{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Example on how to import files using QDataLib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from qdatalib.tolib import Qdatalib\n",
    "from qcodes.dataset.plotting import plot_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Instantiate Qdatalib\n",
    "To use Qdatalib you need an instance. If you have not setup Qdatalib yet see the example: setting_up_qdatalib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "qdatalib = Qdatalib()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Access your data using Qdatalib\n",
    "You can get data from Qdatalib using the funcion get_data_by_catalog. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The query returend 2 results\n",
      "[   {   'C:\\\\Users\\\\rbcma\\\\AppData\\\\Local\\\\Temp\\\\qdatalib_local': {   'db': 'original_path'},\n",
      "        'Presented': 'group meeting June 24, 2021',\n",
      "        '_id': 'aaaaaaaa-0000-0000-0000-017aa9641e21',\n",
      "        'exp_id': 1,\n",
      "        'exp_name': 'qdatalib',\n",
      "        'file': 'shared_tutorial.db',\n",
      "        'my_Score': 2,\n",
      "        'my_field': 'Group NB',\n",
      "        'note': ['This happened'],\n",
      "        'parameters': [['dac_ch1', 'V'], ['dmm_v1', 'V'], ['dmm_v2', 'V']],\n",
      "        'run_id': 1,\n",
      "        'run_time': '2021-07-15 10:59:08',\n",
      "        'sample_name': 'no sample',\n",
      "        'scientist': 'John doe',\n",
      "        'tag': ['double dot']},\n",
      "    {   'C:\\\\Users\\\\rbcma\\\\AppData\\\\Local\\\\Temp\\\\qdatalib_localB': {   'db': 'original_path'},\n",
      "        'Presented': 'group meeting July 24, 2021',\n",
      "        '_id': 'aaaaaaaa-0000-0000-0000-017aa96473d6',\n",
      "        'exp_id': 1,\n",
      "        'exp_name': 'qdatalibB',\n",
      "        'file': 'shared_tutorial.db',\n",
      "        'my_Score': 10,\n",
      "        'my_field': 'Group C',\n",
      "        'note': ['This happened again'],\n",
      "        'parameters': [['dac_ch1', 'V'], ['dmm_v1', 'V'], ['dmm_v2', 'V']],\n",
      "        'run_id': 1,\n",
      "        'run_time': '2021-07-15 10:59:30',\n",
      "        'sample_name': 'no sampleB',\n",
      "        'scientist': 'Jane doe',\n",
      "        'tag': ['sigle dot']}]\n"
     ]
    }
   ],
   "source": [
    "data = qdatalib.get_data_by_catalog({'run_id':1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If more than one data set matches your query criteria, \n",
    "only the mongo collection data are returned. \n",
    "You can use the returned information to make a query that gives you the data you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = qdatalib.get_data_by_catalog({'run_id':1,'tag':'double dot'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<AxesSubplot:title={'center':'Run #1, Experiment qdatalib (no sample)'}, xlabel='Gate ch1 (V)', ylabel='Gate v1 (V)'>,\n",
       "  <AxesSubplot:title={'center':'Run #1, Experiment qdatalib (no sample)'}, xlabel='Gate ch1 (V)', ylabel='Gate v2 (mV)'>],\n",
       " [None, None])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "plot_dataset(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting data from a netcdf is returned as a xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_xarray = qdatalib.get_data_from_nc_by_catalog({'run_id':1,'my_field': 'Group NB'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (dac_ch1: 10)\n",
       "Coordinates:\n",
       "  * dac_ch1  (dac_ch1) float64 0.0 1.111 2.222 3.333 ... 6.667 7.778 8.889 10.0\n",
       "Data variables:\n",
       "    dmm_v1   (dac_ch1) float64 4.856 3.978 3.168 2.504 ... 0.9716 0.7113 0.7752\n",
       "    dmm_v2   (dac_ch1) float64 0.7592 -5.235e-05 ... 0.0006781 -0.000473\n",
       "Attributes: (12/13)\n",
       "    ds_name:                  results\n",
       "    sample_name:              no sample\n",
       "    exp_name:                 qdatalib\n",
       "    snapshot:                 null\n",
       "    guid:                     aaaaaaaa-0000-0000-0000-017aa9641e21\n",
       "    run_timestamp:            2021-07-15 10:59:08\n",
       "    ...                       ...\n",
       "    captured_run_id:          1\n",
       "    captured_counter:         1\n",
       "    run_id:                   1\n",
       "    run_description:          {&quot;version&quot;: 3, &quot;interdependencies&quot;: {&quot;paramspec...\n",
       "    run_timestamp_raw:        1626339548.7205129\n",
       "    completed_timestamp_raw:  1626339548.8823342</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-97fb93f3-69f4-4c7f-8112-3cc62da44e01' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-97fb93f3-69f4-4c7f-8112-3cc62da44e01' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>dac_ch1</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fa1cdb0f-d75b-43ac-a7e5-cfa7341c40bb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fa1cdb0f-d75b-43ac-a7e5-cfa7341c40bb' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch1</span></div><div class='xr-var-dims'>(dac_ch1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.111 2.222 ... 8.889 10.0</div><input id='attrs-d38dc42c-f6b6-4a30-bf51-274ee6094d98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d38dc42c-f6b6-4a30-bf51-274ee6094d98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16f8e3fa-18dd-448c-8d0e-64751e66b86e' class='xr-var-data-in' type='checkbox'><label for='data-16f8e3fa-18dd-448c-8d0e-64751e66b86e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dac_ch1</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate ch1</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([ 0.      ,  1.111111,  2.222222,  3.333333,  4.444444,  5.555556,\n",
       "        6.666667,  7.777778,  8.888889, 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fcf14b92-28dc-49f3-a2f3-736b819b4943' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fcf14b92-28dc-49f3-a2f3-736b819b4943' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>dmm_v1</span></div><div class='xr-var-dims'>(dac_ch1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-67e69c57-8462-4074-888d-877ed7d4f940' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67e69c57-8462-4074-888d-877ed7d4f940' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33e0707b-775e-4635-a9fd-32b33c497741' class='xr-var-data-in' type='checkbox'><label for='data-33e0707b-775e-4635-a9fd-32b33c497741' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dmm_v1</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate v1</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>dac_ch1</dd></dl></div><div class='xr-var-data'><pre>array([4.855916, 3.978291, 3.167727, 2.503683, 1.954306, 1.473822, 1.332216,\n",
       "       0.971628, 0.711277, 0.775152])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dmm_v2</span></div><div class='xr-var-dims'>(dac_ch1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-65fe189e-d556-475c-9ba9-847aadcdd3bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65fe189e-d556-475c-9ba9-847aadcdd3bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c1c4604-82a4-4827-a403-a62ef44c3a65' class='xr-var-data-in' type='checkbox'><label for='data-0c1c4604-82a4-4827-a403-a62ef44c3a65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dmm_v2</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate v2</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>dac_ch1</dd></dl></div><div class='xr-var-data'><pre>array([ 7.592161e-01, -5.235340e-05, -7.234900e-06,  8.302668e-04,\n",
       "       -1.245735e-03, -4.155454e-04, -7.960841e-04, -3.306797e-04,\n",
       "        6.781011e-04, -4.729548e-04])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2f417168-dda6-414f-b440-59a22e374701' class='xr-section-summary-in' type='checkbox'  ><label for='section-2f417168-dda6-414f-b440-59a22e374701' class='xr-section-summary' >Attributes: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>ds_name :</span></dt><dd>results</dd><dt><span>sample_name :</span></dt><dd>no sample</dd><dt><span>exp_name :</span></dt><dd>qdatalib</dd><dt><span>snapshot :</span></dt><dd>null</dd><dt><span>guid :</span></dt><dd>aaaaaaaa-0000-0000-0000-017aa9641e21</dd><dt><span>run_timestamp :</span></dt><dd>2021-07-15 10:59:08</dd><dt><span>completed_timestamp :</span></dt><dd>2021-07-15 10:59:08</dd><dt><span>captured_run_id :</span></dt><dd>1</dd><dt><span>captured_counter :</span></dt><dd>1</dd><dt><span>run_id :</span></dt><dd>1</dd><dt><span>run_description :</span></dt><dd>{&quot;version&quot;: 3, &quot;interdependencies&quot;: {&quot;paramspecs&quot;: [{&quot;name&quot;: &quot;dac_ch1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch1&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: []}, {&quot;name&quot;: &quot;dmm_v1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v1&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: [&quot;dac_ch1&quot;]}, {&quot;name&quot;: &quot;dmm_v2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v2&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: [&quot;dac_ch1&quot;]}]}, &quot;interdependencies_&quot;: {&quot;parameters&quot;: {&quot;dmm_v1&quot;: {&quot;name&quot;: &quot;dmm_v1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v1&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;dac_ch1&quot;: {&quot;name&quot;: &quot;dac_ch1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch1&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;dmm_v2&quot;: {&quot;name&quot;: &quot;dmm_v2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v2&quot;, &quot;unit&quot;: &quot;V&quot;}}, &quot;dependencies&quot;: {&quot;dmm_v1&quot;: [&quot;dac_ch1&quot;], &quot;dmm_v2&quot;: [&quot;dac_ch1&quot;]}, &quot;inferences&quot;: {}, &quot;standalones&quot;: []}, &quot;shapes&quot;: {&quot;dmm_v1&quot;: [10], &quot;dmm_v2&quot;: [10]}}</dd><dt><span>run_timestamp_raw :</span></dt><dd>1626339548.7205129</dd><dt><span>completed_timestamp_raw :</span></dt><dd>1626339548.8823342</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (dac_ch1: 10)\n",
       "Coordinates:\n",
       "  * dac_ch1  (dac_ch1) float64 0.0 1.111 2.222 3.333 ... 6.667 7.778 8.889 10.0\n",
       "Data variables:\n",
       "    dmm_v1   (dac_ch1) float64 ...\n",
       "    dmm_v2   (dac_ch1) float64 ...\n",
       "Attributes: (12/13)\n",
       "    ds_name:                  results\n",
       "    sample_name:              no sample\n",
       "    exp_name:                 qdatalib\n",
       "    snapshot:                 null\n",
       "    guid:                     aaaaaaaa-0000-0000-0000-017aa9641e21\n",
       "    run_timestamp:            2021-07-15 10:59:08\n",
       "    ...                       ...\n",
       "    captured_run_id:          1\n",
       "    captured_counter:         1\n",
       "    run_id:                   1\n",
       "    run_description:          {\"version\": 3, \"interdependencies\": {\"paramspec...\n",
       "    run_timestamp_raw:        1626339548.7205129\n",
       "    completed_timestamp_raw:  1626339548.8823342"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19f595b5708>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "data_xarray.dmm_v1.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting data from a csv file is returned as a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = qdatalib.get_data_from_csv_by_catalog({'run_id':1,'tag':'sigle dot'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dac_ch1</th>\n",
       "      <th>dmm_v1</th>\n",
       "      <th>dmm_v2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.040760</td>\n",
       "      <td>0.759494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.111111</td>\n",
       "      <td>4.033489</td>\n",
       "      <td>0.000178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.222222</td>\n",
       "      <td>3.158298</td>\n",
       "      <td>-0.000819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.333333</td>\n",
       "      <td>2.493970</td>\n",
       "      <td>-0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.444444</td>\n",
       "      <td>2.117269</td>\n",
       "      <td>-0.000218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.555556</td>\n",
       "      <td>1.697006</td>\n",
       "      <td>-0.000441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.666667</td>\n",
       "      <td>1.440228</td>\n",
       "      <td>-0.000125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.777778</td>\n",
       "      <td>0.811189</td>\n",
       "      <td>-0.000124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.888889</td>\n",
       "      <td>0.909516</td>\n",
       "      <td>-0.000595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.698422</td>\n",
       "      <td>0.000352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     dac_ch1    dmm_v1    dmm_v2\n",
       "0   0.000000  5.040760  0.759494\n",
       "1   1.111111  4.033489  0.000178\n",
       "2   2.222222  3.158298 -0.000819\n",
       "3   3.333333  2.493970 -0.000011\n",
       "4   4.444444  2.117269 -0.000218\n",
       "5   5.555556  1.697006 -0.000441\n",
       "6   6.666667  1.440228 -0.000125\n",
       "7   7.777778  0.811189 -0.000124\n",
       "8   8.888889  0.909516 -0.000595\n",
       "9  10.000000  0.698422  0.000352"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='dac_ch1'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "data_df.plot(x='dac_ch1',y='dmm_v2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}