Droplet Diameter Calculator & Distribution Plot Generator.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c4c31c83",
   "metadata": {},
   "source": [
    "# Droplet Diameter Calculator & Distribution Plot Generator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffaf5a95",
   "metadata": {},
   "source": [
    "## The Droplet Diameter Calculator \n",
    "The Droplet Diameter Calculator function takes a .csv file containing droplet measurement results as input, and returns the same .csv file with additional columns for the diameters of all the droplets. Before using the function, make sure to check that the file path is aligned with your directory where the .csv file is located."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b7823cc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>73.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13.873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>37.566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>34.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>76.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>13.754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>28.312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>12.696</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Area\n",
       "0    0.313\n",
       "1    0.209\n",
       "2   22.337\n",
       "3   51.096\n",
       "4   73.045\n",
       "5    1.132\n",
       "6   13.873\n",
       "7    2.086\n",
       "8    6.407\n",
       "9   37.566\n",
       "10  34.511\n",
       "11   4.232\n",
       "12   0.447\n",
       "13   8.970\n",
       "14  14.186\n",
       "15  76.100\n",
       "16  13.754\n",
       "17   3.591\n",
       "18  28.312\n",
       "19   0.298\n",
       "20  12.070\n",
       "21   7.018\n",
       "22  12.696"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('0.tif_result.csv')\n",
    "df = df.drop([' ','Mean','Min','Max'], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6d0bbff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "df[\"Radius\"] = np.sqrt(df[\"Area\"] / math.pi)\n",
    "df[\"Diameter\"] = df[\"Radius\"] * 2\n",
    "\n",
    "df = np.round(df, decimals=3)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24e47799",
   "metadata": {},
   "source": [
    "# Batch Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "36cd0fc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "from pathlib import Path  \n",
    "import os\n",
    "\n",
    "concentrations = ['50','100','150','300','500']\n",
    "\n",
    "for conc in concentrations:\n",
    "    data_path = './results/' + conc + '.tif_result.csv'\n",
    "    if os.path.isfile(data_path) == True:\n",
    "        df = pd.read_csv(data_path)\n",
    "        df = df.drop([' ','Mean','Min','Max'], axis=1)\n",
    "        df[\"Radius\"] = np.sqrt(df[\"Area\"] / math.pi)\n",
    "        df[\"Diameter\"] = df[\"Radius\"] * 2\n",
    "        df = np.round(df, decimals=3)\n",
    "        filepath = Path('./results/transform/' + conc + '_dataframe.csv') \n",
    "        filepath.parent.mkdir(parents=True, exist_ok=True)\n",
    "        df.to_csv(filepath)\n",
    "    else:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0007446a",
   "metadata": {},
   "source": [
    "# The Distribution Plot Generator\n",
    "\n",
    "The Distribution Plot Generator function takes the .csv file resulting from the Droplet Diameter Calculator function and creates a violin plot to display the distribution of droplet diameters. This is accomplished using the Seaborn library to generate the visualization. Also check the path where you graph will be saved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3d5fe2a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Concentration</th>\n",
       "      <th>Diameter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>0.313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>0.209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>22.337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>51.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>73.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>300</td>\n",
       "      <td>27.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>300</td>\n",
       "      <td>92.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>300</td>\n",
       "      <td>7.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>300</td>\n",
       "      <td>10.625</td>\n",
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       "      <th>103</th>\n",
       "      <td>300</td>\n",
       "      <td>8.970</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Concentration  Diameter\n",
       "0              50     0.313\n",
       "1              50     0.209\n",
       "2              50    22.337\n",
       "3              50    51.096\n",
       "4              50    73.045\n",
       "..            ...       ...\n",
       "99            300    27.031\n",
       "100           300    92.328\n",
       "101           300     7.004\n",
       "102           300    10.625\n",
       "103           300     8.970\n",
       "\n",
       "[104 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_theme()\n",
    "\n",
    "merged_df = pd.DataFrame(columns=['Concentration','Diameter'])\n",
    "\n",
    "concentrations = ['50','100','150','300','500']\n",
    "\n",
    "for conc in concentrations:\n",
    "    data_path = './results/transform/' + conc + '_dataframe.csv'\n",
    "    if os.path.isfile(data_path) == True:\n",
    "        df = pd.read_csv(data_path)\n",
    "        for index, row in df.iterrows():\n",
    "            merged_df.loc[len(merged_df.index)] = [conc, row['Area']]\n",
    "    else:\n",
    "        continue\n",
    "\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f4c17fad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Concentration', ylabel='Diameter'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=merged_df, x=\"Concentration\", y=\"Diameter\", cut=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "128c1222",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}