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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing impedance spectra\n",
    "\n",
    "Plotting a basically formated impedance plot is as easy as 1, 2, 3..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from impedance_extend.models.circuits import CustomCircuit\n",
    "from impedance_extend import preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Read in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "frequencies, Z = preprocessing.readCSV('../../../data/exampleData.csv')\n",
    "\n",
    "# keep only the impedance data in the first quandrant\n",
    "frequencies, Z = preprocessing.ignoreBelowX(frequencies, Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Fit a custom circuit\n",
    "\n",
    "(If you want to just plot experimental data without fitting a model you should check out the `visualization.plot_*()` functions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Circuit string: R_0-p(R_1,C_1)-p(R_1,C_1)-Wo_1\n",
      "Fit: True\n",
      "\n",
      "Initial guesses:\n",
      "    R_0 = 1.00e-02 [Ohm]\n",
      "    R_1 = 5.00e-03 [Ohm]\n",
      "    C_1 = 1.00e-01 [F]\n",
      "    R_1 = 5.00e-03 [Ohm]\n",
      "    C_1 = 1.00e-01 [F]\n",
      "  Wo_1_0 = 1.00e-03 [Ohm]\n",
      "  Wo_1_1 = 2.00e+02 [sec]\n",
      "\n",
      "Fit parameters:\n",
      "    R_0 = 1.65e-02  (+/- 1.54e-04) [Ohm]\n",
      "    R_1 = 5.31e-03  (+/- 2.06e-04) [Ohm]\n",
      "    C_1 = 2.32e-01  (+/- 1.90e-02) [F]\n",
      "    R_1 = 8.77e-03  (+/- 1.89e-04) [Ohm]\n",
      "    C_1 = 3.28e+00  (+/- 1.85e-01) [F]\n",
      "  Wo_1_0 = 6.37e-02  (+/- 2.03e-03) [Ohm]\n",
      "  Wo_1_1 = 2.37e+02  (+/- 1.72e+01) [sec]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "circuit = CustomCircuit(initial_guess=[.01, .005, .1, .005, .1, .001, 200], circuit='R_0-p(R_1,C_1)-p(R_1,C_1)-Wo_1')\n",
    "\n",
    "circuit.fit(frequencies, Z)\n",
    "\n",
    "print(circuit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Plot the data and fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### a. Interactive altair plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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0.03265642728210896, \"z_imag\": -0.0027688021541761596, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 0.63096, \"z_real\": 0.03232796211965539, \"z_imag\": -0.0028687505233332576, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 0.79433, \"z_real\": 0.03197349380289498, \"z_imag\": -0.002995332546857452, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 1.0, \"z_real\": 0.03158436174556338, \"z_imag\": -0.0031633863009665544, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 1.2589, \"z_real\": 0.031069936132208306, \"z_imag\": -0.0034345232421858604, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 1.5849, \"z_real\": 0.030461419854177326, \"z_imag\": -0.003652697342055591, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 1.9953, \"z_real\": 0.029900714166654168, \"z_imag\": -0.00389594513544332, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 2.5119, \"z_real\": 0.029379111339927506, \"z_imag\": -0.0041496368125138496, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 3.1623, \"z_real\": 0.028614488514401064, \"z_imag\": -0.0043563647278047945, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 3.9811, \"z_real\": 0.027877380810968015, \"z_imag\": -0.004528514961203703, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 5.0119, \"z_real\": 0.027051941695755265, \"z_imag\": -0.004623972802104744, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 6.3096, \"z_real\": 0.02622642987302172, \"z_imag\": -0.00463483440841946, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 7.9433, \"z_real\": 0.02539677675995668, \"z_imag\": -0.004562544489738368, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 10.0, \"z_real\": 0.024674033206038913, \"z_imag\": -0.0044183840649258165, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 12.589, \"z_real\": 0.023984220630662276, \"z_imag\": -0.004213943600562558, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 15.849, \"z_real\": 0.023376189861574193, \"z_imag\": -0.00397620055979716, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 19.953, \"z_real\": 0.022795788586331325, \"z_imag\": -0.0037290248504921668, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 25.119, \"z_real\": 0.022290491192888506, \"z_imag\": -0.0035578892246933775, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 31.623, \"z_real\": 0.02183347892172112, \"z_imag\": -0.0033509582749051627, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 39.811, \"z_real\": 0.021423948245372654, \"z_imag\": -0.0031826464281827804, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 50.119, \"z_real\": 0.021044983846558948, \"z_imag\": -0.0030507184111723995, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 63.096, \"z_real\": 0.02061274834162727, \"z_imag\": -0.0029386920239828154, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 79.433, \"z_real\": 0.02020959510042839, \"z_imag\": -0.002848034411523496, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 100.0, \"z_real\": 0.019760492004316906, \"z_imag\": -0.0027583877127425357, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 125.89, \"z_real\": 0.019397188854563818, \"z_imag\": -0.0026767011351060705, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 158.49, \"z_real\": 0.01898347057349932, \"z_imag\": -0.002575856490231119, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 199.53, \"z_real\": 0.018562859805406066, \"z_imag\": -0.002455805016755156, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 251.19, \"z_real\": 0.018173948838613962, \"z_imag\": -0.0023163152672671405, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 316.23, \"z_real\": 0.017777098024495532, \"z_imag\": -0.002149498808757098, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 398.11, \"z_real\": 0.017382944047369668, \"z_imag\": -0.0019492643145405137, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 501.19, \"z_real\": 0.017027408256891644, \"z_imag\": -0.0017151675874650793, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 630.96, \"z_real\": 0.016664493440403796, \"z_imag\": -0.0014357936694323731, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 794.33, \"z_real\": 0.016338702344109557, \"z_imag\": -0.001109438368794195, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 1000.0, \"z_real\": 0.0160611742499297, \"z_imag\": -0.0007287022309982213, \"kind\": \"data\", \"fmt\": \"o\"}, {\"f\": 1258.9, \"z_real\": 0.01580888106340524, \"z_imag\": -0.0002827724289657194, \"kind\": \"data\", \"fmt\": \"o\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.plot(f_data=frequencies, Z_data=Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### b. Nyquist plot via matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "circuit.plot(f_data=frequencies, Z_data=Z, kind='nyquist')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### c. Bode plot via matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "circuit.plot(f_data=frequencies, Z_data=Z, kind='bode')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bonus: Easy access to all the customization of matplotlib\n",
    "\n",
    "Here we plot the data, changing the size of the figure, axes label fontsize, and turning off the grid by accessing the plt.Axes() object, ax\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "ax = circuit.plot(ax, frequencies, Z, kind='nyquist')\n",
    "\n",
    "ax.tick_params(axis='both', which='major', labelsize=16)\n",
    "ax.grid(False)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}