BeartypeCallHintParamViolation",
+          "evalue": "Function kwave.utils.mapgen.make_cart_circle() parameter center_pos=\"array([1., 1.])\" violates type hint <class 'kwave.data.Vector'>, as <protocol \"numpy.ndarray\"> \"array([1., 1.])\" not instance of <protocol \"kwave.data.Vector\">.",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mBeartypeCallHintParamViolation\u001b[0m            Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-18-ec4bf22550f5>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0marc_angle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplot_circle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mcircle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_cart_circle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mradius\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_points\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcenter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marc_angle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot_circle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplot_circle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m<@beartype(kwave.utils.mapgen.make_cart_circle) at 0x7971dfbb27a0>\u001b[0m in \u001b[0;36mmake_cart_circle\u001b[0;34m(__beartype_get_violation, __beartype_conf, __beartype_object_495990208, __beartype_object_497678192, __beartype_check_meta, __beartype_func, *args, **kwargs)\u001b[0m\n",
+            "\u001b[0;31mBeartypeCallHintParamViolation\u001b[0m: Function kwave.utils.mapgen.make_cart_circle() parameter center_pos=\"array([1., 1.])\" violates type hint <class 'kwave.data.Vector'>, as <protocol \"numpy.ndarray\"> \"array([1., 1.])\" not instance of <protocol \"kwave.data.Vector\">."
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Cart Spherical Segment"
+      ],
+      "metadata": {
+        "id": "GT0UBekSC84F"
+      },
+      "id": "GT0UBekSC84F"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "bowl_pos = np.array([0.0, 0.0, 0.0])\n",
+        "focus_pos = np.array([0.0, 0.0, 1.0])\n",
+        "num_points_vec = [69, 70, 200, 1000]\n",
+        "radius = 65e-3\n",
+        "ap_diam1 = 30e-3\n",
+        "inner_diameter = 45e-3\n",
+        "outer_diameter = 60e-3\n",
+        "\n",
+        "# find position where to split the bowl\n",
+        "bowl_height1 = radius - np.sqrt(radius**2 - (ap_diam1 / 2)**2)\n",
+        "bowl_height2 = radius - np.sqrt(radius**2 - (inner_diameter / 2)**2)\n",
+        "\n",
+        "plot_bowl = True\n",
+        "\n",
+        "# loop over some different numbers of points\n",
+        "for points_ind in np.arange(0, 1, dtype=int):\n",
+        "\n",
+        "    num_points = num_points_vec[points_ind];\n",
+        "\n",
+        "    # make regular bowl\n",
+        "    bowl_points = make_cart_bowl(bowl_pos, radius, outer_diameter, focus_pos, num_points)\n",
+        "\n",
+        "    # split array\n",
+        "    bowl_points_ann1 = bowl_points[:, bowl_points[2, :] <= bowl_height1]\n",
+        "    bowl_points_ann2 = bowl_points[:, (bowl_points[2, :] <=  bowl_height2) & (bowl_points[2, :] > bowl_height1)]\n",
+        "    bowl_points_ann3 = bowl_points[:, bowl_points[2, :] > bowl_height2]\n",
+        "\n",
+        "    # count points\n",
+        "    num_points_ann1 = np.shape(bowl_points_ann1)[1]\n",
+        "    num_points_ann2 = np.shape(bowl_points_ann2)[1]\n",
+        "    num_points_ann3 = np.shape(bowl_points_ann3)[1]\n",
+        "\n",
+        "    num_points_inner = num_points_ann1 + num_points_ann2\n",
+        "    num_points = num_points_ann3\n",
+        "\n",
+        "    make_cart_spherical_segment(bowl_pos, radius, inner_diameter, outer_diameter, focus_pos, num_points, plot_bowl, num_points_inner)\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 431
+        },
+        "id": "Zc1rNWf6DAZp",
+        "outputId": "e0115f20-eeb6-47ea-e62e-a146f506d697"
+      },
+      "id": "Zc1rNWf6DAZp",
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "language_info": {
+      "name": "python"
+    },
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/kwave/utils/mapgen.py b/kwave/utils/mapgen.py
index ad5f6584..625bd19a 100644
--- a/kwave/utils/mapgen.py
+++ b/kwave/utils/mapgen.py
@@ -1,36 +1,34 @@
 import logging
-import math
 import warnings
-from math import floor
+from math import ceil, floor
 
 import matplotlib.pyplot as plt
 import numpy as np
-import scipy
 from beartype import beartype as typechecker
-from beartype.typing import List, Optional, Tuple, Union, cast
+from beartype.typing import List, Literal, Optional, Tuple, Union, cast
 from jaxtyping import Complex, Float, Int, Integer, Real
-from scipy import optimize
+from scipy.optimize import fmin
 from scipy.spatial.transform import Rotation
+from scipy.special import jv
 
 import kwave.utils.typing as kt
-from kwave.utils.math import compute_linear_transform, compute_rotation_between_vectors
-
-from ..data import Vector
-from .conversion import db2neper, neper2db
-from .data import scale_SI
-from .math import Rx, Ry, Rz, compute_linear_transform, cosd, sind
-from .matlab import ind2sub, matlab_assign, matlab_find, sub2ind
-from .matrix import max_nd
-from .tictoc import TicToc
+from kwave.data import Vector
+from kwave.utils.conversion import db2neper, neper2db
+from kwave.utils.data import scale_SI
+from kwave.utils.math import compute_linear_transform, cosd, sind
+from kwave.utils.matlab import ind2sub, matlab_assign, matlab_find, sub2ind
+from kwave.utils.matrix import max_nd
+from kwave.utils.tictoc import TicToc
 
 # GLOBALS
 # define literals (ref: http://www.wolframalpha.com/input/?i=golden+angle)
 GOLDEN_ANGLE = 2.39996322972865332223155550663361385312499901105811504
 PACKING_NUMBER = 7  # 2*pi
 
-
+@typechecker
 def make_cart_disc(
-    disc_pos: np.ndarray, radius: float, focus_pos: np.ndarray, num_points: int, plot_disc: bool = False, use_spiral: bool = False
+    disc_pos: np.ndarray, radius: float, focus_pos: np.ndarray, num_points: int, 
+    plot_disc: Optional[Union[bool, Literal[0, 1]]] = False, use_spiral: Optional[Union[bool, Literal[0, 1]]] = False
 ) -> np.ndarray:
     """
     Create evenly distributed Cartesian points covering a disc.
@@ -113,7 +111,7 @@ def make_concentric_circle_points(num_points: int, radius: float) -> Tuple[np.nd
     # specified disc
     if len(disc_pos) == 3:
         # check the focus position isn't coincident with the disc position
-        if all(disc_pos == focus_pos):
+        if np.all(np.isclose(np.array(disc_pos), np.array(focus_pos))):
             raise ValueError("The focus_pos must be different from the disc_pos.")
 
         # compute rotation matrix and apply
@@ -129,17 +127,23 @@ def make_concentric_circle_points(num_points: int, radius: float) -> Tuple[np.nd
         _, scale, prefix, unit = scale_SI(np.max(disc))
 
         # create the figure
+        fig = plt.figure()
+        cmap = plt.get_cmap('viridis', np.shape(disc)[1])
+        
         if len(disc_pos) == 2:
-            plt.figure()
-            plt.plot(disc[1, :] * scale, disc[0, :] * scale, ".")
-            plt.gca().invert_yaxis()
-            plt.xlabel(f"y-position [{prefix}m]")
-            plt.ylabel(f"x-position [{prefix}m]")
-            plt.axis("equal")
+            ax = fig.add_subplot(111)
+            ax.scatter(disc[1, :] * scale, disc[0, :] * scale, marker='.', 
+                     c=np.arange(np.shape(disc)[1]), cmap=cmap, alpha=0.9, edgecolor=None)
+            ax.invert_yaxis()
+            ax.xlabel(f"y-position [{prefix}m]")
+            ax.ylabel(f"x-position [{prefix}m]")
+            ax.axis("equal")
+            ax.grid(True)
+            ax.box(True)
         else:
-            fig = plt.figure()
             ax = fig.add_subplot(111, projection="3d")
-            ax.plot3D(disc[0, :] * scale, disc[1, :] * scale, disc[2, :] * scale, ".")
+            ax.scatter(disc[0, :] * scale, disc[1, :] * scale, disc[2, :] * scale, marker='.', 
+                      c=np.arange(np.shape(disc)[1]), cmap=cmap, alpha=0.9, edgecolor=None)
             ax.set_xlabel(f"[{prefix}m]")
             ax.set_ylabel(f"[{prefix}m]")
             ax.set_zlabel(f"[{prefix}m]")
@@ -188,9 +192,8 @@ def make_cart_bowl(
 
     # check for infinite radius of curvature, and call makeCartDisc instead
     if np.isinf(radius):
-        # bowl = make_cart_disc(bowl_pos, diameter / 2, focus_pos, num_points, plot_bowl)
-        # return bowl
-        raise NotImplementedError("make_cart_disc")
+        bowl = make_cart_disc(bowl_pos, diameter / 2, focus_pos, num_points, plot_bowl)
+        return bowl
 
     # compute arc angle from chord (ref: https://en.wikipedia.org/wiki/Chord_(geometry))
     varphi_max = np.arcsin(diameter / (2 * radius))
@@ -217,11 +220,12 @@ def make_cart_bowl(
     if plot_bowl is True:
         # select suitable axis scaling factor
         _, scale, prefix, unit = scale_SI(np.max(bowl))
-
         # create the figure
         fig = plt.figure()
+        cmap = plt.get_cmap('viridis', np.shape(bowl)[1])
         ax = fig.add_subplot(111, projection="3d")
-        ax.scatter(bowl[0, :] * scale, bowl[1, :] * scale, bowl[2, :] * scale)
+        ax.scatter(bowl[0, :] * scale, bowl[1, :] * scale, bowl[2, :] * scale, marker='.', 
+                   c=np.arange(np.shape(bowl)[1]), cmap=cmap, alpha=0.9, edgecolor=None)
         ax.set_xlabel("[" + prefix + unit + "]")
         ax.set_ylabel("[" + prefix + unit + "]")
         ax.set_zlabel("[" + prefix + unit + "]")
@@ -315,7 +319,7 @@ def abs_func(trial_vals):
 
         return absorption_error
 
-    a0_np_fit, y_fit = optimize.fmin(abs_func, [a0_np, y])
+    a0_np_fit, y_fit = fmin(abs_func, [a0_np, y])
 
     a0_fit = neper2db(a0_np_fit, y_fit)
 
@@ -549,17 +553,28 @@ def make_ball(
 
     # plot results
     if plot_ball:
-        raise NotImplementedError
-        # voxelPlot(double(ball))
+        _, scale, prefix, _ = scale_SI(np.max(ball))
+        fig = plt.figure()
+        ax = fig.add_subplot(111, projection="3d")
+        ax.scatter(ball[0] * scale, ball[1] * scale, ball[2] * scale)
+        ax.set_xlabel("[" + prefix + "m]")
+        ax.set_ylabel("[" + prefix + "m]")
+        ax.set_zlabel("[" + prefix + "m]")
+        ax.set_box_aspect([1, 1, 1])
+        plt.grid(True)
+        plt.show()
     return ball
 
 
 @typechecker
 def make_cart_sphere(
-    radius: Union[float, int], num_points: int, center_pos: Vector = Vector([0, 0, 0]), plot_sphere: bool = False
+    radius: Union[float, int], 
+    num_points: int, 
+    center_pos: Optional[Union[Real[kt.ScalarLike, "3"], List, Int[np.ndarray, "3"], Float[np.ndarray, "3"]]] = np.zeros((3,)), 
+    plot_sphere: bool = False
 ) -> Float[np.ndarray, "3 NumPoints"]:
     """
-    Cart_sphere creates a set of points in Cartesian coordinates defining a sphere.
+    Creates a set of points in Cartesian coordinates defining a sphere.
 
     Args:
         radius: the radius of the sphere.
@@ -592,12 +607,15 @@ def make_cart_sphere(
     if plot_sphere:
         # select suitable axis scaling factor
         [x_sc, scale, prefix, _] = scale_SI(np.max(sphere))
-
+        
+        cmap = plt.get_cmap('viridis', np.shape(sphere)[1])
+        
         # create the figure
         plt.figure()
-        plt.style.use("seaborn-poster")
+        # plt.style.use("seaborn-poster")
         ax = plt.axes(projection="3d")
-        ax.plot3D(sphere[0, :] * scale, sphere[1, :] * scale, sphere[2, :] * scale, ".")
+        ax.scatter(sphere[0, :] * scale, sphere[1, :] * scale, sphere[2, :] * scale, marker='.', 
+                   c=np.arange(np.shape(sphere)[1]), cmap=cmap, alpha=0.9, edgecolor=None)
         ax.set_xlabel(f"[{prefix} m]")
         ax.set_ylabel(f"[{prefix} m]")
         ax.set_zlabel(f"[{prefix} m]")
@@ -607,7 +625,7 @@ def make_cart_sphere(
 
     return sphere.squeeze()
 
-
+@typechecker
 def make_cart_circle(
     radius: float, num_points: int, center_pos: Vector = Vector([0, 0]), arc_angle: float = 2 * np.pi, plot_circle: bool = False
 ) -> Float[np.ndarray, "2 NumPoints"]:
@@ -715,7 +733,12 @@ def make_disc(grid_size: Vector, center: Vector, radius, plot_disc=False) -> kt.
 
     # create the figure
     if plot_disc:
-        raise NotImplementedError
+        _, ax = plt.subplots(1, 1)
+        ax.imshow(disc)
+        ax.set_aspect('auto', adjustable='box')
+        ax.yaxis.set_inverted(True)
+        plt.show()
+
     return disc
 
 
@@ -2333,8 +2356,8 @@ def make_sphere(
         and optional flags to plot the sphere and/or return a binary mask.
 
     Args:
-        grid_size: The size of the grid (assumed to be cubic).
-        radius: The radius of the sphere.
+        grid_size: The size of the grid (assumed to be three dimensional).
+        radius: The radius of the sphere in grid points
         plot_sphere: Whether to plot the sphere (default: False).
         binary: Whether to return a binary mask (default: False).
 
@@ -2415,7 +2438,15 @@ def make_sphere(
 
     # plot results
     if plot_sphere:
-        raise NotImplementedError
+        # create the figure: this is a binary mask.
+        fig = plt.figure()
+        cmap = plt.get_cmap('viridis', np.shape(sphere)[0])
+        ax = fig.add_subplot(111, projection="3d")
+        ax.scatter(sphere[0], sphere[1], sphere[2], marker='s', c=np.arange(np.shape(sphere)[0]), cmap=cmap, alpha=0.9, edgecolor=None)
+        ax.set_box_aspect([1, 1, 1])
+        plt.grid(True)
+        plt.show()
+        
     return sphere
 
 
@@ -2444,7 +2475,6 @@ def make_spherical_section(
 
     Raises:
         ValueError: If the width is not an odd number.
-        NotImplementedError: Plotting not currently supported.
     """
     use_spherical_sections = True
 
@@ -2557,7 +2587,13 @@ def make_spherical_section(
 
     # plot if required
     if plot_section:
-        raise NotImplementedError
+        # create the figure
+        fig = plt.figure()
+        ax = fig.add_subplot(111, projection="3d")
+        ax.scatter(ss[0], ss[1], ss[2], marker='s', c='black', alpha=0.9)
+        ax.set_box_aspect([1, 1, 1])
+        plt.grid(True)
+        plt.show()
 
     return ss, dist_map
 
@@ -2587,8 +2623,8 @@ def make_cart_rect(
     """
 
     # Find number of points in along each axis
-    npts_x = math.ceil(np.sqrt(num_points * Lx / Ly))
-    npts_y = math.ceil(num_points / npts_x)
+    npts_x = ceil(np.sqrt(num_points * Lx / Ly))
+    npts_y = ceil(num_points / npts_x)
 
     # Recalculate the true number of points
     num_points = npts_x * npts_y
@@ -2639,6 +2675,20 @@ def make_cart_rect(
     # Shift the rectangle to the appropriate centre
     rect = p0 + np.expand_dims(np.array(rect_pos), axis=1)
 
+    if plot_rect:
+        # create the figure
+        fig = plt.figure()
+        cmap = plt.get_cmap('viridis', np.shape(rect)[1])
+        if len(rect_pos) == 3:
+            ax = fig.add_subplot(111, projection="3d")
+            ax.scatter(rect[0], rect[1], rect[2], marker='s', c=np.arange(np.shape(rect)[1]), cmap=cmap, alpha=0.9, edgecolor=None)
+        if len(rect_pos) == 2:
+            ax = fig.add_subplot(111)
+            ax.scatter(rect[1, :], rect[0, :], marker='s', c=np.arange(np.shape(rect)[1]), cmap=cmap, alpha=0.9, edgecolor=None)
+            ax.invert_yaxis()
+        plt.grid(True)
+        plt.show()
+
     return rect
 
 
@@ -2724,7 +2774,7 @@ def calculate_lateral_pressure() -> Float[np.ndarray, "N"]:
         Z = k * lateral_positions * diameter / (2 * radius)
         # TODO: this should work
         # assert np.all(Z) > 0, 'Z must be greater than 0'
-        lateral_pressure = 2.0 * density * sound_speed * velocity * k * h * scipy.special.jv(1, Z) / Z
+        lateral_pressure = 2.0 * density * sound_speed * velocity * k * h * jv(1, Z) / Z
 
         # replace origin with limit
         lateral_pressure[lateral_positions == 0] = density * sound_speed * velocity * k * h
@@ -2905,7 +2955,7 @@ def make_cart_arc(
     plot_arc: bool = False,
 ) -> Float[np.ndarray, "2 NumPoints"]:
     """
-    make_cart_arc creates a 2 x num_points array of the Cartesian
+    Creates a 2 x num_points array of the Cartesian
     coordinates of points evenly distributed over an arc. The midpoint of
     the arc is set by arc_pos. The orientation of the arc is set by
     focus_pos, which corresponds to any point on the axis of the arc
@@ -2966,7 +3016,9 @@ def make_cart_arc(
 
         # Create the figure
         plt.figure()
-        plt.plot(arc[1, :] * scale, arc[0, :] * scale, "b.")
+        cmap = plt.get_cmap('viridis', np.shape(arc)[1])
+        plt.scatter(arc[1, :] * scale, arc[0, :] * scale, marker='s', c=np.arange(np.shape(arc)[1]), 
+                    cmap=cmap, alpha=0.9, edgecolor=None)
         plt.gca().invert_yaxis()
         plt.xlabel(f"y-position [{prefix}m]")
         plt.ylabel(f"x-position [{prefix}m]")
diff --git a/kwave/utils/math.py b/kwave/utils/math.py
index 9a6edfc4..215e8ac1 100644
--- a/kwave/utils/math.py
+++ b/kwave/utils/math.py
@@ -383,7 +383,7 @@ def gaussian(
 
 def _compute_direction(start_pos: np.ndarray, end_pos: np.ndarray) -> Tuple[np.ndarray, float]:
     """Compute normalized direction vector and magnitude between two points."""
-    direction = end_pos - start_pos
+    direction = np.asarray(end_pos) - np.asarray(start_pos)
     magnitude = np.linalg.norm(direction)
     direction = direction / magnitude
     return direction, magnitude
@@ -457,11 +457,12 @@ def compute_linear_transform(pos1, pos2, offset=None):
     Returns:
         Tuple containing:
         - 3x3 rotation matrix
+        - offset position
 
     """
-    rot_mat, direction = compute_rotation_between_vectors(pos1, pos2)
+    rot_mat, direction = compute_rotation_between_vectors(np.asarray(pos1), np.asarray(pos2))
     if offset is not None:
-        offset_pos = pos1 + offset * direction
+        offset_pos = np.asarray(pos1) + offset * direction
     else:
         offset_pos = 0
     return rot_mat, offset_pos