{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-02T11:34:48.173281",
     "start_time": "2017-03-02T11:34:47.125539"
    },
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "execution": {
     "iopub.execute_input": "2023-11-28T17:55:11.064960Z",
     "iopub.status.busy": "2023-11-28T17:55:11.064103Z",
     "iopub.status.idle": "2023-11-28T17:55:12.420472Z",
     "shell.execute_reply": "2023-11-28T17:55:12.419359Z",
     "shell.execute_reply.started": "2023-11-28T17:55:11.064889Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "run_control": {
     "frozen": false,
     "marked": true,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "import atmPy.aerosols.size_distribution.sizedistribution as atmsd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "# SizeDist (a single size distribution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create instance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### simulate a sizedistribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:00:28.757009Z",
     "iopub.status.busy": "2023-11-28T18:00:28.755978Z",
     "iopub.status.idle": "2023-11-28T18:00:28.767846Z",
     "shell.execute_reply": "2023-11-28T18:00:28.766494Z",
     "shell.execute_reply.started": "2023-11-28T18:00:28.756903Z"
    }
   },
   "outputs": [],
   "source": [
    "sd = atmsd.simulate_sizedistribution(diameter=[10, 2500],\n",
    "                                    numberOfDiameters=100,\n",
    "                                    centerOfAerosolMode=200,\n",
    "                                    widthOfAerosolMode=0.2,\n",
    "                                    numberOfParticsInMode=1000,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### format your own data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T17:58:17.561964Z",
     "iopub.status.busy": "2023-11-28T17:58:17.561364Z",
     "iopub.status.idle": "2023-11-28T17:58:17.568451Z",
     "shell.execute_reply": "2023-11-28T17:58:17.567020Z",
     "shell.execute_reply.started": "2023-11-28T17:58:17.561910Z"
    }
   },
   "outputs": [],
   "source": [
    "atmsd.SizeDist(data, bins, distType)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`data` should have a similar structure as below. However, column names are not required as they are calculated based on `bins`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:00:35.739967Z",
     "iopub.status.busy": "2023-11-28T18:00:35.739329Z",
     "iopub.status.idle": "2023-11-28T18:00:35.775157Z",
     "shell.execute_reply": "2023-11-28T18:00:35.773850Z",
     "shell.execute_reply.started": "2023-11-28T18:00:35.739914Z"
    }
   },
   "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>bincenters</th>\n",
       "      <th>10.286785</th>\n",
       "      <th>10.876803</th>\n",
       "      <th>11.500663</th>\n",
       "      <th>12.160306</th>\n",
       "      <th>12.857784</th>\n",
       "      <th>13.595267</th>\n",
       "      <th>14.375049</th>\n",
       "      <th>15.199558</th>\n",
       "      <th>16.071358</th>\n",
       "      <th>16.993162</th>\n",
       "      <th>...</th>\n",
       "      <th>1472.324349</th>\n",
       "      <th>1556.772354</th>\n",
       "      <th>1646.064038</th>\n",
       "      <th>1740.477219</th>\n",
       "      <th>1840.305650</th>\n",
       "      <th>1945.859933</th>\n",
       "      <th>2057.468486</th>\n",
       "      <th>2175.478563</th>\n",
       "      <th>2300.257336</th>\n",
       "      <th>2432.193036</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.067904e-08</td>\n",
       "      <td>1.653171e-07</td>\n",
       "      <td>3.338144e-07</td>\n",
       "      <td>6.642360e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>5.883500e-07</td>\n",
       "      <td>2.949072e-07</td>\n",
       "      <td>1.456683e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "bincenters   10.286785     10.876803     11.500663     12.160306    \\\n",
       "0           8.067904e-08  1.653171e-07  3.338144e-07  6.642360e-07   \n",
       "\n",
       "bincenters  12.857784    13.595267    14.375049    15.199558    16.071358    \\\n",
       "0              0.000001     0.000003     0.000005     0.000009     0.000017   \n",
       "\n",
       "bincenters  16.993162    ...  1472.324349  1556.772354  1646.064038  \\\n",
       "0               0.00003  ...     0.000049     0.000027     0.000015   \n",
       "\n",
       "bincenters  1740.477219  1840.305650  1945.859933  2057.468486   2175.478563  \\\n",
       "0              0.000008     0.000004     0.000002     0.000001  5.883500e-07   \n",
       "\n",
       "bincenters   2300.257336   2432.193036  \n",
       "0           2.949072e-07  1.456683e-07  \n",
       "\n",
       "[1 rows x 99 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`bins` are the binedges. For an example of how they should be formatted look again to the sizedistribution generated above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:05:24.762720Z",
     "iopub.status.busy": "2023-11-28T18:05:24.761905Z",
     "iopub.status.idle": "2023-11-28T18:05:24.774798Z",
     "shell.execute_reply": "2023-11-28T18:05:24.772955Z",
     "shell.execute_reply.started": "2023-11-28T18:05:24.762664Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  10.        ,   10.57356931,   11.18003679,   11.82128938,\n",
       "         12.49932225,   13.21624501,   13.97428826,   14.77581054,\n",
       "         15.62330568,   16.51941053,   17.46691322,   18.46876174,\n",
       "         19.52807323,   20.64814357,   21.8324577 ,   23.08470046,\n",
       "         24.40876803,   25.80878004,   27.28909244,   28.85431102,\n",
       "         30.50930573,   32.25922586,   34.10951604,   36.06593318,\n",
       "         38.1345644 ,   40.32184597,   42.63458328,   45.07997211,\n",
       "         47.66562094,   50.39957465,   53.29033955,   56.34690986,\n",
       "         59.57879565,   62.99605249,   66.6093127 ,   70.42981842,\n",
       "         74.46945662,   78.74079607,   83.25712644,   88.03249966,\n",
       "         93.08177363,   98.42065845,  104.06576532,  110.03465819,\n",
       "        116.34590844,  123.01915263,  130.07515362,  137.53586517,\n",
       "        145.42450023,  153.76560319,  162.58512621,  171.91050999,\n",
       "        181.77076917,  192.19658255,  203.22038858,  214.87648629,\n",
       "        227.20114199,  240.23270211,  254.01171251,  268.58104466,\n",
       "        283.98602898,  300.27459591,  317.49742505,  335.7081028 ,\n",
       "        354.96328913,  375.32289384,  396.85026299,  419.61237596,\n",
       "        443.68005385,  469.12817988,  496.0359323 ,  524.48703081,\n",
       "        554.56999699,  586.37842978,  620.01129664,  655.57324151,\n",
       "        693.17491038,  732.93329556,  774.97209967,  819.42212055,\n",
       "        866.42165819,  916.11694505,  968.66260102, 1024.22211453,\n",
       "       1082.9683512 , 1145.08409168, 1210.76260037, 1280.20822673,\n",
       "       1353.63704105, 1431.27750677, 1513.37119129, 1600.17351757,\n",
       "       1691.95455884, 1788.99987893, 1891.6114207 , 2000.10844554,\n",
       "       2114.8285267 , 2236.12859958, 2364.38607231, 2500.        ])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd.bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see options for the `distType` argument see help file. This is what our generated was:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:08:04.058781Z",
     "iopub.status.busy": "2023-11-28T18:08:04.057973Z",
     "iopub.status.idle": "2023-11-28T18:08:04.068603Z",
     "shell.execute_reply": "2023-11-28T18:08:04.066909Z",
     "shell.execute_reply.started": "2023-11-28T18:08:04.058728Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dNdDp'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd.distributionType"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "create the instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:08:39.996196Z",
     "iopub.status.busy": "2023-11-28T18:08:39.995406Z",
     "iopub.status.idle": "2023-11-28T18:08:40.005028Z",
     "shell.execute_reply": "2023-11-28T18:08:40.003526Z",
     "shell.execute_reply.started": "2023-11-28T18:08:39.996114Z"
    }
   },
   "outputs": [],
   "source": [
    "sdc = atmsd.SizeDist(sd.data, sd.bins, sd.distributionType)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:11:06.096973Z",
     "iopub.status.busy": "2023-11-28T18:11:06.096097Z",
     "iopub.status.idle": "2023-11-28T18:11:06.108697Z",
     "shell.execute_reply": "2023-11-28T18:11:06.105311Z",
     "shell.execute_reply.started": "2023-11-28T18:11:06.096918Z"
    }
   },
   "source": [
    "You can add two sizedistributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:11:46.442237Z",
     "iopub.status.busy": "2023-11-28T18:11:46.441616Z",
     "iopub.status.idle": "2023-11-28T18:11:46.451336Z",
     "shell.execute_reply": "2023-11-28T18:11:46.449176Z",
     "shell.execute_reply.started": "2023-11-28T18:11:46.442182Z"
    }
   },
   "outputs": [],
   "source": [
    "sdnew = sd + sdc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most attributes and methods of a SizeDist are in the form of paroperties. It is worth checking the namespace and find out what there is, including particle, mass, volume concentrations, growth simulation, mode extrapolation.\n",
    "Some of the properties have setters which will take care of implicit changes to the size distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:16:50.268576Z",
     "iopub.status.busy": "2023-11-28T18:16:50.267576Z",
     "iopub.status.idle": "2023-11-28T18:16:50.602955Z",
     "shell.execute_reply": "2023-11-28T18:16:50.601655Z",
     "shell.execute_reply.started": "2023-11-28T18:16:50.268491Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1280x960 with 1 Axes>,\n",
       " <Axes: xlabel='Particle diameter (nm)', ylabel='$\\\\mathrm{d}N\\\\,/\\\\,\\\\mathrm{d}D_{P}$ (nm$^{-1}\\\\,$cm$^{-3}$)'>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sd.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Moment conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:27:59.868842Z",
     "iopub.status.busy": "2023-11-28T18:27:59.867999Z",
     "iopub.status.idle": "2023-11-28T18:27:59.877829Z",
     "shell.execute_reply": "2023-11-28T18:27:59.875661Z",
     "shell.execute_reply.started": "2023-11-28T18:27:59.868764Z"
    }
   },
   "outputs": [],
   "source": [
    "sd = sd.convert2dNdlogDp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:28:06.268664Z",
     "iopub.status.busy": "2023-11-28T18:28:06.267830Z",
     "iopub.status.idle": "2023-11-28T18:28:06.708699Z",
     "shell.execute_reply": "2023-11-28T18:28:06.707963Z",
     "shell.execute_reply.started": "2023-11-28T18:28:06.268606Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1280x960 with 1 Axes>,\n",
       " <Axes: xlabel='Particle diameter (nm)', ylabel='$\\\\mathrm{d}N\\\\,/\\\\,\\\\mathrm{d}log(D_{P})$ (cm$^{-3}$)'>)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sd.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### optical properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:22:23.321197Z",
     "iopub.status.busy": "2023-11-28T18:22:23.320432Z",
     "iopub.status.idle": "2023-11-28T18:22:23.474902Z",
     "shell.execute_reply": "2023-11-28T18:22:23.474261Z",
     "shell.execute_reply.started": "2023-11-28T18:22:23.321136Z"
    }
   },
   "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>ext_coeff_m^1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ext_coeff_m^1\n",
       "0       0.000086"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd.optical_properties.parameters.refractive_index = 1.5\n",
    "sd.optical_properties.parameters.wavelength = 500\n",
    "sd.optical_properties.extinction_coeff # explore namespace for more Mie results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### t-matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:28:33.484809Z",
     "iopub.status.busy": "2023-11-28T18:28:33.484249Z",
     "iopub.status.idle": "2023-11-28T18:28:33.492070Z",
     "shell.execute_reply": "2023-11-28T18:28:33.490865Z",
     "shell.execute_reply.started": "2023-11-28T18:28:33.484756Z"
    }
   },
   "outputs": [],
   "source": [
    "sd.optical_properties.parameters.refractive_index = 1.5\n",
    "sd.optical_properties.parameters.wavelength = 500\n",
    "sd.optical_properties.parameters.asphericity = 5\n",
    "sd.optical_properties.scattering_coeff # explore namespace for more Mie results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### hygroscopic growth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**GOTCHA** you can loose particles when applying growth!!! See help of `sd.grow_sizedistribution` function! There are functions that extrapolate size distributions assuming normal distributions. Consider using those."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:53:14.248091Z",
     "iopub.status.busy": "2023-11-28T18:53:14.247104Z",
     "iopub.status.idle": "2023-11-28T18:53:14.265020Z",
     "shell.execute_reply": "2023-11-28T18:53:14.263158Z",
     "shell.execute_reply.started": "2023-11-28T18:53:14.247985Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'atmPy.aerosols.size_distribution.sizedistribution' from '/export/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reload(atmsd.hygroscopicity)\n",
    "reload(atmsd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:53:14.484890Z",
     "iopub.status.busy": "2023-11-28T18:53:14.484169Z",
     "iopub.status.idle": "2023-11-28T18:53:14.492086Z",
     "shell.execute_reply": "2023-11-28T18:53:14.490548Z",
     "shell.execute_reply.started": "2023-11-28T18:53:14.484833Z"
    }
   },
   "outputs": [],
   "source": [
    "sd = atmsd.SizeDist(sd.data, sd.bins, sd.distributionType)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:59:20.049584Z",
     "iopub.status.busy": "2023-11-28T18:59:20.048752Z",
     "iopub.status.idle": "2023-11-28T18:59:20.057509Z",
     "shell.execute_reply": "2023-11-28T18:59:20.055644Z",
     "shell.execute_reply.started": "2023-11-28T18:59:20.049528Z"
    }
   },
   "outputs": [],
   "source": [
    "sd.hygroscopicity.parameters.kappa = 0.6\n",
    "sd.hygroscopicity.parameters.RH = 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:59:20.470953Z",
     "iopub.status.busy": "2023-11-28T18:59:20.470332Z",
     "iopub.status.idle": "2023-11-28T18:59:21.665185Z",
     "shell.execute_reply": "2023-11-28T18:59:21.664512Z",
     "shell.execute_reply.started": "2023-11-28T18:59:20.470895Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/htelg/prog/atm-py/atmPy/aerosols/size_distribution/diameter_binning.py:53: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  def match_bins(index, columns, df_match):\n",
      "/export/htelg/prog/atm-py/atmPy/aerosols/size_distribution/diameter_binning.py:81: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  def match2data(data, df_match,new_data):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 1280x960 with 1 Axes>,\n",
       " <Axes: xlabel='Particle diameter (nm)', ylabel='Particle number in bin'>)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,a = sd.plot()\n",
    "sd.hygroscopicity.grown_size_distribution.plot(ax= a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "py310telg",
   "language": "python",
   "name": "py310telg"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  },
  "nav_menu": {},
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "387px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": true
  },
  "toc_position": {
   "height": "960px",
   "left": "0px",
   "right": "2361.33px",
   "top": "107px",
   "width": "212px"
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 },
 "nbformat": 4,
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}