{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"text-align:center; color:Purple\"><u><b>Sensors and Dataloggers Response/ Bode Plots</b></u></h1><br><br>\n",
    "\n",
    "Here I'm using Obspy to generate bode plots for different sensors and dataloggers using the DMC nominal response files. Alternatively, if one needs, can input the poles and zeros to generate the response/ bode plots. <br><br>\n",
    "<b>Sensors: CMG-3T, Titan accelerometer, and STS-2 <br>\n",
    "Dataloggers: Q330, Centaur, and RT-130 <br></b><br>\n",
    "Please download the nominal response files you need from this link to your working folder: http://ds.iris.edu/NRL/ <br>\n",
    "At the end of the execution of the follwing codes, I am saving the plots in .png format into your working folder.<br><br>\n",
    "<u>References</u>\n",
    "1. https://docs.obspy.org/master/tutorial/code_snippets/seismometer_correction_simulation.html?highlight=seed%20resp <br>\n",
    "2. https://docs.obspy.org/packages/autogen/obspy.core.inventory.response.Response.plot.html <br>\n",
    "3. https://docs.obspy.org/tutorial/code_snippets/frequency_response.html <br>\n",
    " \n",
    "\n",
    "<br><br>\n",
    "<I>2020/08/30 <br>\n",
    "by Saminda Nirmal Fonseka<br>\n",
    "IRIS/PASSCAL instrument center</I><br><br>\n",
    "\n",
    "============================================================================================================<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style=\"text-align:center;color:Blue\"> 1. Bode Plots for Sensors</h2><br><br>\n",
    "\n",
    "<br>\n",
    "<h3 style=\"text-align:center;color:orange\">Using DMC Nominal Response Files <br>\n",
    "\n",
    "<I> Download the  sensors or dataloggers transfer function from DMC, you can insert it into the following code and generate bode plots </I></h3>\n",
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=\"text-align:center\">A: CMG-3T </h3><br>\n",
    "<h3>120s/ 1500V/M/S</h3>\n",
    "    \n",
    "Link: http://ds.iris.edu/NRL/sensors/guralp/guralp_cmg3t_sensors.html <br>\n",
    "File: http://ds.iris.edu/NRL/sensors/guralp/RESP.XX.NS007..BHZ.CMG3T.120.1500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loadding necessary tools\n",
    "\n",
    "import obspy\n",
    "from obspy import read\n",
    "import matplotlib.pyplot as plt\n",
    "from obspy import read_inventory\n",
    "from obspy.signal.invsim import paz_to_freq_resp\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the resp file and plot it.\n",
    "\n",
    "<I><B>Response.plot(min_freq, output='VEL', start_stage=None, end_stage=None, label=None, axes=None, sampling_rate=None, unwrap_phase=False, plot_degrees=False, show=True, outfile=None)</B>\n",
    "\n",
    "minimum frequency = 0.0001<br>\n",
    "Output units: \"DISP\" for displacement / \"VEL\" for velocity / \"ACC\" for acceleration  <br>\n",
    "sample rate = 5000Hz<br>\n",
    "unwrap_phase = can wrap or unwrap the phase<br>\n",
    "outfile = save the plot<br></I>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel Response\n",
      "\tFrom M/S (Velocity in Meters per Second) to COUNTS (Digital Counts)\n",
      "\tOverall Sensitivity: 1500 defined at 1.000 Hz\n",
      "\t2 stages:\n",
      "\t\tStage 1: PolesZerosResponseStage from M/S to V, gain: 1500\n",
      "\t\tStage 2: CoefficientsTypeResponseStage from V to COUNTS, gain: 1\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "inv = read_inventory(\"RESP.XX.NS007..BHZ.CMG3T.120.1500.txt\")\n",
    "# print(inv)\n",
    "resp = inv[0][0][0].response\n",
    "print(resp)\n",
    "# resp.plot(0.0001, output=\"VEL\", start_stage=1, end_stage=2, sampling_rate=5000, unwrap_phase=True)\n",
    "\n",
    "bode_plot = resp.plot(0.0005, output=\"VEL\", sampling_rate=800,\n",
    "         unwrap_phase=False, label='CMG3T')\n",
    "bode_plot.savefig(\"RESP.XX.NS007..BHZ.CMG3T.120.1500.png\",dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>\n",
    "<h3 style=\"text-align:center;color:orange\">Alternative Method: Using Poles and Zeros <br>\n",
    "\n",
    "<I>If you have the sensor's or datalogger's transfer function in poles and zeros format, you can insert it into the following code and generate bode plots </I></h3>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<I><b> paz_to_freq_resp(poles, zeros, scale_fac, t_samp, nfft, freq=False)</b>\n",
    "\n",
    "Convert Poles and Zeros (PAZ) to frequency response.\n",
    "\n",
    "The output contains the frequency zero which is the offset of the trace.\n",
    "Parameters:\t\n",
    "\n",
    "1. poles (list of complex) The poles of the transfer function\n",
    "2. zeros (list of complex) The zeros of the transfer function\n",
    "3. scale_fac (float) Gain factor\n",
    "4. t_samp (float) Sampling interval in seconds\n",
    "5. nfft (int) Number of FFT points of signal which needs correction </I>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# insert the device ploes, zeros, and gain information here\n",
    "#Eg: CMG3T\n",
    "\n",
    "poles            = [-3.700800E-02+3.700800E-02j, -3.700800E-02-3.700800E-02j, -5.026500E+02+0j, \n",
    "                   -1.005000E+03+0j, -1.131000E+03+0j]\n",
    "zeros            = [0+0j, 0+0j]\n",
    "gain             = 5.71508E+08\n",
    "sensitivity      = 1.500000E+03\n",
    "sample_interval  = 0.0001\n",
    "nfft             = 65536*1024*2\n",
    "freq             = True # False\n",
    "\n",
    "h, f = paz_to_freq_resp(poles, zeros, gain*sensitivity, sample_interval, nfft, freq)\n",
    "\n",
    "plt.figure(figsize=(8, 6), dpi=300)\n",
    "plt.subplot(211)\n",
    "plt.loglog(f, abs(h))\n",
    "plt.xlabel('Frequency [Hz]')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.xlim(5*10**-4, 5*10**2)\n",
    "plt.ylim(10**-1, 10**4)\n",
    "plt.grid()\n",
    "plt.legend(['CMG3T'])\n",
    "\n",
    "plt.subplot(212)\n",
    "phase = np.unwrap(np.angle(h))\n",
    "plt.semilogx(f, phase)\n",
    "plt.xlabel('Frequency [Hz]')\n",
    "plt.ylabel('Phase [rad]')\n",
    "plt.xlim(5*10**-4, 5*10**2)\n",
    "plt.grid()\n",
    "\n",
    "plt.yticks(\n",
    "    [0, np.pi / 2, np.pi, 3 * np.pi / 2, 2 * np.pi,  -np.pi / 2, -np.pi, -3 * np.pi / 2, -2 * np.pi],\n",
    "    ['$0$', r'$\\frac{\\pi}{2}$', r'$\\pi$', r'$\\frac{3\\pi}{2}$', r'$2\\pi$',   \n",
    "     r'$\\frac{\\pi}{2}$', r'$\\pi$', r'$\\frac{3\\pi}{2}$', r'$2\\pi$'])\n",
    "plt.ylim(-2*np.pi - 0.2, 2*np.pi + 0.2)\n",
    "\n",
    "plt.suptitle('Frequency Response')\n",
    "plt.subplots_adjust(wspace=0.3)\n",
    "# plt.show()\n",
    "\n",
    "plt.savefig(\"RESP.poles.zeros.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=\"text-align:center\">B: Nanometrics Titan Accelerometer </h3>\n",
    "<h3>Titan 0-0.002s / 1.02 V/M/S**2</h3>\n",
    "\n",
    "Link: http://ds.iris.edu/NRL/sensors/nanometrics/nanometrics_titan_sensors.htm <br>\n",
    "File: http://ds.iris.edu/NRL/sensors/nanometrics/RESP.XX.NS128..BNZ.Titan.DC_430.20V.2G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel Response\n",
      "\tFrom M/S**2 (Acceleration in Meters Per Second Per Second) to COUNTS (Digital Counts)\n",
      "\tOverall Sensitivity: 1.02 defined at 1.000 Hz\n",
      "\t2 stages:\n",
      "\t\tStage 1: PolesZerosResponseStage from M/S**2 to V, gain: 1.02\n",
      "\t\tStage 2: CoefficientsTypeResponseStage from V to COUNTS, gain: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "inv = read_inventory(\"RESP.XX.NS128..BNZ.Titan.DC_430.20V.2G.txt\")\n",
    "# print(inv)\n",
    "resp = inv[0][0][0].response\n",
    "print(resp)\n",
    "# resp.plot(0.0001, output=\"ACC\", start_stage=1, end_stage=2, sampling_rate=20000, unwrap_phase=True)\n",
    "\n",
    "bode_plot = resp.plot(0.5, output=\"ACC\", sampling_rate=200,\n",
    "         unwrap_phase=True, plot_degrees=True, label='Titan')\n",
    "\n",
    "bode_plot.savefig(\"RESP.XX.NS128..BNZ.Titan.DC_430.20V.2G.png\", dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=\"text-align:center\">C: Streckeisen, STS-2, generation 3 </h3>\n",
    "<h3>120 s to 0.00833 Hz\t/ 1500 V/M/S</h3>\n",
    "\n",
    "Link: http://ds.iris.edu/NRL/sensors/streckeisen/streckeisen_sts2_sensors.htm <br>\n",
    "File: http://ds.iris.edu/NRL/sensors/streckeisen/RESP.XX.NS085..BHZ.STS2_gen3.120.1500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel Response\n",
      "\tFrom M/S (Velocity in Meters per Second) to COUNTS (Digital Counts)\n",
      "\tOverall Sensitivity: 1500 defined at 1.000 Hz\n",
      "\t2 stages:\n",
      "\t\tStage 1: PolesZerosResponseStage from M/S to V, gain: 1500\n",
      "\t\tStage 2: CoefficientsTypeResponseStage from V to COUNTS, gain: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "inv = read_inventory(\"RESP.XX.NS085..BHZ.STS2_gen3.120.1500.txt\")\n",
    "# print(inv)\n",
    "resp = inv[0][0][0].response\n",
    "print(resp)\n",
    "# resp.plot(0.0001, output=\"VEL\", start_stage=1, end_stage=2, sampling_rate=5000, unwrap_phase=True)\n",
    "\n",
    "bode_plot = resp.plot(0.0005, output=\"VEL\", sampling_rate=200,\n",
    "         unwrap_phase=True, label='STS2')\n",
    "\n",
    "bode_plot.savefig(\"RESP.XX.NS085..BHZ.STS2_gen3.120.1500.png\", dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>============================================================================================================<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style=\"text-align:center;color:Blue\"> 2. Bode Plots for Dataloggers</h2><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=\"text-align:center\">A: Q330 </h3><br>\n",
    "<h3>Quanterra / Q330SR / 1 / 200Hz / Linear Filters all rates</h3>\n",
    "\n",
    "\n",
    "Link: http://ds.iris.edu/NRL/dataloggers/quanterra/quanterra_Q330SR_dataloggers.htm <br>\n",
    "File: http://ds.iris.edu/NRL/dataloggers/quanterra/RESP.XX.NQ007..HHZ.Q330.SR.1.200.all\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel Response\n",
      "\tFrom M/S (Velocity in Meters per Second) to COUNTS (Digital Counts)\n",
      "\tOverall Sensitivity: 419430 defined at 0.050 Hz\n",
      "\t4 stages:\n",
      "\t\tStage 1: PolesZerosResponseStage from M/S to V, gain: 1\n",
      "\t\tStage 2: ResponseStage from V to V, gain: 1\n",
      "\t\tStage 3: CoefficientsTypeResponseStage from V to COUNTS, gain: 419430\n",
      "\t\tStage 4: CoefficientsTypeResponseStage from COUNTS to COUNTS, gain: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "inv = read_inventory(\"RESP.XX.NQ007..HHZ.Q330.SR.1.200.all\")\n",
    "# print(inv)\n",
    "resp = inv[0][0][0].response\n",
    "print(resp)\n",
    "# resp.plot(0.0001, output=\"VEL\", start_stage=1, end_stage=2, sampling_rate=5000, unwrap_phase=True)\n",
    "\n",
    "bode_plot = resp.plot(0.0001, output=\"VEL\", sampling_rate=200,\n",
    "          unwrap_phase=True, label='Q330')\n",
    "\n",
    "bode_plot.savefig(\"RESP.XX.NQ007..HHZ.Q330.SR.1.200.all.png\", dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=\"text-align:center\">B: Centaur </h3><br>\n",
    "<h3>Nanometrics\tCentaur\t40Vpp / gain = 1 / 200Hz / IIR = Off / FIR = Linear </h3>\n",
    "\n",
    "Link: http://ds.iris.edu/NRL/dataloggers/nanometrics/centaur/nanometrics_centaur_dataloggers.htm <br>\n",
    "File: http://ds.iris.edu/NRL/dataloggers/nanometrics/centaur/RESP.XX.NN429..HHZ.CENTAUR.1.200.OFF.LP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel Response\n",
      "\tFrom M/S (Velocity in Meters per Second) to COUNTS (Digital Counts)\n",
      "\tOverall Sensitivity: 400000 defined at 1.000 Hz\n",
      "\t6 stages:\n",
      "\t\tStage 1: PolesZerosResponseStage from M/S to V, gain: 1\n",
      "\t\tStage 2: PolesZerosResponseStage from V to V, gain: 1\n",
      "\t\tStage 3: CoefficientsTypeResponseStage from V to COUNTS, gain: 400000\n",
      "\t\tStage 4: CoefficientsTypeResponseStage from COUNTS to COUNTS, gain: 1\n",
      "\t\tStage 5: CoefficientsTypeResponseStage from COUNTS to COUNTS, gain: 1\n",
      "\t\tStage 6: CoefficientsTypeResponseStage from COUNTS to COUNTS, gain: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "inv = read_inventory(\"RESP.XX.NN429..HHZ.CENTAUR.1.200.OFF.LP\")\n",
    "# print(inv)\n",
    "resp = inv[0][0][0].response\n",
    "print(resp)\n",
    "# resp.plot(0.0001, output=\"VEL\", start_stage=1, end_stage=2, sampling_rate=5000, unwrap_phase=True)\n",
    "\n",
    "bode_plot = resp.plot(0.0001, output=\"VEL\", sampling_rate=200,\n",
    "          unwrap_phase=True, label='Centaur')\n",
    "\n",
    "bode_plot.savefig(\"RESP.XX.NN429..HHZ.CENTAUR.1.200.OFF.LP.png\", dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=\"text-align:center\">C: RT130 </h3><br>\n",
    "<h3>REF TEK / 130-01 / gain = 1 / 200 Hz </h3>\n",
    "\n",
    "Link: http://ds.iris.edu/NRL/dataloggers/reftek/reftek_130_dataloggers.html#130-01 <br>\n",
    "File: http://ds.iris.edu/NRL/dataloggers/reftek/RESP.XX.NR010..HHZ.130.1.200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel Response\n",
      "\tFrom M/S (Velocity in Meters per Second) to COUNTS (Digital Counts)\n",
      "\tOverall Sensitivity: 629129 defined at 0.050 Hz\n",
      "\t10 stages:\n",
      "\t\tStage 1: PolesZerosResponseStage from M/S to V, gain: 1\n",
      "\t\tStage 2: ResponseStage from V to V, gain: 1\n",
      "\t\tStage 3: CoefficientsTypeResponseStage from V to COUNTS, gain: 629129\n",
      "\t\tStage 4: CoefficientsTypeResponseStage from COUNTS to COUNTS, gain: 1\n",
      "\t\tStage 5: CoefficientsTypeResponseStage from COUNTS to COUNTS, gain: 1\n",
      "\t\tStage 6: CoefficientsTypeResponseStage from COUNTS to COUNTS, gain: 1\n",
      "\t\tStage 7: CoefficientsTypeResponseStage from COUNTS to COUNTS, gain: 1\n",
      "\t\tStage 8: CoefficientsTypeResponseStage from COUNTS to COUNTS, gain: 1\n",
      "\t\tStage 9: CoefficientsTypeResponseStage from COUNTS to COUNTS, gain: 1\n",
      "\t\tStage 10: CoefficientsTypeResponseStage from COUNTS to COUNTS, gain: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "inv = read_inventory(\"RESP.XX.NR010..HHZ.130.1.200\")\n",
    "# print(inv)\n",
    "resp = inv[0][0][0].response\n",
    "print(resp)\n",
    "# resp.plot(0.0001, output=\"VEL\", start_stage=1, end_stage=2, sampling_rate=5000, unwrap_phase=True)\n",
    "\n",
    "bode_plot = resp.plot(0.0001, output=\"VEL\", sampling_rate=200,\n",
    "          unwrap_phase=True, label='RT130')\n",
    "\n",
    "bode_plot.savefig(\"RESP.XX.NR010..HHZ.130.1.200.png\", dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------<br><br>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}