Compute_Leaf_Node_Current_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71739fa2",
   "metadata": {},
   "source": [
    "# Compute the Leaf Node current use\n",
    "\n",
    "This notebook shows how the wake-cycle current use was computed for the leaf node.\n",
    "\n",
    "To compute the current, an oscilloscpe was used to measure the voltage across a 2.2ohm resistor connected between the leaf node circuit ground and the power supply ground. The circuit was sampled and when a positive voltage read the data were stored for a 14 second time window. The sampling rate was 200kHz.\n",
    "\n",
    "James Gallagher <jgallagher@opendap.org>  9/5/21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "28980667",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.signal import butter, sosfilt\n",
    "\n",
    "# plotly makes nice plots, but the don't render in Github\n",
    "# and require a runtime engine that sometimes quits.\n",
    "#\n",
    "# import plotly.graph_objects as go\n",
    "# import plotly.express as px\n",
    "\n",
    "# use matploylib since the figures render in Github\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Add ipympl for the jupyter notebook 'magic' (https://github.com/matplotlib/ipympl)\n",
    "# conda install ipympl -c conda-forge"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cfed58a",
   "metadata": {},
   "source": [
    "### The raw data from the oscilloscpe\n",
    "![The raw data from the oscilloscpe](Current_measurement/LN_Current_13dBm_23dBm/23dBm.png)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a9ee7f8",
   "metadata": {},
   "source": [
    "## Process the data\n",
    "\n",
    "Read the data from the Siglent scope's csv file into a numpy array. The data are two columns, time and then voltage. Extract the voltages from the 2D array so they can be filtered.\n",
    "\n",
    "See https://numpy.org/doc/stable/reference/generated/numpy.genfromtxt.html for information about numpy genfromtxt() which reads data from CSV files.\n",
    "\n",
    "See https://www.tutorialspoint.com/numpy/numpy_indexing_and_slicing.htm for information about numpy slicing, which is used in several cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7b53f59a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For the tests where the LoRa was transmitting at 13dBm\n",
    "# data_file = 'Current_measurement/LN_Current_13dBm_23dBm/13dBm_current.csv'\n",
    "\n",
    "# For the tests where the LoRa was transmitting at 23dBm\n",
    "data_file = 'Current_measurement/LN_Current_13dBm_23dBm/23dBm_current.csv'\n",
    "header_lines = 12\n",
    "data = np.genfromtxt(data_file, delimiter=',', skip_header=header_lines)\n",
    "\n",
    "# slice so we have only the voltage values. 'data_file' holds both the sample time\n",
    "# and the voltage as CSV data.\n",
    "offset = 0.0\n",
    "voltages = data[..., 1] + offset    # * 1000 / args.resistance\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94c6739c",
   "metadata": {},
   "source": [
    "Define a low-pass filter using the SciPy package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e36ccab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def butter_lowpass_filter(data, cutoff, fs, order):\n",
    "    \"\"\"\n",
    "    @param data The data to be filtered\n",
    "    @param cutoff The cutoff frequency in Hz\n",
    "    @param fs The sampling frequency\n",
    "    @param order The order of the filter\n",
    "    \"\"\"\n",
    "    nyq = 0.5 * fs  # Nyquist Frequency\n",
    "    normal_cutoff = cutoff / nyq\n",
    "    # Get the filter coefficients - second order sections\n",
    "    sos = butter(order, normal_cutoff, btype='low', analog=False, output='sos')\n",
    "    y = sosfilt(sos, data)\n",
    "    return y\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52054f3a",
   "metadata": {},
   "source": [
    "Now filter the voltages to remove the noise, then threshold the filtered values so that anything below some arbitrary value becomes zero. From the raw and filtered/thresholded voltages, compute the current.\n",
    "\n",
    "See https://stackoverflow.com/questions/12424824/how-i-can-i-conditionally-change-the-values-in-a-numpy-array-taking-into-account for information about the numpy relational op used to threshold the filtered_volts[] array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "99262a39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter requirements.\n",
    "\n",
    "fs = 200000.0  # sample rate, Hz\n",
    "cutoff = 1000.0  # desired cutoff frequency of the filter, Hz\n",
    "order = 4 # I chose a fourth-order filter by experiment. Higher did not produce a better result.\n",
    "\n",
    "filtered_volts = butter_lowpass_filter(voltages, cutoff, fs, order)\n",
    "\n",
    "# Threshold post filtering.\n",
    "# y is derived from 'data' which is a numpy array, so this works to set\n",
    "# all the values of 'y' less than threshold_value to 0.0.\n",
    "threshold_value = 0.00015\n",
    "filtered_volts[filtered_volts < threshold_value] = 0.0\n",
    "\n",
    "resistance = 2.2 # ohms\n",
    "# multiply the current (A) by 1000 to get mA which makes for a better plot\n",
    "mA = voltages / resistance * 1000.0\n",
    "filtered_mA = filtered_volts / resistance * 1000.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2666994f",
   "metadata": {},
   "source": [
    "## Plot the data\n",
    "Now plot the filtered data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "22094b28",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Turn off the interactive mode for the notebook\n",
    "# %matplotlib widget\n",
    "\n",
    "fig, ax = plt.subplots()  # Create a figure containing a single axes.\n",
    "\n",
    "# x=data[::100, 0] Time\n",
    "# y=filtered_mA[::100] Current\n",
    "\n",
    "ax.plot(data[::100, 0], filtered_mA[::100])\n",
    "ax.set_title(\"Leaf Node Current Profile During the Wake State, LoRa Tx at 23dBm\")\n",
    "ax.set_xlabel(\"time (s)\")\n",
    "ax.set_ylabel(\"current (mA)\")\n",
    "\n",
    "plt.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d99c04",
   "metadata": {},
   "source": [
    "Compare the filtered and raw current data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b7a95384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib widget\n",
    "\n",
    "fig, ax = plt.subplots()  # Create a figure containing a single axes.\n",
    "\n",
    "# x=data[::100, 0] Time\n",
    "# y=filtered_mA[::100] Current\n",
    "\n",
    "ax.plot(data[::100, 0], mA[::100], label='signal with noise')\n",
    "ax.plot(data[::100, 0], filtered_mA[::100], label='filtered signal')\n",
    "ax.set_title(\"Leaf node raw and filtered current use during the operating phase, LoRa Tx at 23dBm\")\n",
    "ax.set_xlabel(\"time (s)\")\n",
    "ax.set_ylabel(\"current (mA)\")\n",
    "ax.legend()\n",
    "\n",
    "plt.draw()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7652a10a",
   "metadata": {},
   "source": [
    "## Compute the runtime current\n",
    "Now that the current data looks good, compute the average current use for the wake-cycle time (which looks like about 4s - also compute the exact time)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "366b835a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_values_from_filtered_data(data, zero_value, delta_t, resistance):\n",
    "    \"\"\"\n",
    "    @param data is corrected voltage for a time slice\n",
    "    @param zero_value is the value to call 'zero volts' FIXME Remove\n",
    "    @param delta_t is the duration of each sample\n",
    "    @param resistance Used to convert mV to mA\n",
    "    \"\"\"\n",
    "    volts_samples = 0.0\n",
    "    samples = 0\n",
    "    start_time = 0.0\n",
    "    end_time = 0.0\n",
    "    sample = 0\n",
    "\n",
    "    for volts in data:\n",
    "        if volts > zero_value:\n",
    "            if start_time == 0.0:\n",
    "                start_time = sample * delta_t\n",
    "            end_time = sample\n",
    "            samples += 1         # count the samples, divide once outside of loop\n",
    "            volts_samples += volts     # As above, factor out '* delta_t'\n",
    "        sample += 1\n",
    "\n",
    "    # Complete calculations and print the results\n",
    "    end_time *= delta_t\n",
    "    total_time = samples * delta_t\n",
    "    # volts_samples / samples: average voltage/sample; * delta_t is avg voltage/second\n",
    "    # delta_t is seconds/sample --> avg voltage / sample * delta_t is voltage/second\n",
    "    # voltage/second / resistance is  Amps/ econd; * 1000 --> mA/s\n",
    "    # mA/s * total_time is mAs\n",
    "    mAs = (volts_samples / samples) / resistance * 1000\n",
    "\n",
    "    print(f\"Sample start time: {start_time:.2f}s, end time: {end_time:.2f}s, total time awake: {total_time:.2f}s, mAs: {mAs:.0f}\\n\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d01a86",
   "metadata": {},
   "source": [
    "Calculate the total time the node was awake using the number of samples that were above the zero threshold and multiplying by the duration of a sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "91b2c2e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold value: 0.00015V\n",
      "Delta X: 5e-06s\n",
      "Resistance: 2.2ohms\n",
      "Sample start time: 1.00s, end time: 5.06s, total time awake: 4.04s, mAs: 25\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dx = 0.000005 # Sample duration in seconds\n",
    "\n",
    "# The values for these parameters are set in cells above, including the graphing cells.\n",
    "# The values are:\n",
    "print(f\"Threshold value: {threshold_value}V\")\n",
    "print(f\"Delta X: {dx}s\")\n",
    "print(f\"Resistance: {resistance}ohms\")\n",
    "\n",
    "calc_values_from_filtered_data(filtered_volts, threshold_value, dx, resistance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d94de96d",
   "metadata": {},
   "source": [
    "Battry life calculation\n",
    "\n",
    "The Leaf node has 2 2,400mAh batteries in parallel, providing 4,800mAh of charge.\n",
    "\n",
    "The duty cycle of the LN is 3600s, ~4s on (25mA) and 3596s sleeping (18.5µA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e2d1f4eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "mA_duty_cycle = (25.0 * 4 + 18.5 * 0.001 * 3596.0) / 3600.0 # 18.5 µA is 18.5*10-3 mA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4afb0a55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Battery life: 103768h, or 11.8 years\n"
     ]
    }
   ],
   "source": [
    "battery_life = 4800.0 / mA_duty_cycle\n",
    "\n",
    "hours_per_year = 365.25 * 24 # 365.25 days in a year on average. sort of...\n",
    "\n",
    "print(f\"Battery life: {battery_life:.0f}h, or {battery_life / hours_per_year:.1f} years\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be9d24ac",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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