├── README.md ├── LICENSE └── EIS_2020_04_12.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # Electrochemical-Impedance-Spectroscopy 2 | Building equivalent circuits and working up EIS data using Python 3 | 4 | ## Electrochemical Impedance Spectroscopy 5 | 6 | Electrochemical Impedance Spectroscopy, or EIS for short, is an analytical technique for probing the inner workings of an electrochemical system like a battery or fuel cell. In principle, it could also be used for any unknown electrical circuit. It works by introducing unit impulses to the circuit at a range of frequencies and measuring the impedance. 7 | 8 | Gamry, a manufacturer of potentiostats that can be used to perform EIS measurements, [has a good primer on the subject](https://www.gamry.com/application-notes/EIS/basics-of-electrochemical-impedance-spectroscopy/). 9 | 10 | I'm also familiar with the math behind EIS by a background in [Control Theory](https://en.wikipedia.org/wiki/Control_theory) and didn't learn it as an electrochemist might. So the wording may make more sense to an electrical engineer or chemical engineer than it might an electrochemist (who is more likely to use it). 11 | 12 | The short explaination is an equivalent circuit is set up with several individual circuit elements and the liberal application of [Ohm's Law](https://en.wikipedia.org/wiki/Ohm%27s_law) and worked up using either Laplace Transformations (as I do here) or Fourier Transformations (a special case of Laplace Transformations and how most EIS literature does it). The impedance response is a complex number. 13 | 14 | The advantage to doing it in the frequency domain is all the math is algebra and all the complicated dynamics of the differential equations are removed by the transform. Working with the data as Laplace Transforms also allows (usually) convenient conversion between frequency and time domains. 15 | 16 | A [Gamry Interface 1010E Potentiostat](https://www.gamry.com/potentiostats/interface-1010e-potentiostat/) (the machine I'm most familiar with) has a range of 2 MHz to 10 μHz. Low frequencies take a long time to run in practice, but may be necessary to probe things like diffusion. 17 | 18 | Here I'm going to: 19 | * Set up Python classes for common circuit elements 20 | * Set up a Python class for an Equivalent Circuit made of of those individual elements 21 | * Plot Nyquist and Bode diagrams for example data 22 | * Return those data as a pandas dataframe 23 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /EIS_2020_04_12.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Electrochemical Impedance Spectroscopy\n", 8 | "\n", 9 | "Electrochemical Impedance Spectroscopy, or EIS for short, is an analytical technique for probing the inner workings of an electrochemical system like a battery or fuel cell. In principle, it could also be used for any unknown electrical circuit. It works by introducing unit impulses to the circuit at a range of frequencies and measuring the impedance.\n", 10 | "\n", 11 | "Gamry, a manufacturer of potentiostats that can be used to perform EIS measurements, [has a good primer on the subject](https://www.gamry.com/application-notes/EIS/basics-of-electrochemical-impedance-spectroscopy/).\n", 12 | "\n", 13 | "I'm also familiar with the math behind EIS by a background in [Control Theory](https://en.wikipedia.org/wiki/Control_theory) and didn't learn it as an electrochemist might. So the wording may make more sense to an electrical engineer or chemical engineer than it might an electrochemist (who is more likely to use it).\n", 14 | "\n", 15 | "The short explanation is an equivalent circuit is set up with several individual circuit elements and the liberal application of [Ohm's Law](https://en.wikipedia.org/wiki/Ohm%27s_law) and worked up using either Laplace Transformations (as I do here) or Fourier Transformations (a special case of Laplace Transformations and how most EIS literature does it). The impedance response is a complex number.\n", 16 | "\n", 17 | "The advantage to doing it in the frequency domain is all the math is algebra and all the complicated dynamics of the differential equations are removed by the transform. Working with the data as Laplace Transforms also allows (usually) convenient conversion between frequency and time domains.\n", 18 | "\n", 19 | "A [Gamry Interface 1010E Potentiostat](https://www.gamry.com/potentiostats/interface-1010e-potentiostat/) (the machine I'm most familiar with) has a range of 2 MHz to 10 μHz. Low frequencies take a long time to run in practice, but may be necessary to probe things like diffusion. \n", 20 | "\n", 21 | "Here I'm going to: \n", 22 | "* Set up Python classes for common circuit elements\n", 23 | "* Set up a Python class for an Equivalent Circuit made of of those individual elements\n", 24 | "* Plot Nyquist and Bode diagrams for example data\n", 25 | "* Return those data as a pandas dataframe" 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "metadata": {}, 31 | "source": [ 32 | "First we need to import a couple libraries:" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": 1, 38 | "metadata": {}, 39 | "outputs": [], 40 | "source": [ 41 | "import numpy as np\n", 42 | "import pandas as pd\n", 43 | "import matplotlib.pyplot as plt" 44 | ] 45 | }, 46 | { 47 | "cell_type": "markdown", 48 | "metadata": {}, 49 | "source": [ 50 | "Next let's define the frequency range from 10 μHz to 0.1 MHz:" 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": 2, 56 | "metadata": {}, 57 | "outputs": [ 58 | { 59 | "data": { 60 | "text/plain": [ 61 | "array([1.00000000e+05, 7.94328235e+04, 6.30957344e+04, 5.01187234e+04,\n", 62 | " 3.98107171e+04, 3.16227766e+04, 2.51188643e+04, 1.99526231e+04,\n", 63 | " 1.58489319e+04, 1.25892541e+04, 1.00000000e+04, 7.94328235e+03,\n", 64 | " 6.30957344e+03, 5.01187234e+03, 3.98107171e+03, 3.16227766e+03,\n", 65 | " 2.51188643e+03, 1.99526231e+03, 1.58489319e+03, 1.25892541e+03,\n", 66 | " 1.00000000e+03, 7.94328235e+02, 6.30957344e+02, 5.01187234e+02,\n", 67 | " 3.98107171e+02, 3.16227766e+02, 2.51188643e+02, 1.99526231e+02,\n", 68 | " 1.58489319e+02, 1.25892541e+02, 1.00000000e+02, 7.94328235e+01,\n", 69 | " 6.30957344e+01, 5.01187234e+01, 3.98107171e+01, 3.16227766e+01,\n", 70 | " 2.51188643e+01, 1.99526231e+01, 1.58489319e+01, 1.25892541e+01,\n", 71 | " 1.00000000e+01, 7.94328235e+00, 6.30957344e+00, 5.01187234e+00,\n", 72 | " 3.98107171e+00, 3.16227766e+00, 2.51188643e+00, 1.99526231e+00,\n", 73 | " 1.58489319e+00, 1.25892541e+00, 1.00000000e+00, 7.94328235e-01,\n", 74 | " 6.30957344e-01, 5.01187234e-01, 3.98107171e-01, 3.16227766e-01,\n", 75 | " 2.51188643e-01, 1.99526231e-01, 1.58489319e-01, 1.25892541e-01,\n", 76 | " 1.00000000e-01, 7.94328235e-02, 6.30957344e-02, 5.01187234e-02,\n", 77 | " 3.98107171e-02, 3.16227766e-02, 2.51188643e-02, 1.99526231e-02,\n", 78 | " 1.58489319e-02, 1.25892541e-02, 1.00000000e-02, 7.94328235e-03,\n", 79 | " 6.30957344e-03, 5.01187234e-03, 3.98107171e-03, 3.16227766e-03,\n", 80 | " 2.51188643e-03, 1.99526231e-03, 1.58489319e-03, 1.25892541e-03,\n", 81 | " 1.00000000e-03, 7.94328235e-04, 6.30957344e-04, 5.01187234e-04,\n", 82 | " 3.98107171e-04, 3.16227766e-04, 2.51188643e-04, 1.99526231e-04,\n", 83 | " 1.58489319e-04, 1.25892541e-04, 1.00000000e-04, 7.94328235e-05,\n", 84 | " 6.30957344e-05, 5.01187234e-05, 3.98107171e-05, 3.16227766e-05,\n", 85 | " 2.51188643e-05, 1.99526231e-05, 1.58489319e-05, 1.25892541e-05,\n", 86 | " 1.00000000e-05])" 87 | ] 88 | }, 89 | "execution_count": 2, 90 | "metadata": {}, 91 | "output_type": "execute_result" 92 | } 93 | ], 94 | "source": [ 95 | "myFrequencies = np.logspace(5,-5,num=101)\n", 96 | "myFrequencies" 97 | ] 98 | }, 99 | { 100 | "cell_type": "markdown", 101 | "metadata": {}, 102 | "source": [ 103 | "Now classes for the following circuit elements:\n", 104 | "* Resistors\n", 105 | "* Capacitors\n", 106 | "* Inductors\n", 107 | "* Resistor-Capacity (RC) units\n", 108 | "* Warburg elements (diffusion)\n", 109 | "* Constant Phase Elements (diffusion at an interface)\n", 110 | "\n", 111 | "For each class, there are 3 methods:\n", 112 | "* A constructor, where the physical characteristics are assigned (like resistance or capacitance)\n", 113 | "* An impulse, as if we ran that element through a range of frequencies and measured the impedance\n", 114 | "* The voltage change response that element would have in voltage to a step-change in current\n", 115 | "\n", 116 | "I'm not going to demonstrate the current step here, but it's available." 117 | ] 118 | }, 119 | { 120 | "cell_type": "code", 121 | "execution_count": 3, 122 | "metadata": {}, 123 | "outputs": [], 124 | "source": [ 125 | "class Resistor:\n", 126 | " def __init__(self, R):\n", 127 | " self.ImpulseSet = False\n", 128 | " self.R = R\n", 129 | " \n", 130 | " def impulse(self, Frequencies):\n", 131 | " self.ImpulseSet = True\n", 132 | " s = np.array(Frequencies)\n", 133 | " numFrequencies = len(Frequencies)\n", 134 | " self.Z = self.R * np.ones(numFrequencies)\n", 135 | " \n", 136 | " def CurrentStep(self, i, t):\n", 137 | " self.I = i\n", 138 | " self.t = t\n", 139 | " self.V = i * self.R * np.ones(len(t))\n", 140 | " \n", 141 | "class Capacitor:\n", 142 | " def __init__(self, C):\n", 143 | " self.ImpulseSet = False\n", 144 | " self.C = C\n", 145 | " \n", 146 | " def impulse(self, Frequencies):\n", 147 | " self.ImpulseSet = True\n", 148 | " s = np.array(Frequencies)\n", 149 | " numFrequencies = len(Frequencies)\n", 150 | " self.Z = (1/(s*self.C))\n", 151 | " \n", 152 | " def CurrentStep(self, i, t):\n", 153 | " self.I = i\n", 154 | " self.t = t\n", 155 | " self.V = i * t / self.C\n", 156 | " \n", 157 | "class Inductor:\n", 158 | " def __init__(self, L):\n", 159 | " self.ImpulseSet = False\n", 160 | " self.L = L\n", 161 | " \n", 162 | " def impulse(self, Frequencies):\n", 163 | " self.ImpulseSet = True\n", 164 | " s = np.array(Frequencies)\n", 165 | " numFrequencies = len(Frequencies)\n", 166 | " self.Z = s*self.L\n", 167 | " \n", 168 | " def CurrentStep(self, i, t):\n", 169 | " self.I = i\n", 170 | " self.t = t\n", 171 | " self.V = i * self.L * t\n", 172 | " \n", 173 | "class RC:\n", 174 | " def __init__(self, R, C):\n", 175 | " self.ImpulseSet = False\n", 176 | " self.R = R\n", 177 | " self.C = C\n", 178 | " self.Tau = R*C\n", 179 | " \n", 180 | " def impulse(self, Frequencies):\n", 181 | " self.ImpulseSet = True\n", 182 | " s = np.array(Frequencies)\n", 183 | " numFrequencies = len(Frequencies)\n", 184 | " self.Z = self.R / (self.Tau*s+1)\n", 185 | " \n", 186 | " def CurrentStep(self, i, t):\n", 187 | " self.I = i\n", 188 | " self.t = t\n", 189 | " self.V = i*self.R - i*self.R*np.exp(-t/self.Tau)\n", 190 | " \n", 191 | "class Warburg:\n", 192 | " def __init__(self, W):\n", 193 | " self.ImpulseSet = False\n", 194 | " self.W = W\n", 195 | " \n", 196 | " def impulse(self, Frequencies):\n", 197 | " self.ImpulseSet = True\n", 198 | " s = np.array(Frequencies)\n", 199 | " numFrequencies = len(Frequencies)\n", 200 | " self.Z = self.W / np.sqrt(s)\n", 201 | " \n", 202 | " def CurrentStep(self, i, t):\n", 203 | " self.I = i\n", 204 | " self.t = t\n", 205 | " self.V = 2*i*self.W*np.sqrt(t)/np.sqrt(np.pi)\n", 206 | " \n", 207 | "class ConstantPhaseElement:\n", 208 | " def __init__(self, A, alpha):\n", 209 | " self.ImpulseSet = False\n", 210 | " self.A = A\n", 211 | " self.alpha = alpha\n", 212 | " \n", 213 | " def impulse(self, Frequencies):\n", 214 | " self.ImpulseSet = True\n", 215 | " s = np.array(Frequencies)\n", 216 | " numFrequencies = len(Frequencies)\n", 217 | " self.Z = 1/(self.A*(s**(self.alpha)))\n", 218 | " \n", 219 | " def CurrentStep(self, i, t):\n", 220 | " self.I = i\n", 221 | " self.t = t\n", 222 | " self.V = i*(t**self.alpha)/(self.A*scipy.special.gamma(self.alpha+1))\n" 223 | ] 224 | }, 225 | { 226 | "cell_type": "markdown", 227 | "metadata": {}, 228 | "source": [ 229 | "The Equivalent Circuit class then has the following methods:\n", 230 | "* A contructor\n", 231 | "* A way to set the impulse frequencies for the entire equivalent circuit\n", 232 | "* A way to add elements in both series and parallel\n", 233 | "* A way to plot Nyquist Diagrams, Bode Diagrams, or both\n", 234 | "* A way to return the data as a pandas dataframe, for further data manipulation " 235 | ] 236 | }, 237 | { 238 | "cell_type": "code", 239 | "execution_count": 4, 240 | "metadata": {}, 241 | "outputs": [], 242 | "source": [ 243 | "class EquivalentCircuit():\n", 244 | " def __init__(self):\n", 245 | " self.ElementTypes = (Resistor, Capacitor, RC, Warburg, ConstantPhaseElement)\n", 246 | " self.PlotWidth = 10\n", 247 | " self.Z = 0j\n", 248 | " \n", 249 | " def setImpulseFrequencies(self, Frequencies):\n", 250 | " self.Frequencies = Frequencies\n", 251 | "\n", 252 | " def addSeriesElement(self, *elements):\n", 253 | " for element in elements:\n", 254 | " if isinstance(element, self.ElementTypes):\n", 255 | " element.impulse(self.Frequencies * 1j)\n", 256 | " self.Z += element.Z\n", 257 | " elif isinstance(element, EquivalentCircuit):\n", 258 | " self.Z += elementZ\n", 259 | " else:\n", 260 | " raise ValueError(f'{element} is not a circuit element')\n", 261 | " self.__BodeTerms__()\n", 262 | " \n", 263 | " def addParallelElement(self, *elements):\n", 264 | " invZ = 0\n", 265 | " for element in elements:\n", 266 | " if isinstance(element, self.ElementTypes):\n", 267 | " element.impulse(self.Frequencies * 1j)\n", 268 | " invZ += 1/element.Z\n", 269 | " elif isinstance(element, EquivalentCircuit):\n", 270 | " invZ += 1/element.Z\n", 271 | " else:\n", 272 | " raise ValueError(f'{element} is not a circuit element')\n", 273 | " self.Z = 1 / invZ\n", 274 | " \n", 275 | " def __BodeTerms__(self):\n", 276 | " self.magnitude = 20*np.log10(np.sqrt(np.real(self.Z)**2 + np.imag(self.Z)**2))\n", 277 | " self.phase = np.arctan(np.imag(self.Z)/np.real(self.Z))*180/np.pi\n", 278 | " \n", 279 | " def plotNyquist(self):\n", 280 | " plt.figure(figsize=(self.PlotWidth, self.PlotWidth))\n", 281 | " plt.plot(np.real(self.Z), -np.imag(self.Z))\n", 282 | " maxValue = 1.05 * np.maximum(np.max(np.abs(np.real(self.Z))), np.max(np.abs(np.imag(self.Z))))\n", 283 | " plt.xlabel('Z real')\n", 284 | " plt.ylabel('-Z imag')\n", 285 | " plt.xlim(0, maxValue)\n", 286 | " plt.ylim(0, maxValue)\n", 287 | " plt.grid()\n", 288 | " plt.title('Nyquist Diagram')\n", 289 | " plt.show()\n", 290 | " \n", 291 | " def plotBode(self):\n", 292 | " f, (BodeMagnitudePlot, BodePhasePlot) = plt.subplots(2, 1)\n", 293 | " f.set_size_inches((self.PlotWidth, self.PlotWidth))\n", 294 | " BodeMagnitudePlot.set_title('Bode Diagram')\n", 295 | " BodeMagnitudePlot.semilogx(self.Frequencies, self.magnitude)\n", 296 | " BodeMagnitudePlot.set_xlabel('Frequency (Hz)')\n", 297 | " BodeMagnitudePlot.set_ylabel('Magnitude (dB)')\n", 298 | " BodeMagnitudePlot.set_ylim(0,1.05*np.max(someEquivalentCircuit.magnitude))\n", 299 | " BodeMagnitudePlot.grid()\n", 300 | "\n", 301 | " BodePhasePlot.semilogx(self.Frequencies, -self.phase)\n", 302 | " BodePhasePlot.set_xlabel('Frequency (Hz)')\n", 303 | " BodePhasePlot.set_ylabel('-Phase (degrees)')\n", 304 | " BodePhasePlot.set_ylim(-90,90)\n", 305 | " BodePhasePlot.grid()\n", 306 | " BodePhasePlot.set_yticks(np.linspace(-90, 90, 13))\n", 307 | " plt.show()\n", 308 | " \n", 309 | " def plotAll(self):\n", 310 | " self.plotNyquist()\n", 311 | " self.plotBode()\n", 312 | " \n", 313 | " def to_dataframe(self):\n", 314 | " cols = ['Frequency (Hz)', 'Z (Ohms)', 'Zreal', 'Zimag', 'Radius (Ohms)', 'Magnitude (dB)', 'Phase (degrees)']\n", 315 | " df = pd.DataFrame(columns=cols)\n", 316 | " df['Frequency (Hz)'] = self.Frequencies\n", 317 | " df['Z (Ohms)'] = self.Z\n", 318 | " df['Zreal'] = np.real(self.Z)\n", 319 | " df['Zimag'] = np.imag(self.Z)\n", 320 | " df['Radius (Ohms)'] = np.sqrt(np.real(self.Z)**2 + np.imag(self.Z)**2)\n", 321 | " df['Magnitude (dB)'] = 20*np.log10(np.sqrt(np.real(self.Z)**2 + np.imag(self.Z)**2))\n", 322 | " df['Phase (degrees)'] = np.arctan(np.imag(self.Z)/np.real(self.Z))*180/np.pi\n", 323 | " self.df = df\n", 324 | " return df" 325 | ] 326 | }, 327 | { 328 | "cell_type": "markdown", 329 | "metadata": {}, 330 | "source": [ 331 | "To demonstrate how this works, let's set up an equivalent circuit containing 4 elements:\n", 332 | "* A resistor with a resistance of 40 ohms\n", 333 | "* A Resistor-Capacitor element with a resistor of 90 ohms and capacitance of 500 μF\n", 334 | "* A second Resistor-Capacitor element with a resistor of 200 ohms and capacitance of 30 μF\n", 335 | "* A warburg element with a value of 1" 336 | ] 337 | }, 338 | { 339 | "cell_type": "code", 340 | "execution_count": 5, 341 | "metadata": {}, 342 | "outputs": [], 343 | "source": [ 344 | "someR = Resistor(40)\n", 345 | "someRC1 = RC(90,.005)\n", 346 | "someRC2 = RC(200,.00003)\n", 347 | "someWarburg = Warburg(1)" 348 | ] 349 | }, 350 | { 351 | "cell_type": "code", 352 | "execution_count": 6, 353 | "metadata": {}, 354 | "outputs": [], 355 | "source": [ 356 | "someEquivalentCircuit = EquivalentCircuit()\n", 357 | "someEquivalentCircuit.setImpulseFrequencies(myFrequencies)\n", 358 | "someEquivalentCircuit.addSeriesElement(someR, someRC1, someRC2, someWarburg)" 359 | ] 360 | }, 361 | { 362 | "cell_type": "markdown", 363 | "metadata": {}, 364 | "source": [ 365 | "Plots for the Nyquist and Bode digrams:" 366 | ] 367 | }, 368 | { 369 | "cell_type": "code", 370 | "execution_count": 7, 371 | "metadata": {}, 372 | "outputs": [ 373 | { 374 | "data": { 375 | "image/png": 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\n", 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\n", 388 | "text/plain": [ 389 | "
" 390 | ] 391 | }, 392 | "metadata": { 393 | "needs_background": "light" 394 | }, 395 | "output_type": "display_data" 396 | } 397 | ], 398 | "source": [ 399 | "someEquivalentCircuit.plotAll()" 400 | ] 401 | }, 402 | { 403 | "cell_type": "markdown", 404 | "metadata": {}, 405 | "source": [ 406 | "Returning the equivalent circuit values as a pandas dataframe:" 407 | ] 408 | }, 409 | { 410 | "cell_type": "code", 411 | "execution_count": 8, 412 | "metadata": {}, 413 | "outputs": [ 414 | { 415 | "data": { 416 | "text/html": [ 417 | "
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| " \n", 1043 | " \n", 1044 | " \n", 1045 | " \n", 1046 | " \n", 1047 | " \n", 1048 | " \n", 1049 | " \n", 1050 | " \n", 1051 | " \n", 1052 | " \n", 1053 | " \n", 1054 | " \n", 1055 | " \n", 1056 | "
Frequency (Hz)Z (Ohms)ZrealZimagRadius (Ohms)Magnitude (dB)Phase (degrees)
0100000.000000(40.00279166643429-0.3375684753864916j)40.002792-0.33756840.00421632.042115-0.483486
179432.823472(40.00338947231721-0.4246667168318618j)40.003389-0.42466740.00564332.042425-0.608216
263095.734448(40.004210637163794-0.5342788738489503j)40.004211-0.53427940.00777832.042889-0.765172
350118.723363(40.00537038895218-0.6722291381058704j)40.005370-0.67222940.01101832.043592-0.962677
439810.717055(40.00704946648614-0.8458485042528762j)40.007049-0.84584940.01599032.044671-1.211195
531622.776602(40.00953219932713-1.0643641827870147j)40.009532-1.06436440.02368732.046342-1.523867
625118.864315(40.01326682112679-1.3393891679585193j)40.013267-1.33938940.03567832.048944-1.917182
719952.623150(40.01896100003028-1.685537230252724j)40.018961-1.68553740.05444132.053014-2.411785
815848.931925(40.02773313765615-2.1211944873617696j)40.027733-2.12119440.08389832.059399-3.033445
912589.254118(40.04135194332757-2.6694854615013828j)40.041352-2.66948540.13023832.069435-3.814163
1010000.000000(40.062615639998384-3.3594787313618397j)40.062616-3.35947940.20322532.085218-4.793363
117943.282347(40.09595178772775-4.227683762034331j)40.095952-4.22768440.31821732.110026-6.018980
126309.573445(40.14836505502175-5.319893507795863j)40.148365-5.31989440.49929032.148948-7.548056
135011.872336(40.23093218061143-6.693421003832081j)40.230932-6.69342140.78394032.209784-9.446061
143981.071706(40.36115349979036-8.419750110111554j)40.361153-8.41975041.23002432.304272-11.783494
153162.277660(40.566635399217944-10.587546150456358j)40.566635-10.58754641.92550632.449566-14.627414
162511.886432(40.890815915692784-13.305803123318627j)40.890816-13.30580343.00120032.669612-18.024801
171995.262315(41.401764722896374-16.706549131792872j)41.401765-16.70654944.64543532.995541-21.975191
181584.893192(42.205454444064166-20.94582623061118j)42.205454-20.94582647.11717333.463585-26.394398
191258.925412(43.46514991198426-26.200333033837637j)43.465150-26.20033350.75112534.108914-31.081129
201000.000000(45.42821052743007-32.654792124557986j)45.428211-32.65479255.94691934.955524-35.709407
21794.328235(48.459464878915156-40.47149206187026j)48.459465-40.47149263.13684736.005658-39.867342
22630.957344(53.07400008014952-49.729146723262865j)53.074000-49.72914772.73126937.234423-43.136454
23501.187234(59.948136979411316-60.31683796120883j)59.948137-60.31683885.04057938.592524-45.175653
24398.107171(69.86399177355301-71.78086655518706j)69.863992-71.780867100.16721140.014512-45.775334
25316.227766(83.52246863098011-83.1663876681218j)83.522469-83.166388117.86709041.427851-44.877605
26251.188643(101.18669609670077-92.97932979560281j)101.186696-92.979330137.41871542.760918-42.579558
27199.526231(122.25798583184859-99.45477278432047j)122.257986-99.454773157.60160843.951213-39.127762
28158.489319(145.10047817637712-101.19141966624632j)145.100478-101.191420176.90068544.954590-34.891479
29125.892541(167.4340424383373-97.84037649429646j)167.434042-97.840376193.92498045.752675-30.299955
........................
710.007943(337.9327182171763-8.265099340082763j)337.932718-8.265099338.03377650.579202-1.401051
720.006310(338.90122112577086-9.165054109470361j)338.901221-9.165054339.02512650.604638-1.549097
730.005012(339.9876907950041-10.19714280877964j)339.987691-10.197143340.14057650.633169-1.717939
740.003981(341.20659827848687-11.372897411112348j)341.206598-11.372897341.39608350.665171-1.909042
750.003162(342.5741519751984-12.706201015911235j)342.574152-12.706201342.80971050.701062-2.124148
760.002512(344.1085200943466-14.213380665817024j)344.108520-14.213381344.40193650.741312-2.365254
770.001995(345.8300763990363-15.913351356062485j)345.830076-15.913351346.19600950.786441-2.634604
780.001585(347.76167349571654-17.827809306384747j)347.761673-17.827809348.21834050.837033-2.934667
790.001259(349.9289479306948-19.981474000083242j)349.928948-19.981474350.49897050.893735-3.268127
800.001000(352.36066154280155-22.402379766796606j)352.360662-22.402380353.07209250.957268-3.637854
810.000794(355.08908385454527-25.122218841561928j)355.089084-25.122219355.97666151.028431-4.046879
820.000631(358.1504207353678-28.176738912941524j)358.150421-28.176739359.25708451.108107-4.498361
830.000501(361.5852951257499-31.606199212081016j)361.585295-31.606199362.96401751.197271-4.995532
840.000398(365.4392862645882-35.45589022269169j)365.439286-35.455890367.15527051.296995-5.541644
850.000316(369.7635346151326-39.77672313593609j)369.763535-39.776723371.89684051.408450-6.139891
860.000251(374.61542054176607-44.6258962584295j)374.615421-44.625896377.26407751.532909-6.793321
870.000200(380.0593257592085-50.06764672883367j)380.059326-50.067647383.34300651.671751-7.504725
880.000158(386.16748766817574-56.174097130727866j)386.167488-56.174097390.23180051.826453-8.276515
890.000126(393.02095792036346-63.02620792827521j)393.020958-63.026208398.04243051.998587-9.110582
900.000100(400.7106779363328-70.71484811864656j)400.710678-70.714848406.90249152.189807-10.008146
910.000079(409.3386856502992-79.34199811407133j)409.338686-79.341998416.95720752.401830-10.969598
920.000063(419.01946949618866-89.02210066089677j)419.019469-89.022101428.37162652.636414-11.994343
930.000050(429.8814876025374-99.88357759909778j)429.881488-99.883578441.33300652.895328-13.080657
940.000040(442.0688723556688-112.07053249146567j)442.068872-112.070532456.05338753.180314-14.225566
950.000032(455.74334295006133-125.74466163807756j)455.743343-125.744662472.77237153.493042-15.424774
960.000025(471.08635130454286-141.0873987726884j)471.086351-141.087399491.76010953.835066-16.672627
970.000020(488.30148981947593-158.30232185111964j)488.301490-158.302322513.32053354.207773-17.962161
980.000016(507.6171929245106-177.61785382955162j)507.617193-177.617854537.79486554.612333-19.285203
990.000013(529.2897682648619-199.29029323964826j)529.289768-199.290293565.56545155.049657-20.632560
1000.000010(553.6067977481557-223.60721474997894j)553.606798-223.607215597.06002555.520360-21.994265
\n", 1057 | "

101 rows × 7 columns

\n", 1058 | "
" 1059 | ], 1060 | "text/plain": [ 1061 | " Frequency (Hz) Z (Ohms) Zreal \\\n", 1062 | "0 100000.000000 (40.00279166643429-0.3375684753864916j) 40.002792 \n", 1063 | "1 79432.823472 (40.00338947231721-0.4246667168318618j) 40.003389 \n", 1064 | "2 63095.734448 (40.004210637163794-0.5342788738489503j) 40.004211 \n", 1065 | "3 50118.723363 (40.00537038895218-0.6722291381058704j) 40.005370 \n", 1066 | "4 39810.717055 (40.00704946648614-0.8458485042528762j) 40.007049 \n", 1067 | "5 31622.776602 (40.00953219932713-1.0643641827870147j) 40.009532 \n", 1068 | "6 25118.864315 (40.01326682112679-1.3393891679585193j) 40.013267 \n", 1069 | "7 19952.623150 (40.01896100003028-1.685537230252724j) 40.018961 \n", 1070 | "8 15848.931925 (40.02773313765615-2.1211944873617696j) 40.027733 \n", 1071 | "9 12589.254118 (40.04135194332757-2.6694854615013828j) 40.041352 \n", 1072 | "10 10000.000000 (40.062615639998384-3.3594787313618397j) 40.062616 \n", 1073 | "11 7943.282347 (40.09595178772775-4.227683762034331j) 40.095952 \n", 1074 | "12 6309.573445 (40.14836505502175-5.319893507795863j) 40.148365 \n", 1075 | "13 5011.872336 (40.23093218061143-6.693421003832081j) 40.230932 \n", 1076 | "14 3981.071706 (40.36115349979036-8.419750110111554j) 40.361153 \n", 1077 | "15 3162.277660 (40.566635399217944-10.587546150456358j) 40.566635 \n", 1078 | "16 2511.886432 (40.890815915692784-13.305803123318627j) 40.890816 \n", 1079 | "17 1995.262315 (41.401764722896374-16.706549131792872j) 41.401765 \n", 1080 | "18 1584.893192 (42.205454444064166-20.94582623061118j) 42.205454 \n", 1081 | "19 1258.925412 (43.46514991198426-26.200333033837637j) 43.465150 \n", 1082 | "20 1000.000000 (45.42821052743007-32.654792124557986j) 45.428211 \n", 1083 | "21 794.328235 (48.459464878915156-40.47149206187026j) 48.459465 \n", 1084 | "22 630.957344 (53.07400008014952-49.729146723262865j) 53.074000 \n", 1085 | "23 501.187234 (59.948136979411316-60.31683796120883j) 59.948137 \n", 1086 | "24 398.107171 (69.86399177355301-71.78086655518706j) 69.863992 \n", 1087 | "25 316.227766 (83.52246863098011-83.1663876681218j) 83.522469 \n", 1088 | "26 251.188643 (101.18669609670077-92.97932979560281j) 101.186696 \n", 1089 | "27 199.526231 (122.25798583184859-99.45477278432047j) 122.257986 \n", 1090 | "28 158.489319 (145.10047817637712-101.19141966624632j) 145.100478 \n", 1091 | "29 125.892541 (167.4340424383373-97.84037649429646j) 167.434042 \n", 1092 | ".. ... ... ... \n", 1093 | "71 0.007943 (337.9327182171763-8.265099340082763j) 337.932718 \n", 1094 | "72 0.006310 (338.90122112577086-9.165054109470361j) 338.901221 \n", 1095 | "73 0.005012 (339.9876907950041-10.19714280877964j) 339.987691 \n", 1096 | "74 0.003981 (341.20659827848687-11.372897411112348j) 341.206598 \n", 1097 | "75 0.003162 (342.5741519751984-12.706201015911235j) 342.574152 \n", 1098 | "76 0.002512 (344.1085200943466-14.213380665817024j) 344.108520 \n", 1099 | "77 0.001995 (345.8300763990363-15.913351356062485j) 345.830076 \n", 1100 | "78 0.001585 (347.76167349571654-17.827809306384747j) 347.761673 \n", 1101 | "79 0.001259 (349.9289479306948-19.981474000083242j) 349.928948 \n", 1102 | "80 0.001000 (352.36066154280155-22.402379766796606j) 352.360662 \n", 1103 | "81 0.000794 (355.08908385454527-25.122218841561928j) 355.089084 \n", 1104 | "82 0.000631 (358.1504207353678-28.176738912941524j) 358.150421 \n", 1105 | "83 0.000501 (361.5852951257499-31.606199212081016j) 361.585295 \n", 1106 | "84 0.000398 (365.4392862645882-35.45589022269169j) 365.439286 \n", 1107 | "85 0.000316 (369.7635346151326-39.77672313593609j) 369.763535 \n", 1108 | "86 0.000251 (374.61542054176607-44.6258962584295j) 374.615421 \n", 1109 | "87 0.000200 (380.0593257592085-50.06764672883367j) 380.059326 \n", 1110 | "88 0.000158 (386.16748766817574-56.174097130727866j) 386.167488 \n", 1111 | "89 0.000126 (393.02095792036346-63.02620792827521j) 393.020958 \n", 1112 | "90 0.000100 (400.7106779363328-70.71484811864656j) 400.710678 \n", 1113 | "91 0.000079 (409.3386856502992-79.34199811407133j) 409.338686 \n", 1114 | "92 0.000063 (419.01946949618866-89.02210066089677j) 419.019469 \n", 1115 | "93 0.000050 (429.8814876025374-99.88357759909778j) 429.881488 \n", 1116 | "94 0.000040 (442.0688723556688-112.07053249146567j) 442.068872 \n", 1117 | "95 0.000032 (455.74334295006133-125.74466163807756j) 455.743343 \n", 1118 | "96 0.000025 (471.08635130454286-141.0873987726884j) 471.086351 \n", 1119 | "97 0.000020 (488.30148981947593-158.30232185111964j) 488.301490 \n", 1120 | "98 0.000016 (507.6171929245106-177.61785382955162j) 507.617193 \n", 1121 | "99 0.000013 (529.2897682648619-199.29029323964826j) 529.289768 \n", 1122 | "100 0.000010 (553.6067977481557-223.60721474997894j) 553.606798 \n", 1123 | "\n", 1124 | " Zimag Radius (Ohms) Magnitude (dB) Phase (degrees) \n", 1125 | "0 -0.337568 40.004216 32.042115 -0.483486 \n", 1126 | "1 -0.424667 40.005643 32.042425 -0.608216 \n", 1127 | "2 -0.534279 40.007778 32.042889 -0.765172 \n", 1128 | "3 -0.672229 40.011018 32.043592 -0.962677 \n", 1129 | "4 -0.845849 40.015990 32.044671 -1.211195 \n", 1130 | "5 -1.064364 40.023687 32.046342 -1.523867 \n", 1131 | "6 -1.339389 40.035678 32.048944 -1.917182 \n", 1132 | "7 -1.685537 40.054441 32.053014 -2.411785 \n", 1133 | "8 -2.121194 40.083898 32.059399 -3.033445 \n", 1134 | "9 -2.669485 40.130238 32.069435 -3.814163 \n", 1135 | "10 -3.359479 40.203225 32.085218 -4.793363 \n", 1136 | "11 -4.227684 40.318217 32.110026 -6.018980 \n", 1137 | "12 -5.319894 40.499290 32.148948 -7.548056 \n", 1138 | "13 -6.693421 40.783940 32.209784 -9.446061 \n", 1139 | "14 -8.419750 41.230024 32.304272 -11.783494 \n", 1140 | "15 -10.587546 41.925506 32.449566 -14.627414 \n", 1141 | "16 -13.305803 43.001200 32.669612 -18.024801 \n", 1142 | "17 -16.706549 44.645435 32.995541 -21.975191 \n", 1143 | "18 -20.945826 47.117173 33.463585 -26.394398 \n", 1144 | "19 -26.200333 50.751125 34.108914 -31.081129 \n", 1145 | "20 -32.654792 55.946919 34.955524 -35.709407 \n", 1146 | "21 -40.471492 63.136847 36.005658 -39.867342 \n", 1147 | "22 -49.729147 72.731269 37.234423 -43.136454 \n", 1148 | "23 -60.316838 85.040579 38.592524 -45.175653 \n", 1149 | "24 -71.780867 100.167211 40.014512 -45.775334 \n", 1150 | "25 -83.166388 117.867090 41.427851 -44.877605 \n", 1151 | "26 -92.979330 137.418715 42.760918 -42.579558 \n", 1152 | "27 -99.454773 157.601608 43.951213 -39.127762 \n", 1153 | "28 -101.191420 176.900685 44.954590 -34.891479 \n", 1154 | "29 -97.840376 193.924980 45.752675 -30.299955 \n", 1155 | ".. ... ... ... ... \n", 1156 | "71 -8.265099 338.033776 50.579202 -1.401051 \n", 1157 | "72 -9.165054 339.025126 50.604638 -1.549097 \n", 1158 | "73 -10.197143 340.140576 50.633169 -1.717939 \n", 1159 | "74 -11.372897 341.396083 50.665171 -1.909042 \n", 1160 | "75 -12.706201 342.809710 50.701062 -2.124148 \n", 1161 | "76 -14.213381 344.401936 50.741312 -2.365254 \n", 1162 | "77 -15.913351 346.196009 50.786441 -2.634604 \n", 1163 | "78 -17.827809 348.218340 50.837033 -2.934667 \n", 1164 | "79 -19.981474 350.498970 50.893735 -3.268127 \n", 1165 | "80 -22.402380 353.072092 50.957268 -3.637854 \n", 1166 | "81 -25.122219 355.976661 51.028431 -4.046879 \n", 1167 | "82 -28.176739 359.257084 51.108107 -4.498361 \n", 1168 | "83 -31.606199 362.964017 51.197271 -4.995532 \n", 1169 | "84 -35.455890 367.155270 51.296995 -5.541644 \n", 1170 | "85 -39.776723 371.896840 51.408450 -6.139891 \n", 1171 | "86 -44.625896 377.264077 51.532909 -6.793321 \n", 1172 | "87 -50.067647 383.343006 51.671751 -7.504725 \n", 1173 | "88 -56.174097 390.231800 51.826453 -8.276515 \n", 1174 | "89 -63.026208 398.042430 51.998587 -9.110582 \n", 1175 | "90 -70.714848 406.902491 52.189807 -10.008146 \n", 1176 | "91 -79.341998 416.957207 52.401830 -10.969598 \n", 1177 | "92 -89.022101 428.371626 52.636414 -11.994343 \n", 1178 | "93 -99.883578 441.333006 52.895328 -13.080657 \n", 1179 | "94 -112.070532 456.053387 53.180314 -14.225566 \n", 1180 | "95 -125.744662 472.772371 53.493042 -15.424774 \n", 1181 | "96 -141.087399 491.760109 53.835066 -16.672627 \n", 1182 | "97 -158.302322 513.320533 54.207773 -17.962161 \n", 1183 | "98 -177.617854 537.794865 54.612333 -19.285203 \n", 1184 | "99 -199.290293 565.565451 55.049657 -20.632560 \n", 1185 | "100 -223.607215 597.060025 55.520360 -21.994265 \n", 1186 | "\n", 1187 | "[101 rows x 7 columns]" 1188 | ] 1189 | }, 1190 | "metadata": {}, 1191 | "output_type": "display_data" 1192 | } 1193 | ], 1194 | "source": [ 1195 | "myShinyNewDataframe = someEquivalentCircuit.to_dataframe()\n", 1196 | "display(myShinyNewDataframe)" 1197 | ] 1198 | }, 1199 | { 1200 | "cell_type": "code", 1201 | "execution_count": null, 1202 | "metadata": {}, 1203 | "outputs": [], 1204 | "source": [] 1205 | } 1206 | ], 1207 | "metadata": { 1208 | "kernelspec": { 1209 | "display_name": "Python 3", 1210 | "language": "python", 1211 | "name": "python3" 1212 | }, 1213 | "language_info": { 1214 | "codemirror_mode": { 1215 | "name": "ipython", 1216 | "version": 3 1217 | }, 1218 | "file_extension": ".py", 1219 | "mimetype": "text/x-python", 1220 | "name": "python", 1221 | "nbconvert_exporter": "python", 1222 | "pygments_lexer": "ipython3", 1223 | "version": "3.7.0" 1224 | } 1225 | }, 1226 | "nbformat": 4, 1227 | "nbformat_minor": 2 1228 | } 1229 | --------------------------------------------------------------------------------