AMILIO-Model-Playground/notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8a77807f92f26ee",
   "metadata": {},
   "source": [
    "# This is a sample Jupyter Notebook\n",
    "\n",
    "Below is an example of a code cell. \n",
    "Put your cursor into the cell and press Shift+Enter to execute it and select the next one, or click 'Run Cell' button.\n",
    "\n",
    "Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.\n",
    "\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)."
   ]
  },
  {
   "cell_type": "code",
   "id": "fbc121e30a2defb3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-28T07:57:01.453882Z",
     "start_time": "2025-08-28T07:57:01.312372Z"
    }
   },
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "import csv\n",
    "import math\n",
    "import plotly.graph_objects as go\n",
    "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]"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "id": "a96ea0301fc7692a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-28T07:57:02.356279Z",
     "start_time": "2025-08-28T07:57:02.349490Z"
    }
   },
   "source": [
    "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",
    "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"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "id": "79eaf4a295ca7143",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-25T07:26:41.650060Z",
     "start_time": "2025-08-25T07:26:41.646036Z"
    }
   },
   "source": [
    "def calc_upper(c: int, m: float, b: float) -> float:\n",
    "    return np.floor(c / 32) * 32 * m + b\n",
    "def calc_lower(c: int, m: float, b: float) -> float:\n",
    "    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_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"
   ],
   "outputs": [],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "id": "b098c375bdbc1389",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-25T07:26:41.842019Z",
     "start_time": "2025-08-25T07:26:41.839325Z"
    }
   },
   "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"
   ],
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "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",
    "pol_approx = []\n",
    "\n",
    "\n",
    "\n",
    "# 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",
    "# 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_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",
    "\n",
    "\n",
    "# r_c = [c / 10 for c in range(1, 10240)]\n",
    "r_c = channels\n",
    "\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",
    "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_rect = [np.abs((approx - meas) / meas) for approx, meas in zip(r_v_rect, channel_meas)]\n",
    "\n",
    "print(np.mean(err_sine))\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\", 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",
    "    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",
    ")\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.028252145094345097\n",
      "0.016604167625824784\n"
     ]
    },
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  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-25T09:35:08.874926Z",
     "start_time": "2025-08-25T09:35:08.861215Z"
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   "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",
    ")"
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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"
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   "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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  {
   "metadata": {
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     "end_time": "2025-08-28T08:53:12.979629Z",
     "start_time": "2025-08-28T08:53:12.872389Z"
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    "\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": [
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          "width": 0.5
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         "name": "channel 120",
         "x": {
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