DLD-1-AFM-plotting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: ../jupyter_figures: File exists\n"
     ]
    }
   ],
   "source": [
    "import sys, os, glob, warnings, logging\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sw_plotting import change_bar_width\n",
    "from sw_utilities import tukeyTest\n",
    "\n",
    "# logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.DEBUG)\n",
    "logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO)\n",
    "\n",
    "# ignore warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# plotting configuration\n",
    "font = {'family' : 'Arial',\n",
    "        'size'   : 7}\n",
    "matplotlib.rc('font', **font)\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "\n",
    "# Make a folder if it is not already there to store exported figures\n",
    "!mkdir ../jupyter_figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>E_in_kPa</th>\n",
       "      <th>group_id</th>\n",
       "      <th>E_in_Pa</th>\n",
       "      <th>log_E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.194600</td>\n",
       "      <td>Matrigel</td>\n",
       "      <td>194.600</td>\n",
       "      <td>-0.710857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.196600</td>\n",
       "      <td>Matrigel</td>\n",
       "      <td>196.600</td>\n",
       "      <td>-0.706416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.176000</td>\n",
       "      <td>Matrigel</td>\n",
       "      <td>176.000</td>\n",
       "      <td>-0.754487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.100786</td>\n",
       "      <td>Matrigel</td>\n",
       "      <td>100.786</td>\n",
       "      <td>-0.996600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.271640</td>\n",
       "      <td>Matrigel</td>\n",
       "      <td>271.640</td>\n",
       "      <td>-0.566006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   E_in_kPa  group_id  E_in_Pa     log_E\n",
       "0  0.194600  Matrigel  194.600 -0.710857\n",
       "1  0.196600  Matrigel  196.600 -0.706416\n",
       "2  0.176000  Matrigel  176.000 -0.754487\n",
       "3  0.100786  Matrigel  100.786 -0.996600\n",
       "4  0.271640  Matrigel  271.640 -0.566006"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/DLD-1-spheroids-AFM/210302-AFM-data-SRL-AXCR-average-values.csv')\n",
    "# add column with Pa unit\n",
    "df['E_in_Pa'] = df.E_in_kPa * 1000\n",
    "df['log_E'] = np.log10(df['E_in_kPa'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">E_in_kPa</th>\n",
       "      <th colspan=\"5\" halign=\"left\">E_in_Pa</th>\n",
       "      <th colspan=\"8\" halign=\"left\">log_E</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BB-94</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0.212413</td>\n",
       "      <td>0.069755</td>\n",
       "      <td>0.123640</td>\n",
       "      <td>0.149489</td>\n",
       "      <td>0.203630</td>\n",
       "      <td>0.250145</td>\n",
       "      <td>0.33430</td>\n",
       "      <td>18.0</td>\n",
       "      <td>212.412778</td>\n",
       "      <td>...</td>\n",
       "      <td>250.14500</td>\n",
       "      <td>334.30</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-0.695662</td>\n",
       "      <td>0.146440</td>\n",
       "      <td>-0.907841</td>\n",
       "      <td>-0.825408</td>\n",
       "      <td>-0.691163</td>\n",
       "      <td>-0.601808</td>\n",
       "      <td>-0.475864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fixed</th>\n",
       "      <td>11.0</td>\n",
       "      <td>2.621055</td>\n",
       "      <td>1.187230</td>\n",
       "      <td>0.743400</td>\n",
       "      <td>1.404400</td>\n",
       "      <td>2.978600</td>\n",
       "      <td>3.316100</td>\n",
       "      <td>4.35520</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2621.054545</td>\n",
       "      <td>...</td>\n",
       "      <td>3316.10000</td>\n",
       "      <td>4355.20</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.364455</td>\n",
       "      <td>0.245651</td>\n",
       "      <td>-0.128777</td>\n",
       "      <td>0.147448</td>\n",
       "      <td>0.474012</td>\n",
       "      <td>0.520516</td>\n",
       "      <td>0.639008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laminin</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.145108</td>\n",
       "      <td>0.082253</td>\n",
       "      <td>0.028278</td>\n",
       "      <td>0.103262</td>\n",
       "      <td>0.127540</td>\n",
       "      <td>0.185576</td>\n",
       "      <td>0.37658</td>\n",
       "      <td>20.0</td>\n",
       "      <td>145.108450</td>\n",
       "      <td>...</td>\n",
       "      <td>185.57625</td>\n",
       "      <td>376.58</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.909031</td>\n",
       "      <td>0.268878</td>\n",
       "      <td>-1.548551</td>\n",
       "      <td>-0.986130</td>\n",
       "      <td>-0.894442</td>\n",
       "      <td>-0.731505</td>\n",
       "      <td>-0.424143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matrigel</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.199470</td>\n",
       "      <td>0.094648</td>\n",
       "      <td>0.057064</td>\n",
       "      <td>0.135780</td>\n",
       "      <td>0.194600</td>\n",
       "      <td>0.260810</td>\n",
       "      <td>0.38564</td>\n",
       "      <td>15.0</td>\n",
       "      <td>199.470000</td>\n",
       "      <td>...</td>\n",
       "      <td>260.81000</td>\n",
       "      <td>385.64</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-0.754075</td>\n",
       "      <td>0.237958</td>\n",
       "      <td>-1.243638</td>\n",
       "      <td>-0.867202</td>\n",
       "      <td>-0.710857</td>\n",
       "      <td>-0.584051</td>\n",
       "      <td>-0.413818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_Matrix</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.077343</td>\n",
       "      <td>0.042785</td>\n",
       "      <td>0.017778</td>\n",
       "      <td>0.043396</td>\n",
       "      <td>0.066451</td>\n",
       "      <td>0.112011</td>\n",
       "      <td>0.16268</td>\n",
       "      <td>14.0</td>\n",
       "      <td>77.343464</td>\n",
       "      <td>...</td>\n",
       "      <td>112.01150</td>\n",
       "      <td>162.68</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-1.184143</td>\n",
       "      <td>0.276305</td>\n",
       "      <td>-1.750117</td>\n",
       "      <td>-1.362951</td>\n",
       "      <td>-1.177500</td>\n",
       "      <td>-0.951899</td>\n",
       "      <td>-0.788666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          E_in_kPa                                                    \\\n",
       "             count      mean       std       min       25%       50%   \n",
       "group_id                                                               \n",
       "BB-94         18.0  0.212413  0.069755  0.123640  0.149489  0.203630   \n",
       "Fixed         11.0  2.621055  1.187230  0.743400  1.404400  2.978600   \n",
       "Laminin       20.0  0.145108  0.082253  0.028278  0.103262  0.127540   \n",
       "Matrigel      15.0  0.199470  0.094648  0.057064  0.135780  0.194600   \n",
       "No_Matrix     14.0  0.077343  0.042785  0.017778  0.043396  0.066451   \n",
       "\n",
       "                             E_in_Pa               ...                       \\\n",
       "                75%      max   count         mean  ...         75%      max   \n",
       "group_id                                           ...                        \n",
       "BB-94      0.250145  0.33430    18.0   212.412778  ...   250.14500   334.30   \n",
       "Fixed      3.316100  4.35520    11.0  2621.054545  ...  3316.10000  4355.20   \n",
       "Laminin    0.185576  0.37658    20.0   145.108450  ...   185.57625   376.58   \n",
       "Matrigel   0.260810  0.38564    15.0   199.470000  ...   260.81000   385.64   \n",
       "No_Matrix  0.112011  0.16268    14.0    77.343464  ...   112.01150   162.68   \n",
       "\n",
       "          log_E                                                              \\\n",
       "          count      mean       std       min       25%       50%       75%   \n",
       "group_id                                                                      \n",
       "BB-94      18.0 -0.695662  0.146440 -0.907841 -0.825408 -0.691163 -0.601808   \n",
       "Fixed      11.0  0.364455  0.245651 -0.128777  0.147448  0.474012  0.520516   \n",
       "Laminin    20.0 -0.909031  0.268878 -1.548551 -0.986130 -0.894442 -0.731505   \n",
       "Matrigel   15.0 -0.754075  0.237958 -1.243638 -0.867202 -0.710857 -0.584051   \n",
       "No_Matrix  14.0 -1.184143  0.276305 -1.750117 -1.362951 -1.177500 -0.951899   \n",
       "\n",
       "                     \n",
       "                max  \n",
       "group_id             \n",
       "BB-94     -0.475864  \n",
       "Fixed      0.639008  \n",
       "Laminin   -0.424143  \n",
       "Matrigel  -0.413818  \n",
       "No_Matrix -0.788666  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('group_id').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of test:\n",
      "   Multiple Comparison of Means - Tukey HSD, FWER=0.05   \n",
      "========================================================\n",
      " group1    group2  meandiff p-adj  lower   upper  reject\n",
      "--------------------------------------------------------\n",
      "   BB-94     Fixed   2.4086 0.001  1.9323   2.885   True\n",
      "   BB-94   Laminin  -0.0673   0.9 -0.4717  0.3371  False\n",
      "   BB-94  Matrigel  -0.0129   0.9 -0.4481  0.4222  False\n",
      "   BB-94 No_Matrix  -0.1351   0.9 -0.5786  0.3085  False\n",
      "   Fixed   Laminin  -2.4759 0.001 -2.9432 -2.0087   True\n",
      "   Fixed  Matrigel  -2.4216 0.001 -2.9157 -1.9275   True\n",
      "   Fixed No_Matrix  -2.5437 0.001 -3.0452 -2.0422   True\n",
      " Laminin  Matrigel   0.0544   0.9 -0.3708  0.4795  False\n",
      " Laminin No_Matrix  -0.0678   0.9 -0.5015   0.366  False\n",
      "Matrigel No_Matrix  -0.1221   0.9 -0.5847  0.3404  False\n",
      "--------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.001 0.9   0.9   0.9   0.001 0.001 0.001 0.9   0.9   0.9  ]\n",
      "Summary of test:\n",
      "    Multiple Comparison of Means - Tukey HSD, FWER=0.05   \n",
      "=========================================================\n",
      " group1    group2  meandiff p-adj   lower   upper  reject\n",
      "---------------------------------------------------------\n",
      "   BB-94     Fixed   1.0601  0.001  0.8056  1.3146   True\n",
      "   BB-94   Laminin  -0.2134 0.0546 -0.4294  0.0027  False\n",
      "   BB-94  Matrigel  -0.0584    0.9 -0.2909  0.1741  False\n",
      "   BB-94 No_Matrix  -0.4885  0.001 -0.7255 -0.2515   True\n",
      "   Fixed   Laminin  -1.2735  0.001 -1.5231 -1.0238   True\n",
      "   Fixed  Matrigel  -1.1185  0.001 -1.3825 -0.8545   True\n",
      "   Fixed No_Matrix  -1.5486  0.001 -1.8166 -1.2806   True\n",
      " Laminin  Matrigel    0.155 0.3223 -0.0722  0.3821  False\n",
      " Laminin No_Matrix  -0.2751 0.0119 -0.5069 -0.0434   True\n",
      "Matrigel No_Matrix  -0.4301  0.001 -0.6772 -0.1829   True\n",
      "---------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.001      0.05462197 0.9        0.001      0.001      0.001\n",
      " 0.001      0.32229552 0.01189299 0.001     ]\n",
      "Summary of test:\n",
      "    Multiple Comparison of Means - Tukey HSD, FWER=0.05   \n",
      "=========================================================\n",
      " group1    group2  meandiff p-adj   lower   upper  reject\n",
      "---------------------------------------------------------\n",
      "   BB-94   Laminin  -0.0673 0.0391 -0.1322 -0.0024   True\n",
      "   BB-94  Matrigel  -0.0129    0.9 -0.0827  0.0568  False\n",
      "   BB-94 No_Matrix  -0.1351  0.001 -0.2062 -0.0639   True\n",
      " Laminin  Matrigel   0.0544 0.1631 -0.0138  0.1225  False\n",
      " Laminin No_Matrix  -0.0678  0.059 -0.1373  0.0018  False\n",
      "Matrigel No_Matrix  -0.1221  0.001 -0.1963 -0.0479   True\n",
      "---------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.03908244 0.9        0.001      0.16314191 0.05897961 0.001     ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fb4b5f5b950>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note that because of the fixed sample is extremely stiff,\n",
    "# it dominates the Tukey test and makes all other pairwise\n",
    "# comparisons to be not different.\n",
    "#\n",
    "# This is because the raw data of fixed samples has vastly\n",
    "# different scales of variance, which violates the assumptions\n",
    "# of Tukey test (homogeneity of variance).\n",
    "# \n",
    "# For robust comparisons, we performed stats on log-transformed\n",
    "# data which satistifies the Tukey test assumption.\n",
    "#\n",
    "# Another strategy is to compare groups without the fixed group.\n",
    "\n",
    "# All groups\n",
    "tukeyTest(df.E_in_kPa, df.group_id)\n",
    "\n",
    "# Compare log-transformed data\n",
    "tukeyTest(df.log_E, df.group_id)\n",
    "\n",
    "# Without fixed\n",
    "df_filtered = df[df.group_id != 'Fixed']\n",
    "tukeyTest(df_filtered.E_in_kPa, df_filtered.group_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "ax = sns.boxplot(data=df, x='group_id', y='E_in_kPa',\n",
    "                 order=['No_Matrix', 'Matrigel', 'BB-94', 'Laminin', 'Fixed'])\n",
    "\n",
    "sns.swarmplot(data=df, x='group_id', y='E_in_kPa',\n",
    "              order=['No_Matrix', 'Matrigel', 'BB-94', 'Laminin', 'Fixed'], ax=ax)\n",
    "\n",
    "ax.set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='group_id', ylabel='E_in_kPa'>"
      ]
     },
     "execution_count": 5,
     "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": [
    "ax = sns.boxplot(data=df, x='group_id', y='E_in_kPa',\n",
    "                 order=['No_Matrix', 'Matrigel', 'BB-94', 'Laminin'])\n",
    "\n",
    "sns.swarmplot(data=df, x='group_id', y='E_in_kPa',\n",
    "              order=['No_Matrix', 'Matrigel', 'BB-94', 'Laminin'], ax=ax)\n",
    "\n",
    "# ax.set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# ignore laminin\n",
    "ax = sns.boxplot(data=df, x='group_id', y='E_in_kPa',\n",
    "                 order=['No_Matrix', 'Matrigel', 'BB-94', 'Fixed'])\n",
    "sns.swarmplot(data=df, x='group_id', y='E_in_kPa',\n",
    "              order=['No_Matrix', 'Matrigel', 'BB-94', 'Fixed'], ax=ax)\n",
    "ax.set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 270x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make swarm plot with error bars for the raw counts\n",
    "datasetPrefix = 'DLD-1-AFM-E-plot'\n",
    "outputFigPath = '../jupyter_figures/' + datasetPrefix + '.svg'\n",
    "\n",
    "fig_width, fig_height = .9, 1.0\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "xCol, yCol = 'group_id', 'E_in_kPa'\n",
    "ax.set_ylim([0.01, 50])\n",
    "\n",
    "# xCol, yCol = 'group_id', 'E_in_Pa'\n",
    "# ax.set_ylim([10, 50000])\n",
    "\n",
    "plottingOrder = ['No_Matrix', 'Matrigel', 'BB-94', 'Fixed']\n",
    "\n",
    "# # Customize color of each group\n",
    "# paletteDict = {'No_Matrix': 'black', 'Matrigel': 'green',\n",
    "#                'BB-94': 'orange', 'Laminin': 'gray', 'Fixed': 'red'}\n",
    "\n",
    "# Note: it makes a difference to set log scale before or after!\n",
    "ax.set_yscale('log') \n",
    "\n",
    "ax = sns.swarmplot(x=xCol, y=yCol, data=df, order=plottingOrder,\n",
    "#                    hue='group_id', palette=paletteDict,\n",
    "                   size=1.5)\n",
    "ax = sns.pointplot(x=xCol, y=yCol, data=df, order=plottingOrder,\n",
    "                   ci=95, capsize=.2, errwidth=.7,\n",
    "#                    hue='group_id', palette=paletteDict,\n",
    "                   color='Gray',\n",
    "                   markers='_', scale=.5, join=False)\n",
    "\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Young's modulus (kPa)\")\n",
    "# plt.ylabel(\"Young's modulus (Pa)\")\n",
    "\n",
    "# rotate x tick labels if necessary\n",
    "# x_labels = ax.get_xticklabels()\n",
    "x_labels = ['No Matrix', 'Matrigel', 'BB-94', 'Fixed']\n",
    "ax.set_xticklabels(labels=x_labels, rotation=45, ha=\"right\")\n",
    "\n",
    "for o in fig.findobj():\n",
    "    o.set_clip_on(False)\n",
    "for o in ax.findobj():\n",
    "    o.set_clip_on(False)\n",
    "\n",
    "plt.savefig(outputFigPath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 270x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make swarm plot with error bars\n",
    "\n",
    "datasetPrefix = 'DLD-1-AFM-E-plot-with-laminin'\n",
    "outputFigPath = '../jupyter_figures/' + datasetPrefix + '.svg'\n",
    "\n",
    "fig_width, fig_height = 0.9, 1.0\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "xCol, yCol = 'group_id', 'E_in_kPa'\n",
    "plottingOrder = ['No_Matrix', 'Matrigel', 'BB-94', 'Laminin', 'Fixed']\n",
    "\n",
    "# # Customize color of each group\n",
    "# paletteDict = {'No_Matrix': 'black', 'Matrigel': 'green',\n",
    "#                'BB-94': 'orange', 'Laminin': 'gray', 'Fixed': 'red'}\n",
    "\n",
    "# Note: it makes a difference to set log scale before or after!\n",
    "ax.set_yscale('log') \n",
    "\n",
    "ax = sns.swarmplot(x=xCol, y=yCol, data=df, order=plottingOrder,\n",
    "#                    hue='group_id', palette=paletteDict,\n",
    "                   size=1.5)\n",
    "ax = sns.pointplot(x=xCol, y=yCol, data=df, order=plottingOrder,\n",
    "                   ci=95, capsize=.2, errwidth=.7,\n",
    "#                    hue='group_id', palette=paletteDict,\n",
    "                   color='Gray',\n",
    "                   markers='_', scale=.5, join=False)\n",
    "\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Young's modulus (kPa)\")\n",
    "\n",
    "ax.set_ylim([0.01, 10])\n",
    "\n",
    "# rotate x tick labels if necessary\n",
    "# x_labels = ax.get_xticklabels()\n",
    "x_labels = ['No Matrix', 'Matrigel', 'BB-94', 'Laminin', 'Fixed']\n",
    "ax.set_xticklabels(labels=x_labels, rotation=45, ha=\"right\")\n",
    "\n",
    "for o in fig.findobj():\n",
    "    o.set_clip_on(False)\n",
    "for o in ax.findobj():\n",
    "    o.set_clip_on(False)\n",
    "\n",
    "plt.savefig(outputFigPath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_force_indentation_df(force_file):\n",
    "    '''TO COMPLETE\n",
    "    '''\n",
    "    with open(force_file) as f:\n",
    "        lines = f.readlines()\n",
    "\n",
    "    segments = []\n",
    "    data_lines = []\n",
    "    for line in lines:\n",
    "        if line.startswith('# recorded-num-points:'):\n",
    "            segments.append(int(line.strip().split(':')[1].strip()))\n",
    "        if len(line.strip())>0 and (not line.startswith('#')):\n",
    "            data_lines.append(line)\n",
    "\n",
    "    data = [line.strip().split(' ') for line in data_lines]\n",
    "    df = pd.DataFrame(data)\n",
    "    df = df.astype('float')\n",
    "    df.columns = ['Indentation (µm)', 'Force (nN)']\n",
    "    df['Indentation (µm)'] = df['Indentation (µm)']*1e6\n",
    "    df['Force (nN)'] = df['Force (nN)']*1e9\n",
    "    df['segments'] = ['approach']*segments[0] + ['withdraw']*segments[1]\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "force_file = '../data/DLD-1-spheroids-AFM/force-curve-examples/new_spheroid7.4_3nN_10zl_5speed.txt'\n",
    "\n",
    "df = get_force_indentation_df(force_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Indentation (µm)    float64\n",
       "Force (nN)          float64\n",
       "segments             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 420x240 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make swarm plot with error bars for the raw counts\n",
    "datasetPrefix = 'DLD-1-AFM-force-curve-example'\n",
    "outputFigPath = '../jupyter_figures/' + datasetPrefix + '.svg'\n",
    "\n",
    "fig_width, fig_height = 1.4, 0.8\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "xCol, yCol = 'Indentation (µm)', 'Force (nN)'\n",
    "# plottingOrder = ['No_Matrix', 'Laminin', 'Matrigel', 'BB-94', 'Fixed']\n",
    "\n",
    "# Customize color of each group\n",
    "paletteDict = {'approach': 'red', 'withdraw': '.4'}\n",
    "hue_order = ['withdraw', 'approach']\n",
    "\n",
    "ax.axhline(y=0, ls='--', lw=.8, c='k')\n",
    "ax.axvline(x=0, ls='--', lw=.8, c='k')\n",
    "\n",
    "ax = sns.lineplot(x=xCol, y=yCol,\n",
    "                  size=.8, legend=False,\n",
    "                  hue_order=hue_order, hue='segments', palette=paletteDict,\n",
    "                  data=df)\n",
    "\n",
    "plt.ylim([-1.5,4])\n",
    "plt.xticks([-2, 0, 2, 4, 6])\n",
    "# rotate x tick labels if necessary\n",
    "# x_labels = ax.get_xticklabels()\n",
    "# x_labels = ['No Matrix', 'Laminin', 'Matrigel', 'BB-94', 'Fixed']\n",
    "# ax.set_xticklabels(labels=x_labels, rotation=45, ha=\"right\")\n",
    "\n",
    "for o in fig.findobj():\n",
    "    o.set_clip_on(False)\n",
    "for o in ax.findobj():\n",
    "    o.set_clip_on(False)\n",
    "\n",
    "plt.savefig(outputFigPath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make swarm plot with error bars for the raw counts\n",
    "datasetPrefix = 'DLD-1-AFM-force-curve-example'\n",
    "outputFigPath = '../jupyter_figures/' + datasetPrefix + '.png'\n",
    "\n",
    "fig_width, fig_height = 2.0, 1.6\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "ax = fig.add_axes([0.2, 0.25, 0.7, 0.7])\n",
    "\n",
    "xCol, yCol = 'Indentation (µm)', 'Force (nN)'\n",
    "# plottingOrder = ['No_Matrix', 'Laminin', 'Matrigel', 'BB-94', 'Fixed']\n",
    "\n",
    "# Customize color of each group\n",
    "paletteDict = {'approach': 'red', 'withdraw': '.4'}\n",
    "hue_order = ['withdraw', 'approach']\n",
    "\n",
    "ax.axhline(y=0, ls='--', lw=.8, c='k')\n",
    "ax.axvline(x=0, ls='--', lw=.8, c='k')\n",
    "\n",
    "ax = sns.lineplot(x=xCol, y=yCol,\n",
    "                  size=.8, legend=False,\n",
    "                  hue_order=hue_order, hue='segments', palette=paletteDict,\n",
    "                  data=df)\n",
    "\n",
    "ax.set_yticks([0, 1, 2, 3])\n",
    "\n",
    "# rotate x tick labels if necessary\n",
    "# x_labels = ax.get_xticklabels()\n",
    "# x_labels = ['No Matrix', 'Laminin', 'Matrigel', 'BB-94', 'Fixed']\n",
    "# ax.set_xticklabels(labels=x_labels, rotation=45, ha=\"right\")\n",
    "\n",
    "for o in fig.findobj():\n",
    "    o.set_clip_on(False)\n",
    "for o in ax.findobj():\n",
    "    o.set_clip_on(False)\n",
    "\n",
    "plt.savefig(outputFigPath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}