{
 "cells": [
  {
   "cell_type": "code",
   "id": "fbc121e30a2defb3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T14:47:24.473239Z",
     "start_time": "2025-11-21T14:47:24.470530Z"
    }
   },
   "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",
    "from scipy.fft import fft, ifft\n",
    "from experiment_loader import load_2d_experiment, load_3d_experiment\n",
    "from modeling import *\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "# plt.rcParams['figure.figsize'] = [25, 15]"
   ],
   "outputs": [],
   "execution_count": 20
  },
  {
   "cell_type": "code",
   "id": "37bf71a9-de31-4274-8b55-d20efc1bf556",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T14:47:24.836372Z",
     "start_time": "2025-11-21T14:47:24.834668Z"
    }
   },
   "source": [
    "PLOT_WIDTH = 2000\n",
    "PLOT_HEIGHT = 1000"
   ],
   "outputs": [],
   "execution_count": 21
  },
  {
   "cell_type": "code",
   "id": "a96ea0301fc7692a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T14:47:25.000327Z",
     "start_time": "2025-11-21T14:47:24.997035Z"
    }
   },
   "source": [
    "channels, filter_meas = load_2d_experiment(\"./lut_channnel_sweep.csv\")\n",
    "filters, filter_meas = load_2d_experiment(\"./lut_filter_sweep.csv\")\n",
    "inputs, input_meas = load_2d_experiment(\"./lut_input_sweep.csv\")\n",
    "channel_cf, filters_cf, channel_filter_meas = load_3d_experiment(\"./lut_channel_filter_sweep.csv\")"
   ],
   "outputs": [],
   "execution_count": 22
  },
  {
   "cell_type": "code",
   "id": "c7abf7a2802a93b8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T14:47:25.193118Z",
     "start_time": "2025-11-21T14:47:25.191467Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T14:47:25.395929Z",
     "start_time": "2025-11-21T14:47:25.393327Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def calculate_complexity(\n",
    "    input_shape: tuple, kernel_size: tuple, stride: tuple, filters: int, padding: str\n",
    "):\n",
    "    if padding == \"valid\":\n",
    "        out_x = np.floor((input_shape[0] - kernel_size[0]) / stride[0]) + 1\n",
    "        out_y = np.floor((input_shape[1] - kernel_size[1]) / stride[1]) + 1\n",
    "    else:\n",
    "        out_x = np.floor((input_shape[0] - 1) / stride[0]) + 1\n",
    "        out_y = np.floor((input_shape[1] - 1) / stride[1]) + 1\n",
    "    return kernel_size[0] * kernel_size[1] * input_shape[2] * out_x * out_y * filters\n"
   ],
   "id": "cad87ae957f50f74",
   "outputs": [],
   "execution_count": 23
  },
  {
   "cell_type": "code",
   "id": "9a653659b7f067cf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T14:47:25.715960Z",
     "start_time": "2025-11-21T14:47:25.702136Z"
    }
   },
   "source": [
    "import scipy\n",
    "from collections import Counter\n",
    "from itertools import repeat, chain\n",
    "\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",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=filters, y=filter_meas, name=\"Filter Measurements\"))\n",
    "deltas = []\n",
    "for i in range(1, len(filter_meas)):\n",
    "    deltas.append((filter_meas[i] - filter_meas[i-1]) / filter_meas[i-1])\n",
    "\n",
    "\n",
    "peaks = scipy.signal.find_peaks(deltas, prominence=0.1)[0]\n",
    "distances = []\n",
    "\n",
    "for (fpidx, first_peak) in enumerate(peaks):\n",
    "    for (spidx, second_peak) in enumerate(peaks[fpidx+1:]):\n",
    "        distances.append(int(second_peak - first_peak))\n",
    "\n",
    "print(set(list(chain.from_iterable(repeat(i, c) for i,c in Counter(distances).most_common()))))\n",
    "\n",
    "\n",
    "counted_distances = {int(d):distances.count(d) for d in distances}\n",
    "print(counted_distances)\n",
    "fig.add_trace(go.Scatter(x=filters[1:], y=deltas, name=\"Deltas\"))\n",
    "\n",
    "fig.update_layout(\n",
    "    autosize=False,\n",
    "    width=PLOT_WIDTH,\n",
    "    height=PLOT_HEIGHT,\n",
    ")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{128, 256, 2, 64, 192, 320, 448, 384, 51, 115, 117, 53, 245, 373, 501, 243, 371, 499}\n",
      "{2: 1, 53: 1, 117: 1, 245: 1, 373: 1, 501: 1, 51: 1, 115: 1, 243: 1, 371: 1, 499: 1, 64: 1, 192: 1, 320: 1, 448: 1, 128: 3, 256: 2, 384: 1}\n"
     ]
    },
    {
     "data": {
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   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T14:47:25.979315Z",
     "start_time": "2025-11-21T14:47:25.968826Z"
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   "source": [
    "from scipy.fft import fft, fftfreq\n",
    "import numpy as np\n",
    "\n",
    "# Same signal creation...\n",
    "\n",
    "# Perform FFT\n",
    "bins = 512\n",
    "offset = 7\n",
    "signal = deltas[offset:offset + bins]\n",
    "print(len(deltas))\n",
    "print(len(signal))\n",
    "fft_result = fft(signal)\n",
    "\n",
    "n = len(fft_result)\n",
    "frequencies = fftfreq(n, 1)[:n//2]\n",
    "magnitude = 2 * np.abs(fft_result[:n//2]) / n\n",
    "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "# fig.add_trace(go.Scatter(x=channels[1:], y=deltas, name=\"Point to point variance\", line=dict(color=\"lightgreen\")), secondary_y=True)\n",
    "fig.add_trace(go.Scatter(x=frequencies, y=magnitude, name=\"Point to point variance\", line=dict(color=\"lightgreen\")), secondary_y=True)\n",
    "mean_magnitude = np.mean(magnitude)\n",
    "\n",
    "for (mag, freq) in sorted(zip(magnitude, frequencies), reverse=True):\n",
    "    if freq > 0 and mag > mean_magnitude:\n",
    "        print(round(1 / freq), mag)\n",
    "fig.show()"
   ],
   "id": "b67cdbe1c2001abd",
   "outputs": [
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   "execution_count": 25
  },
  {
   "cell_type": "code",
   "id": "361ee9ff66d1b6e5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T14:47:26.604240Z",
     "start_time": "2025-11-21T14:47:26.596345Z"
    }
   },
   "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=PLOT_WIDTH,\n",
    "    height=PLOT_HEIGHT,\n",
    ")"
   ],
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
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        "plotlyServerURL": "https://plot.ly"
       }
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 26
  },
  {
   "cell_type": "code",
   "id": "51ca081aacbae203",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T14:47:26.973076Z",
     "start_time": "2025-11-21T14:47:26.894776Z"
    }
   },
   "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",
    "x_unique = list(set(df[\"channels\"]))\n",
    "x_unique.sort()\n",
    "y_unique = list(set(df[\"filters\"]))\n",
    "y_unique.sort()\n",
    "# print(y_unique)\n",
    "z = []\n",
    "for y in y_unique:\n",
    "    z.append([])\n",
    "    for x in x_unique:\n",
    "        z[-1].append(df.loc[df[\"channels\"] == x].loc[df[\"filters\"] == y][\"ms\"].values[0])\n",
    "# np.reshape([x.shape[0], y.shape[0]])\n",
    "# print(z)\n",
    "fig.add_trace(go.Scatter3d(\n",
    "    x=df['channels'],\n",
    "    y=df['filters'],\n",
    "    z=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",
    "fig.add_trace(go.Surface(\n",
    "    x=x_unique,\n",
    "    y=y_unique,\n",
    "    z=z,\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=PLOT_WIDTH,\n",
    "    height=PLOT_HEIGHT,\n",
    "        template='plotly_white',\n",
    ")\n",
    "fig.layout.scene.camera.projection.type = \"orthographic\"\n",
    "# Display the plot\n",
    "fig.show()\n"
   ],
   "outputs": [
    {
     "data": {
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    "fig = go.Figure()\n",
    "split_df = df.groupby('channels')\n",
    "first = True\n",
    "# deltas = []\n",
    "# approx = split_df.get_group(100)\n",
    "\n",
    "# lv = list(approx['ms'])[0]\n",
    "# for meas in list(approx['ms'])[1:]:\n",
    "#     deltas.append((meas / lv))\n",
    "#     lv = meas\n",
    "deltas = calculate_deltas(list(split_df.get_group(120)['ms']))\n",
    "for category, category_df in split_df:\n",
    "    # print(category_df)\n",
    "    # if not first:\n",
    "    #    continue\n",
    "    upper_right = 195\n",
    "    upper_left = 129\n",
    "    lower_right = 192\n",
    "    lower_left = 126\n",
    "    # # upper_m, upper_b = lin_interpol( upper_sampled_channels[0], upper_sampled_channels[1], upper_sampled_filter_meas[0], upper_sampled_filter_meas[1])\n",
    "\n",
    "    upper_right_meas = category_df.loc[category_df[\"filters\"] == upper_right][\"ms\"].values[0]\n",
    "    upper_left_meas = category_df.loc[category_df[\"filters\"] == upper_left][\"ms\"].values[0]\n",
    "\n",
    "    lower_right_meas = category_df.loc[category_df[\"filters\"] == lower_right][\"ms\"].values[0]\n",
    "    lower_left_meas = category_df.loc[category_df[\"filters\"] == lower_left][\"ms\"].values[0]\n",
    "\n",
    "    # print(upper_right_meas)\n",
    "    # print(upper_left_meas)\n",
    "\n",
    "    upper_m, upper_b = lin_interpol(upper_left - 3, upper_right - 3, upper_left_meas, upper_right_meas)\n",
    "    lower_m, lower_b = lin_interpol(lower_left, lower_right, lower_left_meas, lower_right_meas)\n",
    "    # print(list(category_df['channels']))\n",
    "    # print(list(range(category_df['channels'][0], list(category_df['channels'])[-1])))\n",
    "    start = list(category_df['filters'])[0]\n",
    "    end = list(category_df['filters'])[-1]\n",
    "    r_c = list(range(start, end))\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",
    "\n",
    "    # fig.add_trace(go.Scatter(x=r_c, y=[c * upper_m + upper_b for c in r_c], name=\"Upper Sampled Channels\"))\n",
    "    # fig.add_trace(go.Scatter(x=r_c, y=[c * lower_m + lower_b for c in r_c], name=\"Lower Sampled Channels\"))\n",
    "    r_v_rect = [calc_rect(c, upper_m, upper_b, lower_m, lower_b) for c in r_c]\n",
    "    lv = list(category_df['ms'])[0]\n",
    "    delta_approx = [lv]\n",
    "    for delta in deltas:\n",
    "        lv = delta * lv\n",
    "        delta_approx.append(lv)\n",
    "        \n",
    "    errs = [(1 - (g / m)) * 100 for g, m in zip(delta_approx, list(category_df['ms']))]\n",
    "    all_errs = []\n",
    "    all_errs.append(np.mean(np.abs(errs)))\n",
    "\n",
    "    print(np.mean(np.abs(errs)))\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\"filter {category}\",\n",
    "        mode='markers+lines',\n",
    "        line=dict(\n",
    "            dash='dash',\n",
    "            width=.5\n",
    "        )\n",
    "    ))\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=list(category_df['filters']),\n",
    "        y=delta_approx,\n",
    "        name=f\"delta_approx {category}\",\n",
    "        mode='lines',\n",
    "    ))\n",
    "    \n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=list(category_df['filters']),\n",
    "        y=errs,\n",
    "        name=f\"delta_approx err {category}\",\n",
    "        mode='lines',\n",
    "    ))\n",
    "\n",
    "    first = False\n",
    "print(f\"{np.mean(all_errs)=}\")\n",
    "\n",
    "fig.update_layout(\n",
    "    scene=dict(\n",
    "        xaxis_title='filters',\n",
    "        yaxis_title='filters',\n",
    "        zaxis_title='ms'\n",
    "    ),\n",
    "    width=PLOT_WIDTH,\n",
    "    height=PLOT_HEIGHT,\n",
    "        template='plotly_white',\n",
    ")\n",
    "fig.show()"
   ],
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 0 is out of bounds for axis 0 with size 0",
     "output_type": "error",
     "traceback": [
      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
      "\u001B[31mIndexError\u001B[39m                                Traceback (most recent call last)",
      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[28]\u001B[39m\u001B[32m, line 22\u001B[39m\n\u001B[32m     19\u001B[39m lower_left = \u001B[32m126\u001B[39m\n\u001B[32m     20\u001B[39m \u001B[38;5;66;03m# # upper_m, upper_b = lin_interpol( upper_sampled_channels[0], upper_sampled_channels[1], upper_sampled_filter_meas[0], upper_sampled_filter_meas[1])\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m22\u001B[39m upper_right_meas = \u001B[43mcategory_df\u001B[49m\u001B[43m.\u001B[49m\u001B[43mloc\u001B[49m\u001B[43m[\u001B[49m\u001B[43mcategory_df\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mfilters\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m \u001B[49m\u001B[43m==\u001B[49m\u001B[43m \u001B[49m\u001B[43mupper_right\u001B[49m\u001B[43m]\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mms\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m.\u001B[49m\u001B[43mvalues\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[32m     23\u001B[39m upper_left_meas = category_df.loc[category_df[\u001B[33m\"\u001B[39m\u001B[33mfilters\u001B[39m\u001B[33m\"\u001B[39m] == upper_left][\u001B[33m\"\u001B[39m\u001B[33mms\u001B[39m\u001B[33m\"\u001B[39m].values[\u001B[32m0\u001B[39m]\n\u001B[32m     25\u001B[39m lower_right_meas = category_df.loc[category_df[\u001B[33m\"\u001B[39m\u001B[33mfilters\u001B[39m\u001B[33m\"\u001B[39m] == lower_right][\u001B[33m\"\u001B[39m\u001B[33mms\u001B[39m\u001B[33m\"\u001B[39m].values[\u001B[32m0\u001B[39m]\n",
      "\u001B[31mIndexError\u001B[39m: index 0 is out of bounds for axis 0 with size 0"
     ]
    }
   ],
   "execution_count": 28
  },
  {
   "cell_type": "code",
   "id": "c958d465066f3b1d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T14:47:28.023381Z",
     "start_time": "2025-11-21T14:47:28.007330Z"
    }
   },
   "source": [
    "filenames = [\"lut_input_sweep_K1x1.csv\", \"lut_input_sweep_K3x3.csv\", \"lut_input_sweep_K5x5.csv\"]\n",
    "fig = go.Figure()\n",
    "# deltas = calculate_deltas(list(split_df.get_group(120)['ms']))\n",
    "inputs_k1, inputs_k1_meas = load_2d_experiment(\"lut_input_sweep_K1x1.csv\")\n",
    "inputs_k3, inputs_k3_meas = load_2d_experiment(\"lut_input_sweep_K3x3.csv\")\n",
    "inputs_k5, inputs_k5_meas = load_2d_experiment(\"lut_input_sweep_K5x5.csv\") \n",
    "\n",
    "fig.add_trace(go.Scatter(x=inputs_k1, y=inputs_k1_meas, name=f\"Input Measurements K1\"))\n",
    "fig.add_trace(go.Scatter(x=inputs_k3, y=inputs_k3_meas, name=f\"Input Measurements K3\"))\n",
    "fig.add_trace(go.Scatter(x=inputs_k5, y=inputs_k5_meas, name=f\"Input Measurements K5\"))\n",
    "\n",
    "fig.update_layout(\n",
    "    autosize=False,\n",
    "width=PLOT_WIDTH,\n",
    "height=PLOT_HEIGHT,\n",
    "    # margin=dict(\n",
    "    #     l=50,\n",
    "    #     r=50,\n",
    "    #     b=100,\n",
    "    #     t=100,\n",
    "    #     pad=4\n",
    "    # ),\n",
    "    )\n",
    "fig.show()"
   ],
   "outputs": [
    {
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     "metadata": {},
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   ],
   "execution_count": 29
  },
  {
   "cell_type": "code",
   "id": "e266a562cbc80021",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T14:47:28.951609Z",
     "start_time": "2025-11-21T14:47:28.940721Z"
    }
   },
   "source": [
    "# Source - https://stackoverflow.com/a\n",
    "# Posted by John La Rooy, modified by community. See post 'Timeline' for change history\n",
    "# Retrieved 2025-11-19, License - CC BY-SA 4.0\n",
    "\n",
    "import os\n",
    "from glob import glob\n",
    "\n",
    "result = [y for x in os.walk(\"./Sweeps\") for y in glob(os.path.join(x[0], '*.json'))]\n"
   ],
   "outputs": [],
   "execution_count": 30
  },
  {
   "cell_type": "code",
   "id": "6d714069-fdc3-42a1-86ac-797f5c4a268f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T14:47:30.961124Z",
     "start_time": "2025-11-21T14:47:29.298112Z"
    }
   },
   "source": [
    "import re\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "# print(result)\n",
    "test_str = \"\"\n",
    "# Source - https://stackoverflow.com/a\n",
    "# Posted by Nadia Alramli\n",
    "# Retrieved 2025-11-19, License - CC BY-SA 2.5\n",
    "test = \"Conv2D_In256_C128_F112_Kx3_Ky3_Sx1_Sy1_Px1_Py1_Dx1_Dx1.json\"\n",
    "characterization = {}\n",
    "for res in result:\n",
    "    m = re.match( r'.*In(?P<i>\\d+)_C(?P<c>\\d+)_F(?P<f>\\d+)_Kx(?P<kx>\\d+)_Ky(?P<ky>\\d+)_Sx(?P<sx>\\d+)_Sy(?P<sy>\\d+)_Px(?P<px>\\d+)_Py(?P<py>\\d+)_Dx(?P<dx>\\d+)_Dx(?P<dy>\\d+)\\.json', res)\n",
    "    if m:\n",
    "        # print(m.groupdict())\n",
    "        keys = m.groupdict()\n",
    "        for key in keys:\n",
    "            keys[key] = int(keys[key])\n",
    "        with open(res) as f:\n",
    "            data = json.load(f)\n",
    "            # print(data)\n",
    "            # print(np.mean(data[\"layers\"][\"/lut_conv2d/Conv\"][\"LatencyMS\"]))\n",
    "            if keys[\"i\"] not in characterization:\n",
    "                characterization[keys[\"i\"]] = {}\n",
    "            if keys[\"c\"] not in characterization[keys[\"i\"]]:\n",
    "                characterization[keys[\"i\"]][keys[\"c\"]] = {}\n",
    "            if keys[\"f\"] not in characterization[keys[\"i\"]][keys[\"c\"]]:\n",
    "                characterization[keys[\"i\"]][keys[\"c\"]][keys[\"f\"]] = {}\n",
    "            if keys[\"kx\"] not in characterization[keys[\"i\"]][keys[\"c\"]][keys[\"f\"]]:\n",
    "                characterization[keys[\"i\"]][keys[\"c\"]][keys[\"f\"]][keys[\"kx\"]] = {}\n",
    "            if keys[\"ky\"] not in characterization[keys[\"i\"]][keys[\"c\"]][keys[\"f\"]][keys[\"kx\"]]:\n",
    "                characterization[keys[\"i\"]][keys[\"c\"]][keys[\"f\"]][keys[\"kx\"]][keys[\"ky\"]] = np.mean(data[\"layers\"][\"/lut_conv2d/Conv\"][\"LatencyMS\"])\n",
    "\n",
    "    else:\n",
    "        print(res)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./Sweeps/lut_channel_sweep.json\n"
     ]
    }
   ],
   "execution_count": 31
  },
  {
   "cell_type": "code",
   "id": "5d10b8cf-ae00-447c-bc1f-b248e7c2a3f2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T14:47:31.046582Z",
     "start_time": "2025-11-21T14:47:30.970015Z"
    }
   },
   "source": [
    "characterization_list = []\n",
    "import plotly.express as px\n",
    "df = []\n",
    "\n",
    "print(df)\n",
    "print(sorted(characterization[256].keys()))\n",
    "for c in characterization[256]:\n",
    "    for f in characterization[256][c]:\n",
    "\n",
    "        for kx in characterization[256][c][f]:\n",
    "            for ky in characterization[256][c][f][kx]:\n",
    "                df.append([c, f, (kx, ky), characterization[256][c][f][kx][ky], calculate_complexity((256, 256, c), (kx, ky), (1, 1), f, \"valid\") / characterization[256][c][f][kx][ky], np.log2(characterization[256][c][f][kx][ky])])\n",
    "\n",
    "# print(df)\n",
    "df = pd.DataFrame(data=df, columns=[\"Channels\", \"Filters\", \"Kernel\",\"LatencyMS\", \"Op-Cost\", \"LatencyLog\"])\n",
    "print(df)\n",
    "import plotly.express as px\n",
    "fig = px.scatter_3d(df, x='Channels', y='Filters', z='LatencyMS', color=\"Kernel\")\n",
    "fig.update_layout(width=2000, height=1000, margin=dict(l=0, r=0, b=0, t=0))\n",
    "fig.show()\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049]\n",
      "      Channels  Filters  Kernel  LatencyMS       Op-Cost  LatencyLog\n",
      "0          128      338  (3, 3)  13.605897  1.846330e+09    3.766160\n",
      "1          128      338  (5, 5)  34.520517  1.989713e+09    5.109382\n",
      "2          128       24  (3, 3)   2.793949  6.384292e+08    1.482306\n",
      "3          128       24  (5, 5)   7.066144  6.902077e+08    2.820923\n",
      "4          128      327  (3, 3)  13.563218  1.791864e+09    3.761628\n",
      "...        ...      ...     ...        ...           ...         ...\n",
      "6898       253      256  (3, 3)  16.929702  2.221371e+09    4.081485\n",
      "6899       253      397  (3, 3)  33.772264  1.726875e+09    5.077767\n",
      "6900       253      328  (3, 3)  25.439789  1.894047e+09    4.669015\n",
      "6901       253      307  (3, 3)  25.458320  1.771492e+09    4.670065\n",
      "6902       707      128  (3, 3)  25.984329  2.022217e+09    4.699570\n",
      "\n",
      "[6903 rows x 6 columns]\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "hovertemplate": "Kernel=(3, 3)<br>Channels=%{x}<br>Filters=%{y}<br>LatencyMS=%{z}<extra></extra>",
         "legendgroup": "(3, 3)",
         "marker": {
          "color": "#636efa",
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "(3, 3)",
         "scene": "scene",
         "showlegend": true,
         "x": {
          "dtype": "i2",
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         "y": {
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"
         },
         "type": "scatter3d"
        },
        {
         "hovertemplate": "Kernel=(5, 5)<br>Channels=%{x}<br>Filters=%{y}<br>LatencyMS=%{z}<extra></extra>",
         "legendgroup": "(5, 5)",
         "marker": {
          "color": "#EF553B",
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "(5, 5)",
         "scene": "scene",
         "showlegend": true,
         "x": {
          "dtype": "i2",
          "bdata": 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   "execution_count": 32
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  {
   "cell_type": "code",
   "id": "9af37da0-3655-4b4f-9cd3-98cae5120fcf",
   "metadata": {
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     "end_time": "2025-11-21T14:50:27.178360Z",
     "start_time": "2025-11-21T14:50:26.408338Z"
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   "source": [
    "\n",
    "# df = pd.DataFrame(data=df, columns=[\"Channels\", \"Filters\", \"Kernel\",\"LatencyMS\", \"Op-Cost\", \"LatencyLog\"])\n",
    "import plotly.io as pio\n",
    "pio.renderers.default = \"browser\"\n",
    "channel_step_size = 32\n",
    "channel_rect_size = 8\n",
    "\n",
    "filter_step_size = 128\n",
    "filter_rect_size = 1\n",
    "\n",
    "kernel_prediction = (3, 3)\n",
    "\n",
    "filter_start = 128\n",
    "filter_end = 1028\n",
    "fc_dims = [filter_start, filter_end]\n",
    "# print(channels)\n",
    "upper_sampled_channels = [97, 1025]\n",
    "lower_sampled_channels = [96, 1024]\n",
    "predictions = []\n",
    "for fc_dim in fc_dims:\n",
    "    print(fc_dim)\n",
    "    # print(df[(df[\"Kernel\"] == (3, 3)) & (df[\"Filters\"] == fc_dim)& (df[\"Channels\"] == 97)])\n",
    "\n",
    "    upper_sampled_channel_meas = [df[(df[\"Kernel\"] == (3, 3)) & (df[\"Filters\"] == fc_dim) & (df[\"Channels\"] == c)].iloc[0][\"LatencyMS\"] for c in upper_sampled_channels]\n",
    "    lower_sampled_channel_meas = [df[(df[\"Kernel\"] == (3, 3)) & (df[\"Filters\"] == fc_dim) & (df[\"Channels\"] == c)].iloc[0][\"LatencyMS\"] for c in lower_sampled_channels]\n",
    "    print(upper_sampled_channel_meas)\n",
    "    print(lower_sampled_channel_meas)\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",
    "    for c in range (upper_sampled_channels[1] + 1):\n",
    "        # def calc_rect(c:int , m_u: float, b_u: float, m_l: float, b_l: float, step_period: float, rect_period: float) -> float:\n",
    "        r_v_rect = calc_rect(c, upper_m, upper_b, lower_m, lower_b, channel_step_size, channel_rect_size)\n",
    "        predictions.append([c, fc_dim, r_v_rect])\n",
    "\n",
    "for c in range (1, upper_sampled_channels[1] + 1, 3):\n",
    "    filter_preds = [pred for pred in predictions if pred[0] == c]\n",
    "    # print(filter_preds)\n",
    "    f128_pred_lat = [pred[2] for pred in filter_preds if pred[1] == 128][0]\n",
    "    f1028_pred_lat = [pred[2] for pred in filter_preds if pred[1] == 1028][0]\n",
    "\n",
    "    upper_m, upper_b = lin_interpol( 128, 1028, f128_pred_lat, f1028_pred_lat)\n",
    "    # lower_m, lower_b = lin_interpol(lower_sampled_filters[0], lower_sampled_filters[1], lower_sampled_filters_meas[0], lower_sampled_filters_meas[1])\n",
    "    for f in range (0,1100,3):\n",
    "        # def calc_rect(c:int , m_u: float, b_u: float, m_l: float, b_l: float, step_period: float, rect_period: float) -> float:\n",
    "        r_v_rect = calc_rect(f, upper_m, upper_b, upper_m, upper_b, filter_step_size, filter_rect_size)\n",
    "        predictions.append([c, f, r_v_rect])\n",
    "print(\"hi\")\n",
    "\n",
    "\n",
    "\n",
    "cf_dims = [96, 1024]\n",
    "upper_sampled_filters = [128, 1028]\n",
    "lower_sampled_filters = [128, 1028]\n",
    "for cf_dim in cf_dims:\n",
    "    # print(df[(df[\"Kernel\"] == (3, 3)) & (df[\"Filters\"] == fc_dim)& (df[\"Channels\"] == 97)])\n",
    "    print(df[(df[\"Kernel\"] == (3, 3)) & (df[\"Filters\"] == 1024)& (df[\"Channels\"] == cf_dim)])\n",
    "    upper_sampled_filters_meas = [df[(df[\"Kernel\"] == (3, 3)) & (df[\"Filters\"] == f) & (df[\"Channels\"] == cf_dim)].iloc[0][\"LatencyMS\"] for f in upper_sampled_filters]\n",
    "    lower_sampled_filters_meas = [df[(df[\"Kernel\"] == (3, 3)) & (df[\"Filters\"] == f) & (df[\"Channels\"] == cf_dim)].iloc[0][\"LatencyMS\"] for f in lower_sampled_filters]\n",
    "    print(upper_sampled_filters_meas)\n",
    "    print(lower_sampled_filters_meas)\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_filters[0], upper_sampled_filters[1], upper_sampled_filters_meas[0], upper_sampled_filters_meas[1])\n",
    "    lower_m, lower_b = lin_interpol(lower_sampled_filters[0], lower_sampled_filters[1], lower_sampled_filters_meas[0], lower_sampled_filters_meas[1])\n",
    "    for f in range (1100):\n",
    "        # def calc_rect(c:int , m_u: float, b_u: float, m_l: float, b_l: float, step_period: float, rect_period: float) -> float:\n",
    "        r_v_rect = calc_rect(f, upper_m, upper_b, lower_m, lower_b, filter_step_size, filter_rect_size)\n",
    "        predictions.append([cf_dim, f, r_v_rect])\n",
    "\n",
    "\n",
    "sub_df = df[(df[\"Kernel\"] == (3, 3))]\n",
    "pdf = pd.DataFrame(data=predictions, columns=[\"Channels\", \"Filters\", \"LatencyMS\"])\n",
    "fig = go.Figure()\n",
    "\n",
    "fig =fig.add_trace(go.Scatter3d(x=sub_df['Channels'], y=sub_df['Filters'], z=sub_df['LatencyMS'], marker=dict(size=2), mode='markers'))\n",
    "\n",
    "fig.add_trace(go.Scatter3d(\n",
    "        x=pdf['Channels'],\n",
    "        y=pdf['Filters'],\n",
    "        z=pdf['LatencyMS'],\n",
    "        mode='markers',\n",
    "        marker=dict(size=2),\n",
    "        # name=f\"channel {category}\",\n",
    "        # mode='markers+lines',\n",
    "        line=dict(\n",
    "            dash='dash',\n",
    "            width=.1\n",
    "        )\n",
    "    ))\n",
    "fig.update_layout(width=2000, height=1000, margin=dict(l=0, r=0, b=0, t=0),\n",
    "    scene=dict(\n",
    "        xaxis_title='channels',\n",
    "        yaxis_title='filters',\n",
    "        zaxis_title='ms'\n",
    "    ),\n",
    "\n",
    "        template='plotly_white',\n",
    ")\n",
    "fig.show()\n",
    "#\n",
    "#\n",
    "# channel_lower_approx =\n",
    "\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128\n",
      "[np.float64(4.9109807968139645), np.float64(37.22674671808878)]\n",
      "[np.float64(3.5613492742369446), np.float64(32.39117988380226)]\n",
      "1028\n",
      "[np.float64(44.43491191864014), np.float64(331.73373631068637)]\n",
      "[np.float64(32.60566730499268), np.float64(290.88586764865454)]\n",
      "hi\n",
      "      Channels  Filters  Kernel  LatencyMS       Op-Cost  LatencyLog\n",
      "1096        96     1024  (3, 3)  29.010786  1.967531e+09    4.858517\n",
      "[np.float64(3.5613492742369446), np.float64(32.60566730499268)]\n",
      "[np.float64(3.5613492742369446), np.float64(32.60566730499268)]\n",
      "      Channels  Filters  Kernel   LatencyMS       Op-Cost  LatencyLog\n",
      "1869      1024     1024  (3, 3)  260.395382  2.338173e+09     8.02456\n",
      "[np.float64(32.39117988380226), np.float64(290.88586764865454)]\n",
      "[np.float64(32.39117988380226), np.float64(290.88586764865454)]\n"
     ]
    }
   ],
   "execution_count": 38
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T14:47:51.358209Z",
     "start_time": "2025-11-21T14:47:51.354108Z"
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   },
   "cell_type": "code",
   "source": "calculate_complexity((256, 256, 1024), (3, 3), (1, 1),1024, \"same\")",
   "id": "e97d1684e488064a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(618475290624.0)"
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     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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   "execution_count": 34
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