{
 "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": "2023-11-28T22:02:35.945248Z",
     "iopub.status.busy": "2023-11-28T22:02:35.944584Z",
     "iopub.status.idle": "2023-11-28T22:02:36.711830Z",
     "shell.execute_reply": "2023-11-28T22:02:36.710611Z",
     "shell.execute_reply.started": "2023-11-28T22:02:35.945191Z"
    },
    "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": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T22:13:52.740084Z",
     "iopub.status.busy": "2023-11-28T22:13:52.739827Z",
     "iopub.status.idle": "2023-11-28T22:13:52.744377Z",
     "shell.execute_reply": "2023-11-28T22:13:52.743756Z",
     "shell.execute_reply.started": "2023-11-28T22:13:52.740055Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "# SizeDist_LS - a layer series (aka vertical profile) of size distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SizeDist_TS is a subclass of SizeDist with all its properties methods, etc. Here we will mostly focus on what is unique to the SizeDist_TS class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create instance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### simulate a sizedistribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T22:13:58.989016Z",
     "iopub.status.busy": "2023-11-28T22:13:58.988358Z",
     "iopub.status.idle": "2023-11-28T22:13:59.061093Z",
     "shell.execute_reply": "2023-11-28T22:13:59.060272Z",
     "shell.execute_reply.started": "2023-11-28T22:13:58.988959Z"
    }
   },
   "outputs": [],
   "source": [
    "sd = atmsd.simulate_sizedistribution_layerseries(diameter=[10, 2500],\n",
    "                                                numberOfDiameters=30,\n",
    "                                                heightlimits=[0, 6000],\n",
    "                                                noOflayers=50,\n",
    "                                                layerHeight=[500.0, 4000.0],\n",
    "                                                layerThickness=[100.0, 300.0],\n",
    "                                                layerDensity=[1000.0, 5000.0],\n",
    "                                                layerModecenter=[200.0, 800.0],\n",
    "                                                widthOfAerosolMode=0.2,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### format your own data"
   ]
  },
  {
   "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": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T22:03:41.045795Z",
     "iopub.status.busy": "2023-11-28T22:03:41.044988Z",
     "iopub.status.idle": "2023-11-28T22:03:41.102347Z",
     "shell.execute_reply": "2023-11-28T22:03:41.101162Z",
     "shell.execute_reply.started": "2023-11-28T22:03:41.045741Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>bincenters</th>\n",
       "      <th>11.048638</th>\n",
       "      <th>13.365842</th>\n",
       "      <th>16.169028</th>\n",
       "      <th>19.560119</th>\n",
       "      <th>23.662416</th>\n",
       "      <th>28.625078</th>\n",
       "      <th>34.628546</th>\n",
       "      <th>41.891108</th>\n",
       "      <th>50.676829</th>\n",
       "      <th>61.305159</th>\n",
       "      <th>...</th>\n",
       "      <th>411.502887</th>\n",
       "      <th>497.806397</th>\n",
       "      <th>602.210134</th>\n",
       "      <th>728.510216</th>\n",
       "      <th>881.298909</th>\n",
       "      <th>1066.131608</th>\n",
       "      <th>1289.728823</th>\n",
       "      <th>1560.220544</th>\n",
       "      <th>1887.441843</th>\n",
       "      <th>2283.290476</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>3.052113e-12</td>\n",
       "      <td>3.131145e-11</td>\n",
       "      <td>2.707518e-10</td>\n",
       "      <td>1.973355e-09</td>\n",
       "      <td>1.212285e-08</td>\n",
       "      <td>6.277254e-08</td>\n",
       "      <td>2.739684e-07</td>\n",
       "      <td>1.007852e-06</td>\n",
       "      <td>3.125064e-06</td>\n",
       "      <td>8.167451e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.003399e-05</td>\n",
       "      <td>4.000585e-06</td>\n",
       "      <td>1.344432e-06</td>\n",
       "      <td>3.808201e-07</td>\n",
       "      <td>9.092150e-08</td>\n",
       "      <td>1.829696e-08</td>\n",
       "      <td>3.103537e-09</td>\n",
       "      <td>4.437113e-10</td>\n",
       "      <td>5.346995e-11</td>\n",
       "      <td>5.431060e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90.0</th>\n",
       "      <td>4.276988e-11</td>\n",
       "      <td>4.387737e-10</td>\n",
       "      <td>3.794100e-09</td>\n",
       "      <td>2.765302e-08</td>\n",
       "      <td>1.698799e-07</td>\n",
       "      <td>8.796444e-07</td>\n",
       "      <td>3.839175e-06</td>\n",
       "      <td>1.412324e-05</td>\n",
       "      <td>4.379215e-05</td>\n",
       "      <td>1.144522e-04</td>\n",
       "      <td>...</td>\n",
       "      <td>1.406084e-04</td>\n",
       "      <td>5.606101e-05</td>\n",
       "      <td>1.883980e-05</td>\n",
       "      <td>5.336510e-06</td>\n",
       "      <td>1.274101e-06</td>\n",
       "      <td>2.563990e-07</td>\n",
       "      <td>4.349049e-08</td>\n",
       "      <td>6.217817e-09</td>\n",
       "      <td>7.492852e-10</td>\n",
       "      <td>7.610656e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150.0</th>\n",
       "      <td>4.181474e-10</td>\n",
       "      <td>4.289750e-09</td>\n",
       "      <td>3.709370e-08</td>\n",
       "      <td>2.703547e-07</td>\n",
       "      <td>1.660862e-06</td>\n",
       "      <td>8.600002e-06</td>\n",
       "      <td>3.753438e-05</td>\n",
       "      <td>1.380784e-04</td>\n",
       "      <td>4.281419e-04</td>\n",
       "      <td>1.118962e-03</td>\n",
       "      <td>...</td>\n",
       "      <td>1.374683e-03</td>\n",
       "      <td>5.480906e-04</td>\n",
       "      <td>1.841907e-04</td>\n",
       "      <td>5.217335e-05</td>\n",
       "      <td>1.245648e-05</td>\n",
       "      <td>2.506731e-06</td>\n",
       "      <td>4.251926e-07</td>\n",
       "      <td>6.078961e-08</td>\n",
       "      <td>7.325522e-09</td>\n",
       "      <td>7.440695e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210.0</th>\n",
       "      <td>2.852166e-09</td>\n",
       "      <td>2.926021e-08</td>\n",
       "      <td>2.530146e-07</td>\n",
       "      <td>1.844078e-06</td>\n",
       "      <td>1.132867e-05</td>\n",
       "      <td>5.866026e-05</td>\n",
       "      <td>2.560205e-04</td>\n",
       "      <td>9.418269e-04</td>\n",
       "      <td>2.920338e-03</td>\n",
       "      <td>7.632395e-03</td>\n",
       "      <td>...</td>\n",
       "      <td>9.376656e-03</td>\n",
       "      <td>3.738503e-03</td>\n",
       "      <td>1.256357e-03</td>\n",
       "      <td>3.558723e-04</td>\n",
       "      <td>8.496515e-05</td>\n",
       "      <td>1.709831e-05</td>\n",
       "      <td>2.900221e-06</td>\n",
       "      <td>4.146434e-07</td>\n",
       "      <td>4.996708e-08</td>\n",
       "      <td>5.075267e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270.0</th>\n",
       "      <td>1.357295e-08</td>\n",
       "      <td>1.392441e-07</td>\n",
       "      <td>1.204051e-06</td>\n",
       "      <td>8.775639e-06</td>\n",
       "      <td>5.391110e-05</td>\n",
       "      <td>2.791537e-04</td>\n",
       "      <td>1.218356e-03</td>\n",
       "      <td>4.481986e-03</td>\n",
       "      <td>1.389737e-02</td>\n",
       "      <td>3.632120e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>4.462183e-02</td>\n",
       "      <td>1.779087e-02</td>\n",
       "      <td>5.978779e-03</td>\n",
       "      <td>1.693533e-03</td>\n",
       "      <td>4.043340e-04</td>\n",
       "      <td>8.136780e-05</td>\n",
       "      <td>1.380163e-05</td>\n",
       "      <td>1.973214e-06</td>\n",
       "      <td>2.377844e-07</td>\n",
       "      <td>2.415229e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5730.0</th>\n",
       "      <td>3.630329e-24</td>\n",
       "      <td>1.292883e-22</td>\n",
       "      <td>3.880953e-21</td>\n",
       "      <td>9.819357e-20</td>\n",
       "      <td>2.094080e-18</td>\n",
       "      <td>3.764170e-17</td>\n",
       "      <td>5.703098e-16</td>\n",
       "      <td>7.283131e-15</td>\n",
       "      <td>7.839547e-14</td>\n",
       "      <td>7.112618e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>2.220879e-07</td>\n",
       "      <td>3.073871e-07</td>\n",
       "      <td>3.586013e-07</td>\n",
       "      <td>3.526174e-07</td>\n",
       "      <td>2.922545e-07</td>\n",
       "      <td>2.041664e-07</td>\n",
       "      <td>1.202190e-07</td>\n",
       "      <td>5.966609e-08</td>\n",
       "      <td>2.496018e-08</td>\n",
       "      <td>8.801028e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5790.0</th>\n",
       "      <td>1.122985e-24</td>\n",
       "      <td>3.999329e-23</td>\n",
       "      <td>1.200511e-21</td>\n",
       "      <td>3.037462e-20</td>\n",
       "      <td>6.477705e-19</td>\n",
       "      <td>1.164386e-17</td>\n",
       "      <td>1.764163e-16</td>\n",
       "      <td>2.252921e-15</td>\n",
       "      <td>2.425039e-14</td>\n",
       "      <td>2.200175e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>6.869938e-08</td>\n",
       "      <td>9.508530e-08</td>\n",
       "      <td>1.109276e-07</td>\n",
       "      <td>1.090766e-07</td>\n",
       "      <td>9.040428e-08</td>\n",
       "      <td>6.315564e-08</td>\n",
       "      <td>3.718784e-08</td>\n",
       "      <td>1.845676e-08</td>\n",
       "      <td>7.721035e-09</td>\n",
       "      <td>2.722458e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5850.0</th>\n",
       "      <td>3.337566e-25</td>\n",
       "      <td>1.188620e-23</td>\n",
       "      <td>3.567978e-22</td>\n",
       "      <td>9.027487e-21</td>\n",
       "      <td>1.925206e-19</td>\n",
       "      <td>3.460614e-18</td>\n",
       "      <td>5.243179e-17</td>\n",
       "      <td>6.695792e-16</td>\n",
       "      <td>7.207337e-15</td>\n",
       "      <td>6.539030e-14</td>\n",
       "      <td>...</td>\n",
       "      <td>2.041779e-08</td>\n",
       "      <td>2.825982e-08</td>\n",
       "      <td>3.296823e-08</td>\n",
       "      <td>3.241810e-08</td>\n",
       "      <td>2.686860e-08</td>\n",
       "      <td>1.877017e-08</td>\n",
       "      <td>1.105241e-08</td>\n",
       "      <td>5.485440e-09</td>\n",
       "      <td>2.294730e-09</td>\n",
       "      <td>8.091280e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5910.0</th>\n",
       "      <td>9.530465e-26</td>\n",
       "      <td>3.394122e-24</td>\n",
       "      <td>1.018841e-22</td>\n",
       "      <td>2.577811e-21</td>\n",
       "      <td>5.497452e-20</td>\n",
       "      <td>9.881830e-19</td>\n",
       "      <td>1.497197e-17</td>\n",
       "      <td>1.911992e-16</td>\n",
       "      <td>2.058065e-15</td>\n",
       "      <td>1.867229e-14</td>\n",
       "      <td>...</td>\n",
       "      <td>5.830329e-09</td>\n",
       "      <td>8.069631e-09</td>\n",
       "      <td>9.414124e-09</td>\n",
       "      <td>9.257032e-09</td>\n",
       "      <td>7.672365e-09</td>\n",
       "      <td>5.359848e-09</td>\n",
       "      <td>3.156031e-09</td>\n",
       "      <td>1.566375e-09</td>\n",
       "      <td>6.552632e-10</td>\n",
       "      <td>2.310476e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5970.0</th>\n",
       "      <td>2.614728e-26</td>\n",
       "      <td>9.311935e-25</td>\n",
       "      <td>2.795239e-23</td>\n",
       "      <td>7.072347e-22</td>\n",
       "      <td>1.508252e-20</td>\n",
       "      <td>2.711127e-19</td>\n",
       "      <td>4.107631e-18</td>\n",
       "      <td>5.245642e-17</td>\n",
       "      <td>5.646398e-16</td>\n",
       "      <td>5.122831e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.599578e-09</td>\n",
       "      <td>2.213941e-09</td>\n",
       "      <td>2.582809e-09</td>\n",
       "      <td>2.539711e-09</td>\n",
       "      <td>2.104950e-09</td>\n",
       "      <td>1.470500e-09</td>\n",
       "      <td>8.658720e-10</td>\n",
       "      <td>4.297423e-10</td>\n",
       "      <td>1.797746e-10</td>\n",
       "      <td>6.338901e-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "bincenters   11.048638     13.365842     16.169028     19.560119    \\\n",
       "30.0        3.052113e-12  3.131145e-11  2.707518e-10  1.973355e-09   \n",
       "90.0        4.276988e-11  4.387737e-10  3.794100e-09  2.765302e-08   \n",
       "150.0       4.181474e-10  4.289750e-09  3.709370e-08  2.703547e-07   \n",
       "210.0       2.852166e-09  2.926021e-08  2.530146e-07  1.844078e-06   \n",
       "270.0       1.357295e-08  1.392441e-07  1.204051e-06  8.775639e-06   \n",
       "...                  ...           ...           ...           ...   \n",
       "5730.0      3.630329e-24  1.292883e-22  3.880953e-21  9.819357e-20   \n",
       "5790.0      1.122985e-24  3.999329e-23  1.200511e-21  3.037462e-20   \n",
       "5850.0      3.337566e-25  1.188620e-23  3.567978e-22  9.027487e-21   \n",
       "5910.0      9.530465e-26  3.394122e-24  1.018841e-22  2.577811e-21   \n",
       "5970.0      2.614728e-26  9.311935e-25  2.795239e-23  7.072347e-22   \n",
       "\n",
       "bincenters   23.662416     28.625078     34.628546     41.891108    \\\n",
       "30.0        1.212285e-08  6.277254e-08  2.739684e-07  1.007852e-06   \n",
       "90.0        1.698799e-07  8.796444e-07  3.839175e-06  1.412324e-05   \n",
       "150.0       1.660862e-06  8.600002e-06  3.753438e-05  1.380784e-04   \n",
       "210.0       1.132867e-05  5.866026e-05  2.560205e-04  9.418269e-04   \n",
       "270.0       5.391110e-05  2.791537e-04  1.218356e-03  4.481986e-03   \n",
       "...                  ...           ...           ...           ...   \n",
       "5730.0      2.094080e-18  3.764170e-17  5.703098e-16  7.283131e-15   \n",
       "5790.0      6.477705e-19  1.164386e-17  1.764163e-16  2.252921e-15   \n",
       "5850.0      1.925206e-19  3.460614e-18  5.243179e-17  6.695792e-16   \n",
       "5910.0      5.497452e-20  9.881830e-19  1.497197e-17  1.911992e-16   \n",
       "5970.0      1.508252e-20  2.711127e-19  4.107631e-18  5.245642e-17   \n",
       "\n",
       "bincenters   50.676829     61.305159    ...   411.502887    497.806397   \\\n",
       "30.0        3.125064e-06  8.167451e-06  ...  1.003399e-05  4.000585e-06   \n",
       "90.0        4.379215e-05  1.144522e-04  ...  1.406084e-04  5.606101e-05   \n",
       "150.0       4.281419e-04  1.118962e-03  ...  1.374683e-03  5.480906e-04   \n",
       "210.0       2.920338e-03  7.632395e-03  ...  9.376656e-03  3.738503e-03   \n",
       "270.0       1.389737e-02  3.632120e-02  ...  4.462183e-02  1.779087e-02   \n",
       "...                  ...           ...  ...           ...           ...   \n",
       "5730.0      7.839547e-14  7.112618e-13  ...  2.220879e-07  3.073871e-07   \n",
       "5790.0      2.425039e-14  2.200175e-13  ...  6.869938e-08  9.508530e-08   \n",
       "5850.0      7.207337e-15  6.539030e-14  ...  2.041779e-08  2.825982e-08   \n",
       "5910.0      2.058065e-15  1.867229e-14  ...  5.830329e-09  8.069631e-09   \n",
       "5970.0      5.646398e-16  5.122831e-15  ...  1.599578e-09  2.213941e-09   \n",
       "\n",
       "bincenters   602.210134    728.510216    881.298909    1066.131608  \\\n",
       "30.0        1.344432e-06  3.808201e-07  9.092150e-08  1.829696e-08   \n",
       "90.0        1.883980e-05  5.336510e-06  1.274101e-06  2.563990e-07   \n",
       "150.0       1.841907e-04  5.217335e-05  1.245648e-05  2.506731e-06   \n",
       "210.0       1.256357e-03  3.558723e-04  8.496515e-05  1.709831e-05   \n",
       "270.0       5.978779e-03  1.693533e-03  4.043340e-04  8.136780e-05   \n",
       "...                  ...           ...           ...           ...   \n",
       "5730.0      3.586013e-07  3.526174e-07  2.922545e-07  2.041664e-07   \n",
       "5790.0      1.109276e-07  1.090766e-07  9.040428e-08  6.315564e-08   \n",
       "5850.0      3.296823e-08  3.241810e-08  2.686860e-08  1.877017e-08   \n",
       "5910.0      9.414124e-09  9.257032e-09  7.672365e-09  5.359848e-09   \n",
       "5970.0      2.582809e-09  2.539711e-09  2.104950e-09  1.470500e-09   \n",
       "\n",
       "bincenters   1289.728823   1560.220544   1887.441843   2283.290476  \n",
       "30.0        3.103537e-09  4.437113e-10  5.346995e-11  5.431060e-12  \n",
       "90.0        4.349049e-08  6.217817e-09  7.492852e-10  7.610656e-11  \n",
       "150.0       4.251926e-07  6.078961e-08  7.325522e-09  7.440695e-10  \n",
       "210.0       2.900221e-06  4.146434e-07  4.996708e-08  5.075267e-09  \n",
       "270.0       1.380163e-05  1.973214e-06  2.377844e-07  2.415229e-08  \n",
       "...                  ...           ...           ...           ...  \n",
       "5730.0      1.202190e-07  5.966609e-08  2.496018e-08  8.801028e-09  \n",
       "5790.0      3.718784e-08  1.845676e-08  7.721035e-09  2.722458e-09  \n",
       "5850.0      1.105241e-08  5.485440e-09  2.294730e-09  8.091280e-10  \n",
       "5910.0      3.156031e-09  1.566375e-09  6.552632e-10  2.310476e-10  \n",
       "5970.0      8.658720e-10  4.297423e-10  1.797746e-10  6.338901e-11  \n",
       "\n",
       "[100 rows x 29 columns]"
      ]
     },
     "execution_count": 3,
     "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": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T22:03:51.030003Z",
     "iopub.status.busy": "2023-11-28T22:03:51.029161Z",
     "iopub.status.idle": "2023-11-28T22:03:51.042788Z",
     "shell.execute_reply": "2023-11-28T22:03:51.040983Z",
     "shell.execute_reply.started": "2023-11-28T22:03:51.029919Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  10.        ,   12.09727592,   14.63440848,   17.70364774,\n",
       "         21.41659115,   25.90824126,   31.34191432,   37.91517855,\n",
       "         45.86703767,   55.48662105,   67.1236965 ,   81.20138776,\n",
       "         98.23155932,  118.83342776,  143.75607647,  173.90569229,\n",
       "        210.37851445,  254.50069379,  307.87651158,  372.44671113,\n",
       "        450.55906317,  545.05373075,  659.36653747,  797.65389392,\n",
       "        964.9439247 , 1167.31929089, 1412.1383554 , 1708.3027329 ,\n",
       "       2066.58095226, 2500.        ])"
      ]
     },
     "execution_count": 4,
     "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": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T22:04:00.741788Z",
     "iopub.status.busy": "2023-11-28T22:04:00.740908Z",
     "iopub.status.idle": "2023-11-28T22:04:00.752028Z",
     "shell.execute_reply": "2023-11-28T22:04:00.750060Z",
     "shell.execute_reply.started": "2023-11-28T22:04:00.741700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dNdDp'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd.distributionType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T22:04:59.419589Z",
     "iopub.status.busy": "2023-11-28T22:04:59.419184Z",
     "iopub.status.idle": "2023-11-28T22:04:59.431688Z",
     "shell.execute_reply": "2023-11-28T22:04:59.430165Z",
     "shell.execute_reply.started": "2023-11-28T22:04:59.419545Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0.,   60.],\n",
       "       [  60.,  120.],\n",
       "       [ 120.,  180.],\n",
       "       [ 180.,  240.],\n",
       "       [ 240.,  300.],\n",
       "       [ 300.,  360.],\n",
       "       [ 360.,  420.],\n",
       "       [ 420.,  480.],\n",
       "       [ 480.,  540.],\n",
       "       [ 540.,  600.],\n",
       "       [ 600.,  660.],\n",
       "       [ 660.,  720.],\n",
       "       [ 720.,  780.],\n",
       "       [ 780.,  840.],\n",
       "       [ 840.,  900.],\n",
       "       [ 900.,  960.],\n",
       "       [ 960., 1020.],\n",
       "       [1020., 1080.],\n",
       "       [1080., 1140.],\n",
       "       [1140., 1200.],\n",
       "       [1200., 1260.],\n",
       "       [1260., 1320.],\n",
       "       [1320., 1380.],\n",
       "       [1380., 1440.],\n",
       "       [1440., 1500.],\n",
       "       [1500., 1560.],\n",
       "       [1560., 1620.],\n",
       "       [1620., 1680.],\n",
       "       [1680., 1740.],\n",
       "       [1740., 1800.],\n",
       "       [1800., 1860.],\n",
       "       [1860., 1920.],\n",
       "       [1920., 1980.],\n",
       "       [1980., 2040.],\n",
       "       [2040., 2100.],\n",
       "       [2100., 2160.],\n",
       "       [2160., 2220.],\n",
       "       [2220., 2280.],\n",
       "       [2280., 2340.],\n",
       "       [2340., 2400.],\n",
       "       [2400., 2460.],\n",
       "       [2460., 2520.],\n",
       "       [2520., 2580.],\n",
       "       [2580., 2640.],\n",
       "       [2640., 2700.],\n",
       "       [2700., 2760.],\n",
       "       [2760., 2820.],\n",
       "       [2820., 2880.],\n",
       "       [2880., 2940.],\n",
       "       [2940., 3000.],\n",
       "       [3000., 3060.],\n",
       "       [3060., 3120.],\n",
       "       [3120., 3180.],\n",
       "       [3180., 3240.],\n",
       "       [3240., 3300.],\n",
       "       [3300., 3360.],\n",
       "       [3360., 3420.],\n",
       "       [3420., 3480.],\n",
       "       [3480., 3540.],\n",
       "       [3540., 3600.],\n",
       "       [3600., 3660.],\n",
       "       [3660., 3720.],\n",
       "       [3720., 3780.],\n",
       "       [3780., 3840.],\n",
       "       [3840., 3900.],\n",
       "       [3900., 3960.],\n",
       "       [3960., 4020.],\n",
       "       [4020., 4080.],\n",
       "       [4080., 4140.],\n",
       "       [4140., 4200.],\n",
       "       [4200., 4260.],\n",
       "       [4260., 4320.],\n",
       "       [4320., 4380.],\n",
       "       [4380., 4440.],\n",
       "       [4440., 4500.],\n",
       "       [4500., 4560.],\n",
       "       [4560., 4620.],\n",
       "       [4620., 4680.],\n",
       "       [4680., 4740.],\n",
       "       [4740., 4800.],\n",
       "       [4800., 4860.],\n",
       "       [4860., 4920.],\n",
       "       [4920., 4980.],\n",
       "       [4980., 5040.],\n",
       "       [5040., 5100.],\n",
       "       [5100., 5160.],\n",
       "       [5160., 5220.],\n",
       "       [5220., 5280.],\n",
       "       [5280., 5340.],\n",
       "       [5340., 5400.],\n",
       "       [5400., 5460.],\n",
       "       [5460., 5520.],\n",
       "       [5520., 5580.],\n",
       "       [5580., 5640.],\n",
       "       [5640., 5700.],\n",
       "       [5700., 5760.],\n",
       "       [5760., 5820.],\n",
       "       [5820., 5880.],\n",
       "       [5880., 5940.],\n",
       "       [5940., 6000.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd.layerbounderies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "create the instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T22:05:26.938603Z",
     "iopub.status.busy": "2023-11-28T22:05:26.937578Z",
     "iopub.status.idle": "2023-11-28T22:05:26.947235Z",
     "shell.execute_reply": "2023-11-28T22:05:26.945103Z",
     "shell.execute_reply.started": "2023-11-28T22:05:26.938510Z"
    }
   },
   "outputs": [],
   "source": [
    "sdc = atmsd.SizeDist_LS(sd.data, sd.bins, sd.distributionType, sd.layerbounderies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T22:05:29.062637Z",
     "iopub.status.busy": "2023-11-28T22:05:29.061732Z",
     "iopub.status.idle": "2023-11-28T22:05:29.739788Z",
     "shell.execute_reply": "2023-11-28T22:05:29.738262Z",
     "shell.execute_reply.started": "2023-11-28T22:05:29.062578Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1280x960 with 2 Axes>,\n",
       " <Axes: xlabel='Diameter (nm)', ylabel='Altitude (m)'>,\n",
       " <matplotlib.collections.QuadMesh at 0x7efdf7dfcd60>,\n",
       " <matplotlib.colorbar.Colorbar at 0x7efdf7c06470>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sd.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### hygroscopic growth and optical properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The difference to hygroscopic growth and optical properties of the SizeDist instance is that you can let the RH and refractive index change over time.\n",
    "\n",
    "**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": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T22:17:55.512317Z",
     "iopub.status.busy": "2023-11-28T22:17:55.511978Z",
     "iopub.status.idle": "2023-11-28T22:17:55.521024Z",
     "shell.execute_reply": "2023-11-28T22:17:55.520381Z",
     "shell.execute_reply.started": "2023-11-28T22:17:55.512285Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'atmPy.aerosols.size_distribution.sizedistribution' from '/export/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reload(atmsd.hygroscopicity)\n",
    "reload(atmsd.optical_properties)\n",
    "reload(atmsd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T22:17:55.735230Z",
     "iopub.status.busy": "2023-11-28T22:17:55.734695Z",
     "iopub.status.idle": "2023-11-28T22:17:55.740500Z",
     "shell.execute_reply": "2023-11-28T22:17:55.739510Z",
     "shell.execute_reply.started": "2023-11-28T22:17:55.735193Z"
    }
   },
   "outputs": [],
   "source": [
    "sd = atmsd.SizeDist_LS(sd.data, sd.bins, sd.distributionType, sd.layerbounderies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T22:17:55.977255Z",
     "iopub.status.busy": "2023-11-28T22:17:55.976374Z",
     "iopub.status.idle": "2023-11-28T22:17:55.983611Z",
     "shell.execute_reply": "2023-11-28T22:17:55.982673Z",
     "shell.execute_reply.started": "2023-11-28T22:17:55.977179Z"
    }
   },
   "outputs": [],
   "source": [
    "sd.optical_properties.parameters.refractive_index = 1.5\n",
    "sd.optical_properties.parameters.wavelength = 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T22:17:56.202399Z",
     "iopub.status.busy": "2023-11-28T22:17:56.201074Z",
     "iopub.status.idle": "2023-11-28T22:17:56.216822Z",
     "shell.execute_reply": "2023-11-28T22:17:56.215967Z",
     "shell.execute_reply.started": "2023-11-28T22:17:56.202321Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 1)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rh = pd.DataFrame(index = sd.data.index, columns=['RH'], dtype=float)\n",
    "rh.RH.iloc[[0,-1]] = [0,95]\n",
    "rh = rh.interpolate()\n",
    "rh.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T22:17:56.424664Z",
     "iopub.status.busy": "2023-11-28T22:17:56.423849Z",
     "iopub.status.idle": "2023-11-28T22:17:56.433292Z",
     "shell.execute_reply": "2023-11-28T22:17:56.431517Z",
     "shell.execute_reply.started": "2023-11-28T22:17:56.424585Z"
    }
   },
   "outputs": [],
   "source": [
    "sd.hygroscopicity.parameters.kappa = 1.5\n",
    "sd.hygroscopicity.parameters.RH = rh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T22:17:56.675082Z",
     "iopub.status.busy": "2023-11-28T22:17:56.674012Z",
     "iopub.status.idle": "2023-11-28T22:18:19.257903Z",
     "shell.execute_reply": "2023-11-28T22:18:19.256981Z",
     "shell.execute_reply.started": "2023-11-28T22:17:56.674976Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1280x960 with 2 Axes>,\n",
       " <Axes: xlabel='Diameter (nm)', ylabel='Altitude (m)'>,\n",
       " <matplotlib.collections.QuadMesh at 0x7efdec2d9060>,\n",
       " <matplotlib.colorbar.Colorbar at 0x7efde9db9900>)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1280x960 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sd.hygroscopicity.grown_size_distribution.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T22:18:19.259569Z",
     "iopub.status.busy": "2023-11-28T22:18:19.259320Z",
     "iopub.status.idle": "2023-11-28T22:18:21.498145Z",
     "shell.execute_reply": "2023-11-28T22:18:21.497377Z",
     "shell.execute_reply.started": "2023-11-28T22:18:19.259541Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Altitude'>"
      ]
     },
     "execution_count": 49,
     "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": [
    "a = sd.optical_properties.scattering_coeff.plot()\n",
    "sd.hygroscopicity.grown_size_distribution.optical_properties.scattering_coeff.plot(ax = a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**, the refractive index of the hygroscopically grown size distribution is changing and approaches that of water."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T22:18:21.499662Z",
     "iopub.status.busy": "2023-11-28T22:18:21.499377Z",
     "iopub.status.idle": "2023-11-28T22:18:21.507880Z",
     "shell.execute_reply": "2023-11-28T22:18:21.506959Z",
     "shell.execute_reply.started": "2023-11-28T22:18:21.499632Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "asphericity      :  1\n",
       "mie_result       :  None\n",
       "refractive_index :          index_of_refraction\n",
       "60.0               1.500000\n",
       "180.0              1.495104\n",
       "300.0              1.490300\n",
       "420.0              1.485588\n",
       "540.0              1.480963\n",
       "660.0              1.476423\n",
       "780.0              1.471967\n",
       "900.0              1.467592\n",
       "1020.0             1.463295\n",
       "1140.0             1.459076\n",
       "1260.0             1.454930\n",
       "1380.0             1.450858\n",
       "1500.0             1.446856\n",
       "1620.0             1.442923\n",
       "1740.0             1.439057\n",
       "1860.0             1.435256\n",
       "1980.0             1.431519\n",
       "2100.0             1.427845\n",
       "2220.0             1.424231\n",
       "2340.0             1.420676\n",
       "2460.0             1.417179\n",
       "2580.0             1.413739\n",
       "2700.0             1.410353\n",
       "2820.0             1.407021\n",
       "2940.0             1.403742\n",
       "3060.0             1.400513\n",
       "3180.0             1.397335\n",
       "3300.0             1.394205\n",
       "3420.0             1.391124\n",
       "3540.0             1.388088\n",
       "3660.0             1.385099\n",
       "3780.0             1.382154\n",
       "3900.0             1.379252\n",
       "4020.0             1.376394\n",
       "4140.0             1.373576\n",
       "4260.0             1.370800\n",
       "4380.0             1.368064\n",
       "4500.0             1.365366\n",
       "4620.0             1.362707\n",
       "4740.0             1.360085\n",
       "4860.0             1.357500\n",
       "4980.0             1.354951\n",
       "5100.0             1.352437\n",
       "5220.0             1.349957\n",
       "5340.0             1.347511\n",
       "5460.0             1.345098\n",
       "5580.0             1.342717\n",
       "5700.0             1.340368\n",
       "5820.0             1.338050\n",
       "5940.0             1.335763\n",
       "wavelength       :  500"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd.hygroscopicity.grown_size_distribution.optical_properties.parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AOD (aerosol optical depth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A vertical profile of aerosol optical properties can be integrated to the AOD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T22:22:29.135442Z",
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     "iopub.status.idle": "2023-11-28T22:22:29.147788Z",
     "shell.execute_reply": "2023-11-28T22:22:29.146001Z",
     "shell.execute_reply.started": "2023-11-28T22:22:29.135351Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7.599809165410463, 13.401056561321452)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd.optical_properties.aod, sd.hygroscopicity.grown_size_distribution.optical_properties.aod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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