diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..94a25f7
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
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+  </component>
+</project>
\ No newline at end of file
diff --git a/consuming_channnel_sweep.csv b/consuming_channnel_sweep.csv
new file mode 100644
index 0000000..5973c01
--- /dev/null
+++ b/consuming_channnel_sweep.csv
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diff --git a/lut_channel_filter_sweep.csv b/lut_channel_filter_sweep.csv
new file mode 100644
index 0000000..6d10db8
--- /dev/null
+++ b/lut_channel_filter_sweep.csv
@@ -0,0 +1,341 @@
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diff --git a/lut_channnel_sweep.csv b/lut_channnel_sweep.csv
new file mode 100644
index 0000000..09dc368
--- /dev/null
+++ b/lut_channnel_sweep.csv
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diff --git a/lut_filter_sweep.csv b/lut_filter_sweep.csv
new file mode 100644
index 0000000..ed6cd17
--- /dev/null
+++ b/lut_filter_sweep.csv
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diff --git a/notebook.ipynb b/notebook.ipynb
index 7dbbc13..fbe24a7 100644
--- a/notebook.ipynb
+++ b/notebook.ipynb
@@ -1,8 +1,9 @@
 {
  "cells": [
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "8a77807f92f26ee",
+   "metadata": {},
    "source": [
     "# This is a sample Jupyter Notebook\n",
     "\n",
@@ -13,17 +14,17 @@
     "\n",
     "To learn more about Jupyter Notebooks in PyCharm, see [help](https://www.jetbrains.com/help/pycharm/ipython-notebook-support.html).\n",
     "For an overview of PyCharm, go to Help -> Learn IDE features or refer to [our documentation](https://www.jetbrains.com/help/pycharm/getting-started.html)."
-   ],
-   "id": "8a77807f92f26ee"
+   ]
   },
   {
+   "cell_type": "code",
+   "id": "fbc121e30a2defb3",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-15T09:41:42.374585Z",
-     "start_time": "2025-08-15T09:41:42.366946Z"
+     "end_time": "2025-08-28T07:57:01.453882Z",
+     "start_time": "2025-08-28T07:57:01.312372Z"
     }
    },
-   "cell_type": "code",
    "source": [
     "# import matplotlib.pyplot as plt\n",
     "import csv\n",
@@ -32,40 +33,52 @@
     "import numpy as np\n",
     "from jupyter_client.connect import channel_socket_types\n",
     "from prompt_toolkit.key_binding.bindings.named_commands import uppercase_word\n",
+    "import pandas as pd\n",
+    "\n",
     "# plt.rcParams['figure.figsize'] = [25, 15]"
    ],
-   "id": "fbc121e30a2defb3",
    "outputs": [],
-   "execution_count": 87
+   "execution_count": 1
   },
   {
+   "cell_type": "code",
+   "id": "a96ea0301fc7692a",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-15T13:50:51.376122Z",
-     "start_time": "2025-08-15T13:50:51.365041Z"
+     "end_time": "2025-08-28T07:57:02.356279Z",
+     "start_time": "2025-08-28T07:57:02.349490Z"
     }
    },
-   "cell_type": "code",
    "source": [
-    "with open('/home/aoster/some.csv') as csvfile:\n",
+    "with open('./lut_channnel_sweep.csv') as csvfile:\n",
     "    spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')\n",
     "    rows = list(spamreader)\n",
     "    channels = [int(row[0]) for row in rows]\n",
     "    channel_meas = [float(row[1]) for row in rows]\n",
-    "    # print(rows)"
+    "with open('./lut_filter_sweep.csv') as csvfile:\n",
+    "    spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')\n",
+    "    rows = list(spamreader)\n",
+    "    filters = [int(row[0]) for row in rows]\n",
+    "    filter_meas = [float(row[1]) for row in rows]\n",
+    "with open('./lut_channel_filter_sweep.csv') as csvfile:\n",
+    "    spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')\n",
+    "    rows = list(spamreader)\n",
+    "    channel_cf = [int(row[0]) for row in rows]\n",
+    "    filters_cf = [int(row[1]) for row in rows]\n",
+    "    channel_filter_meas = [float(row[2]) for row in rows]\n"
    ],
-   "id": "a96ea0301fc7692a",
    "outputs": [],
-   "execution_count": 219
+   "execution_count": 2
   },
   {
+   "cell_type": "code",
+   "id": "79eaf4a295ca7143",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-15T14:44:59.021272Z",
-     "start_time": "2025-08-15T14:44:59.008370Z"
+     "end_time": "2025-08-25T07:26:41.650060Z",
+     "start_time": "2025-08-25T07:26:41.646036Z"
     }
    },
-   "cell_type": "code",
    "source": [
     "def calc_upper(c: int, m: float, b: float) -> float:\n",
     "    return np.floor(c / 32) * 32 * m + b\n",
@@ -73,40 +86,55 @@
     "    return np.ceil(c / 32) * 32 * m + b\n",
     "def calc_mean(c:int , m_u: float, b_u: float, m_l: float, b_l: float) -> float:\n",
     "    return (calc_upper(c, m_u, b_u) + calc_lower(c, m_l, b_l)) / 2\n",
-    "def calc_sine(c:int , m_u: float, b_u: float, m_l: float, b_l: float, amp_factor: float, freq_factor: float) -> float:\n",
-    "    # return np.sin(c%32 / np.pi * 2.5) * (.05 * np.ceil(c / 32)) + calc_mean(c, m_u, b_u, m_l, b_l)\n",
-    "    return np.sin(c%32 / np.pi * freq_factor) * (amp_factor * np.ceil(c / 32)) + calc_mean(c, m_u, b_u, m_l, b_l)\n"
+    "def calc_rect(c:int , m_u: float, b_u: float, m_l: float, b_l: float) -> float:\n",
+    "    if ((c - 1) % 8) < 4:\n",
+    "        return calc_upper(c, m_u, b_u)\n",
+    "    else:\n",
+    "        return calc_lower(c, m_l, b_l)\n"
    ],
-   "id": "79eaf4a295ca7143",
    "outputs": [],
-   "execution_count": 239
+   "execution_count": 12
   },
   {
+   "cell_type": "code",
+   "id": "b098c375bdbc1389",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-15T14:44:59.201138Z",
-     "start_time": "2025-08-15T14:44:59.193282Z"
+     "end_time": "2025-08-25T07:26:41.842019Z",
+     "start_time": "2025-08-25T07:26:41.839325Z"
     }
    },
-   "cell_type": "code",
    "source": [
     "def lin_interpol(x0:float, x1:float, y0:float, y1:float) -> (float, float):\n",
     "    m = (y1 - y0) / (x1 - x0)\n",
     "    b = y1 - m * x1\n",
     "    return m, b"
    ],
-   "id": "b098c375bdbc1389",
    "outputs": [],
-   "execution_count": 240
+   "execution_count": 13
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-15T14:52:54.110688Z",
-     "start_time": "2025-08-15T14:52:54.070141Z"
+     "end_time": "2025-08-25T07:26:42.022595Z",
+     "start_time": "2025-08-25T07:26:42.017289Z"
     }
    },
    "cell_type": "code",
+   "source": "",
+   "id": "c7abf7a2802a93b8",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "id": "e93f7a254e1da4d9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-25T09:35:08.774152Z",
+     "start_time": "2025-08-25T09:35:08.712147Z"
+    }
+   },
    "source": [
     "first_order_approx = []\n",
     "second_order_approx = []\n",
@@ -117,56 +145,48 @@
     "# upper_sampled_channels = [1, 3, 5, 9, 15, 33, 65, 129, 257, 513, 993]\n",
     "upper_sampled_channels = [7, 993]\n",
     "lower_sampled_channels = [1, 991]\n",
-    "# print(len(upper_sampled_channels))\n",
+    "# upper_sampled_channels = [7, 515]\n",
+    "# lower_sampled_channels = [1, 529]\n",
+    "# # print(len(upper_sampled_channels))\n",
     "\n",
     "upper_sampled_channel_meas = [channel_meas[channels.index(c)] for c in upper_sampled_channels]\n",
     "lower_sampled_channel_meas = [channel_meas[channels.index(c)] for c in lower_sampled_channels]\n",
     "\n",
-    "upper_m = (upper_sampled_channel_meas[1] - upper_sampled_channel_meas[0]) / (upper_sampled_channels[1] - upper_sampled_channels[0])\n",
-    "upper_b = upper_sampled_channel_meas[1] - upper_m * upper_sampled_channels[1]\n",
-    "lower_m = (lower_sampled_channel_meas[1] - lower_sampled_channel_meas[0]) / (lower_sampled_channels[1] - lower_sampled_channels[0])\n",
-    "lower_b = lower_sampled_channel_meas[1] - lower_m * lower_sampled_channels[1]\n",
-    "\n",
-    "print(upper_m, upper_b)\n",
     "upper_m, upper_b = lin_interpol( upper_sampled_channels[0], upper_sampled_channels[1], upper_sampled_channel_meas[0], upper_sampled_channel_meas[1])\n",
     "lower_m, lower_b = lin_interpol(lower_sampled_channels[0], lower_sampled_channels[1], lower_sampled_channel_meas[0], lower_sampled_channel_meas[1])\n",
-    "print(upper_m, upper_b)\n",
     "\n",
     "\n",
-    "r_c = [c / 10 for c in range(1, 10240)]\n",
+    "# r_c = [c / 10 for c in range(1, 10240)]\n",
     "r_c = channels\n",
-    "# print(r_c)\n",
-    "r_v_upper = [calc_upper(c, upper_m, upper_b) for c in r_c]\n",
     "\n",
-    "# print(r_c)\n",
+    "r_v_upper = [calc_upper(c, upper_m, upper_b) for c in r_c]\n",
     "r_v_lower = [calc_lower(c, lower_m, lower_b) for c in r_c]\n",
     "r_v_mean = [calc_mean(c,upper_m, upper_b, lower_m, lower_b) for c in r_c]\n",
-    "\n",
-    "# r_v_mean = [np.mean([u, l]) for (u, l) in zip(r_v_upper, r_v_lower)]\n",
-    "\n",
-    "r_v_sine = [calc_sine(c, upper_m, upper_b, lower_m, lower_b, 0.05, 2.5) for c in r_c]\n",
+    "r_v_rect = [calc_rect(c, upper_m, upper_b, lower_m, lower_b) for c in r_c]\n",
     "\n",
     "err_upper = [np.abs((approx - meas) / meas) for approx, meas in zip(r_v_upper, channel_meas)]\n",
     "err_lower = [np.abs((approx - meas) / meas) for approx, meas in zip(r_v_lower, channel_meas)]\n",
     "err_mean = [np.abs((approx - meas) / meas) for approx, meas in zip(r_v_mean, channel_meas)]\n",
-    "err_sine = [np.abs((approx - meas) / meas) for approx, meas in zip(r_v_sine, channel_meas)]\n",
+    "err_rect = [np.abs((approx - meas) / meas) for approx, meas in zip(r_v_rect, channel_meas)]\n",
+    "\n",
     "print(np.mean(err_sine))\n",
-    "# plt.plot(channels, channel_meas)\n",
-    "# plt.plot(channels, approx)\n",
-    "# plt.show()\n",
+    "print(np.mean(err_rect))\n",
     "\n",
     "fig = go.Figure()\n",
-    "fig.add_trace(go.Scatter(x=channels, y=channel_meas, name=\"Channel Measurements\"))\n",
-    "# fig.add_trace(go.Scatter(x=list(range(1024)), y=[c * upper_m + upper_b for c in range(1024)], name=\"Upper Sampled Channels\"))\n",
-    "# fig.add_trace(go.Scatter(x=list(range(1024)), y=[c * lower_m + lower_b for c in range(1024)], name=\"Lower Sampled Channels\"))\n",
-    "# fig.add_trace(go.Scatter(x=r_c, y=r_v_upper, name=\"Upper_steps\"))\n",
-    "# fig.add_trace(go.Scatter(x=r_c, y=r_v_lower, name=\"Lower_steps\"))\n",
-    "# fig.add_trace(go.Scatter(x=r_c, y=r_v_mean, name=\"Mean Steps\"))\n",
-    "fig.add_trace(go.Scatter(x=r_c, y=r_v_sine, name=\"Sine Steps\"))\n",
-    "fig.add_trace(go.Scatter(x=r_c, y=err_upper, name=\"Upper Errors\"))\n",
-    "fig.add_trace(go.Scatter(x=r_c, y=err_lower, name=\"Lower Errors\"))\n",
-    "fig.add_trace(go.Scatter(x=r_c, y=err_mean, name=\"Mean Errors\"))\n",
-    "fig.add_trace(go.Scatter(x=r_c, y=err_sine, name=\"Sine Errors\"))\n",
+    "fig.add_trace(go.Scatter(x=channels, y=channel_meas, name=\"Channel Measurements\", mode=\"markers\"))\n",
+    "fig.add_trace(go.Scatter(x=filters, y=filter_meas, name=\"Filter Measurements\", mode=\"markers\"))\n",
+    "\n",
+    "fig.add_trace(go.Scatter(x=list(range(1024)), y=[c * upper_m + upper_b for c in range(1024)], name=\"Upper Sampled Channels\"))\n",
+    "fig.add_trace(go.Scatter(x=list(range(1024)), y=[c * lower_m + lower_b for c in range(1024)], name=\"Lower Sampled Channels\"))\n",
+    "# fig.add_trace(go.Scatter(x=r_c, y=r_v_upper, name=\"Upper_steps\", mode=\"markers\"))\n",
+    "# fig.add_trace(go.Scatter(x=r_c, y=r_v_lower, name=\"Lower_steps\", mode=\"markers\"))\n",
+    "fig.add_trace(go.Scatter(x=r_c, y=r_v_mean, name=\"Mean Steps\"))\n",
+    "fig.add_trace(go.Scatter(x=r_c, y=r_v_rect, name=\"Rect Steps\"))\n",
+    "\n",
+    "# fig.add_trace(go.Scatter(x=r_c, y=err_upper, name=\"Upper Errors\"))\n",
+    "# fig.add_trace(go.Scatter(x=r_c, y=err_lower, name=\"Lower Errors\"))\n",
+    "# fig.add_trace(go.Scatter(x=r_c, y=err_mean, name=\"Mean Errors\"))\n",
+    "fig.add_trace(go.Scatter(x=r_c, y=err_rect, name=\"Rect Errors\", mode=\"markers\"))\n",
     "\n",
     "fig.update_layout(\n",
     "    autosize=False,\n",
@@ -181,15 +201,13 @@
     "    # ),\n",
     ")\n"
    ],
-   "id": "e93f7a254e1da4d9",
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.034859607127407585 1.4743395442270284\n",
-      "0.034859607127407585 1.4743395442270284\n",
-      "0.028253851455235686\n"
+      "0.028252145094345097\n",
+      "0.016604167625824784\n"
      ]
     },
     {
@@ -197,6194 +215,13607 @@
       "application/vnd.plotly.v1+json": {
        "data": [
         {
+         "mode": "markers",
          "name": "Channel Measurements",
          "x": [
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      "output_type": "display_data"
     }
    ],
-   "execution_count": 248
+   "execution_count": 19
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-25T09:35:08.874926Z",
+     "start_time": "2025-08-25T09:35:08.861215Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "with open('./lut_filter_sweep.csv') as csvfile:\n",
+    "    spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')\n",
+    "    rows = list(spamreader)\n",
+    "    filters = [int(row[0]) for row in rows]\n",
+    "    filter_meas = [float(row[1]) for row in rows]\n",
+    "fig = go.Figure()\n",
+    "fig.add_trace(go.Scatter(x=filters, y=filter_meas, name=\"Filter Measurements\"))\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    autosize=False,\n",
+    "    width=2000,\n",
+    "    height=1000,\n",
+    "    # margin=dict(\n",
+    "    #     l=50,\n",
+    "    #     r=50,\n",
+    "    #     b=100,\n",
+    "    #     t=100,\n",
+    "    #     pad=4\n",
+    "    # ),\n",
+    ")"
+   ],
+   "id": "9a653659b7f067cf",
+   "outputs": [
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+   "execution_count": 20
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+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-25T07:21:43.266040618Z",
+     "start_time": "2025-08-22T09:23:29.481329Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "with open('./consuming_channnel_sweep.csv') as csvfile:\n",
+    "    spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')\n",
+    "    rows = list(spamreader)\n",
+    "    filters = [int(row[0]) for row in rows]\n",
+    "    filter_meas = [float(row[1]) for row in rows]\n",
+    "fig = go.Figure()\n",
+    "fig.add_trace(go.Scatter(x=filters, y=filter_meas, name=\"Filter Measurements\"))\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    autosize=False,\n",
+    "    width=2000,\n",
+    "    height=1000,\n",
+    "    # margin=dict(\n",
+    "    #     l=50,\n",
+    "    #     r=50,\n",
+    "    #     b=100,\n",
+    "    #     t=100,\n",
+    "    #     pad=4\n",
+    "    # ),\n",
+    ")"
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+   "id": "361ee9ff66d1b6e5",
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+   "metadata": {
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+     "start_time": "2025-08-28T08:53:12.872389Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "\n",
+    "# Read data from a csv\n",
+    "df = pd.read_csv('./lut_channel_filter_sweep.csv', usecols=[\"channels\", \"filters\", \"ms\"])\n",
+    "split_df = df.groupby('channels')\n",
+    "fig = go.Figure()\n",
+    "\n",
+    "for category, category_df in split_df:\n",
+    "    fig.add_trace(go.Scatter(\n",
+    "        # x=category_df['channels'],\n",
+    "        x=category_df['filters'],\n",
+    "        y=category_df['ms'],\n",
+    "        # mode='markers',\n",
+    "        marker=dict(size=7),\n",
+    "        name=f\"channel {category}\",\n",
+    "        mode='markers+lines',\n",
+    "        line=dict(\n",
+    "            dash='dash',\n",
+    "            width=.5\n",
+    "        )\n",
+    "    ))\n",
+    "\n",
+    "# Customize the plot\n",
+    "fig.update_layout(\n",
+    "    scene=dict(\n",
+    "        xaxis_title='channels',\n",
+    "        yaxis_title='filters',\n",
+    "        zaxis_title='ms'\n",
+    "    ),\n",
+    "    width=1600,\n",
+    "    height=1200,\n",
+    "        template='plotly_white',\n",
+    ")\n",
+    "fig.layout.scene.camera.projection.type = \"orthographic\"\n",
+    "# Display the plot\n",
+    "fig.show()\n",
+    "fig = go.Figure()\n",
+    "split_df = df.groupby('filters')\n",
+    "\n",
+    "for category, category_df in split_df:\n",
+    "    fig.add_trace(go.Scatter(\n",
+    "        # x=category_df['channels'],\n",
+    "        x=category_df['channels'],\n",
+    "        y=category_df['ms'],\n",
+    "        # mode='markers',\n",
+    "        marker=dict(size=7),\n",
+    "        name=f\"filter {category}\",\n",
+    "        mode='markers+lines',\n",
+    "        line=dict(\n",
+    "            dash='dash',\n",
+    "            width=.5\n",
+    "        )\n",
+    "    ))\n",
+    "\n",
+    "# Customize the plot\n",
+    "fig.update_layout(\n",
+    "    scene=dict(\n",
+    "        xaxis_title='channels',\n",
+    "        yaxis_title='filters',\n",
+    "        zaxis_title='ms'\n",
+    "    ),\n",
+    "    width=1600,\n",
+    "    height=1200,\n",
+    "        template='plotly_white',\n",
+    ")\n",
+    "fig.layout.scene.camera.projection.type = \"orthographic\"\n",
+    "# Display the plot\n",
+    "fig.show()\n",
+    "fig = go.Figure()\n",
+    "\n",
+    "for category, category_df in split_df:\n",
+    "    fig.add_trace(go.Scatter3d(\n",
+    "        x=category_df['channels'],\n",
+    "        y=category_df['filters'],\n",
+    "        z=category_df['ms'],\n",
+    "        # mode='markers',\n",
+    "        marker=dict(size=7),\n",
+    "        name=f\"channel {category}\",\n",
+    "        mode='markers+lines',\n",
+    "        line=dict(\n",
+    "            dash='dash',\n",
+    "            width=.5\n",
+    "        )\n",
+    "    ))\n",
+    "\n",
+    "# Customize the plot\n",
+    "fig.update_layout(\n",
+    "    scene=dict(\n",
+    "        xaxis_title='channels',\n",
+    "        yaxis_title='filters',\n",
+    "        zaxis_title='ms'\n",
+    "    ),\n",
+    "    width=1600,\n",
+    "    height=1200,\n",
+    "        template='plotly_white',\n",
+    ")\n",
+    "fig.layout.scene.camera.projection.type = \"orthographic\"\n",
+    "# Display the plot\n",
+    "fig.show()\n"
+   ],
+   "id": "51ca081aacbae203",
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "data": [
+        {
+         "line": {
+          "dash": "dash",
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+         "marker": {
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+         },
+         "mode": "markers+lines",
+         "name": "channel 120",
+         "x": {
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+         "y": {
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+           "ticks": "",
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+          "yaxis": {
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+           "gridcolor": "#EBF0F8",
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+           "ticks": "",
+           "title": {
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+           "zerolinecolor": "#EBF0F8",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "scene": {
+         "xaxis": {
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+         },
+         "yaxis": {
+          "title": {
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+          }
+         },
+         "zaxis": {
+          "title": {
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+         },
+         "camera": {
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+         }
+        },
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+        "height": 1200
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+       "config": {
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+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+   "execution_count": 32
   },
   {
    "metadata": {},
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    "outputs": [],
    "execution_count": null,
    "source": "",
-   "id": "272d06ff782a5546"
+   "id": "c9f108c1b000a586"
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@@ -7199,14 +23166,14 @@
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    "mimetype": "text/x-python",
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    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
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