diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..059eb39
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,414 @@
+
+# Created by https://www.toptal.com/developers/gitignore/api/python,intellij,pycharm,macos,windows,linux,vim
+# Edit at https://www.toptal.com/developers/gitignore?templates=python,intellij,pycharm,macos,windows,linux,vim
+
+### Intellij ###
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# AWS User-specific
+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+### Intellij Patch ###
+# Comment Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-215987721
+
+# *.iml
+# modules.xml
+# .idea/misc.xml
+# *.ipr
+
+# Sonarlint plugin
+# https://plugins.jetbrains.com/plugin/7973-sonarlint
+.idea/**/sonarlint/
+
+# SonarQube Plugin
+# https://plugins.jetbrains.com/plugin/7238-sonarqube-community-plugin
+.idea/**/sonarIssues.xml
+
+# Markdown Navigator plugin
+# https://plugins.jetbrains.com/plugin/7896-markdown-navigator-enhanced
+.idea/**/markdown-navigator.xml
+.idea/**/markdown-navigator-enh.xml
+.idea/**/markdown-navigator/
+
+# Cache file creation bug
+# See https://youtrack.jetbrains.com/issue/JBR-2257
+.idea/$CACHE_FILE$
+
+# CodeStream plugin
+# https://plugins.jetbrains.com/plugin/12206-codestream
+.idea/codestream.xml
+
+### Linux ###
+*~
+
+# temporary files which can be created if a process still has a handle open of a deleted file
+.fuse_hidden*
+
+# KDE directory preferences
+.directory
+
+# Linux trash folder which might appear on any partition or disk
+.Trash-*
+
+# .nfs files are created when an open file is removed but is still being accessed
+.nfs*
+
+### macOS ###
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+### PyCharm ###
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+
+# AWS User-specific
+
+# Generated files
+
+# Sensitive or high-churn files
+
+# Gradle
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+
+# Mongo Explorer plugin
+
+# File-based project format
+
+# IntelliJ
+
+# mpeltonen/sbt-idea plugin
+
+# JIRA plugin
+
+# Cursive Clojure plugin
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+
+# Editor-based Rest Client
+
+# Android studio 3.1+ serialized cache file
+
+### PyCharm Patch ###
+# Comment Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-215987721
+
+# *.iml
+# modules.xml
+# .idea/misc.xml
+# *.ipr
+
+# Sonarlint plugin
+# https://plugins.jetbrains.com/plugin/7973-sonarlint
+
+# SonarQube Plugin
+# https://plugins.jetbrains.com/plugin/7238-sonarqube-community-plugin
+
+# Markdown Navigator plugin
+# https://plugins.jetbrains.com/plugin/7896-markdown-navigator-enhanced
+
+# Cache file creation bug
+# See https://youtrack.jetbrains.com/issue/JBR-2257
+
+# CodeStream plugin
+# https://plugins.jetbrains.com/plugin/12206-codestream
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+### Vim ###
+# Swap
+[._]*.s[a-v][a-z]
+!*.svg  # comment out if you don't need vector files
+[._]*.sw[a-p]
+[._]s[a-rt-v][a-z]
+[._]ss[a-gi-z]
+[._]sw[a-p]
+
+# Session
+Session.vim
+Sessionx.vim
+
+# Temporary
+.netrwhist
+# Auto-generated tag files
+tags
+# Persistent undo
+[._]*.un~
+
+### Windows ###
+# Windows thumbnail cache files
+Thumbs.db
+Thumbs.db:encryptable
+ehthumbs.db
+ehthumbs_vista.db
+
+# Dump file
+*.stackdump
+
+# Folder config file
+[Dd]esktop.ini
+
+# Recycle Bin used on file shares
+$RECYCLE.BIN/
+
+# Windows Installer files
+*.cab
+*.msi
+*.msix
+*.msm
+*.msp
+
+# Windows shortcuts
+*.lnk
+
+# End of https://www.toptal.com/developers/gitignore/api/python,intellij,pycharm,macos,windows,linux,vim
+
+# Don't commit example results
+ChEMBL_1614027_*
diff --git a/ChEMBL_1614027.gz b/ChEMBL_1614027.gz
deleted file mode 100644
index c1feac3..0000000
Binary files a/ChEMBL_1614027.gz and /dev/null differ
diff --git a/NAA_Workflow_ChEMBL.ipynb b/NAA_Workflow_ChEMBL.ipynb
index 67775d9..ece81b0 100644
--- a/NAA_Workflow_ChEMBL.ipynb
+++ b/NAA_Workflow_ChEMBL.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "1. Data curation and clean up\n",
     "2. Run Nonadditivity Analysis\n",
-    "3. Generate Plots\n"
+    "3. Generate Plots"
    ]
   },
   {
@@ -28,28 +28,28 @@
     "from rdkit.Chem.MolStandardize.standardize import canonicalize_tautomer_smiles\n",
     "from rdkit.Chem import rdFMCS\n",
     "rdBase.DisableLog('rdApp.info')\n",
-    "\n",
+    "import os\n",
+    "from textwrap import dedent\n",
     "import sys\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import seaborn as sns\n",
-    "\n",
+    "from rdkit.Chem import PandasTools\n",
     "from scipy import stats\n",
     "from scipy.stats import normaltest\n",
     "from PIL import Image\n",
     "from PIL import ImageFont\n",
     "from PIL import ImageDraw\n",
-    "\n",
+    "from tqdm.auto import tqdm\n",
     "import multiprocessing as mp\n",
-    "from multiprocessing import Process, Pipe\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set path for reading and writing data"
+    "from multiprocessing import Process, Pipe\n",
+    "\n",
+    "import pystow\n",
+    "import chembl_downloader\n",
+    "from nonadditivity_az.utils import get_processed_assay_df\n",
+    "from nonadditivity_az.plotting import NA_distribution, plot_outliers, draw_image\n",
+    "from nonadditivity.api import run_nonadd_calculation_helper"
    ]
   },
   {
@@ -58,24 +58,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "my_path = './naa/ChEMBL_1614027/'\n",
-    "my_name = 'ChEMBL_1614027'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# <font color='crimson'>STEP I</font>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Data Curation\n",
-    "\n",
-    "* Data example for ChEMBL1614027"
+    "sns.set_style(\"white\")"
    ]
   },
   {
@@ -84,14 +67,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = pd.read_csv(my_path+'ChEMBL_1614027.gz', compression='gzip', header=0, sep=';', error_bad_lines=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Rearrange columns and keep the ones necessary"
+    "# https://www.rdkit.org/docs/Cookbook.html\n",
+    "from rdkit.Chem.Draw import IPythonConsole\n",
+    "IPythonConsole.ipython_useSVG = True"
    ]
   },
   {
@@ -100,18 +78,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def rearrange(df):\n",
-    "    df = df.rename(columns=({'Molecule ChEMBL ID':'COMPOUND_NAME', 'Smiles': 'SMILES', 'Standard Value': 'VALUE', 'Standard Value' : 'VALUE', 'Standard Units': 'UNIT', 'Standard Type': 'ENDPOINT'}))\n",
-    "    df = df[['SMILES', 'COMPOUND_NAME', 'ENDPOINT', 'Standard Relation', 'VALUE', 'UNIT']]\n",
-    "    \n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "###  Remove NaNs from SMILES column"
+    "import matplotlib_inline\n",
+    "matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\")"
    ]
   },
   {
@@ -120,17 +88,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def discard_nan_smiles(df):\n",
-    "    df = df.dropna(subset = ['SMILES'])\n",
-    "    \n",
-    "    return(df)"
+    "PandasTools.ChangeMoleculeRendering(renderer=\"SVG\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Deleting uncertain values"
+    "### Set path for reading and writing data"
    ]
   },
   {
@@ -139,431 +104,690 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def discard_uncertain_values(df):\n",
-    "    df = df[df['Standard Relation'] != \"'>'\"]\n",
-    "    df = df[df['Standard Relation'] != \"'<'\"]\n",
-    "    df['VALUE'] = df['VALUE'].astype(float)\n",
-    "    df = df[df['VALUE'] > 0] #in case one needs to delete negative values\n",
-    "    df = df.drop(columns=['Standard Relation'])\n",
-    "\n",
-    "    return(df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Converting values to logged ones"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "unit_conversion = {\n",
-    "    'M': 1,\n",
-    "    'mM': 1000,\n",
-    "    'uM': 1000000,\n",
-    "    'nM': 1000000000,\n",
-    "    'pM': 1000000000000,\n",
-    "    'fM': 1000000000000000    \n",
-    "}\n",
-    "\n",
-    "def log_converstion(x, UNIT):\n",
-    "    if UNIT not in unit_conversion:\n",
-    "        return x\n",
-    "    x= -1 * np.log10(x / unit_conversion[UNIT])\n",
-    "    return x\n",
-    "\n",
-    "def create_conversion_column(df):\n",
-    "    arr = [log_converstion(x['VALUE'], x['UNIT']) for idx, x in df.iterrows()]\n",
-    "\n",
-    "    df['NEW_VALUE'] = arr\n",
-    "    df = df[df['NEW_VALUE'] > 0]\n",
-    "    df = df.drop(columns=['VALUE'])\n",
-    "    \n",
-    "    # deleting the values that are more than 10 mM and less than 1 fM\n",
-    "    \n",
-    "    df = df[df['NEW_VALUE'] > 2]\n",
-    "    df = df[df['NEW_VALUE'] < 11]\n",
-    "    \n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Calculating the avarage of the activity and calculating the median"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calculate_average(df):\n",
-    "    df['Median_Value'] = df.groupby(['COMPOUND_NAME'])['NEW_VALUE'].transform('median')\n",
-    "    df['max_value'] = df.groupby(['COMPOUND_NAME'])['NEW_VALUE'].transform('max')\n",
-    "    df['min_value'] = df.groupby(['COMPOUND_NAME'])['NEW_VALUE'].transform('min')\n",
-    "    df['difference'] = df.max_value - df.min_value\n",
-    "    \n",
-    "    df = df.drop_duplicates(subset=['COMPOUND_NAME'], keep= 'first')\n",
-    "\n",
-    "    return(df)"
+    "assay_chembl_id = 'CHEMBL1614027'"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Delete compounds that have been measured several times in one test and differ more than 2.5 log units"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def discard_ambiguous_compound_measurements(df, max_thrs = 2.5):\n",
-    "    \n",
-    "    df = df[df.difference < max_thrs]  \n",
-    "    \n",
-    "    df = df[['SMILES', 'COMPOUND_NAME', 'ENDPOINT', 'Median_Value', 'MEASUREMENT']]\n",
-    "    df = df.rename(columns=({'Median_Value':'VALUE'}))\n",
-    "    \n",
-    "    return(df)"
+    "# <font color='crimson'>STEP I</font>"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Standardize molecules using RDkit"
+    "# Data Curation"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def standardize_rdkit(row, col):\n",
-    "    smi = row[col]\n",
-    "\n",
-    "    try:\n",
-    "        mol = Chem.MolFromSmiles(smi)   # sanitization is done by default\n",
-    "        fmol = rdMolStandardize.FragmentParent(mol)   # returns largest fragment\n",
-    "        cmol = rdMolStandardize.ChargeParent(fmol)   # uncharges the largest fragment\n",
-    "        smi = Chem.MolToSmiles(cmol)   \n",
-    "        ssmi = MolStandardize.canonicalize_tautomer_smiles(smi)   # returns the canonicalized tautomer\n",
-    "        tsmi = MolStandardize.rdMolStandardize.StandardizeSmiles(ssmi)   # standardize \n",
-    "    except:\n",
-    "        tsmi = 'none'\n",
-    "    \n",
-    "    return tsmi"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "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>ID</th>\n",
+       "      <th>SMILES</th>\n",
+       "      <th>VALUE</th>\n",
+       "      <th>MEASUREMENT</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CHEMBL598952</td>\n",
+       "      <td>O=c1onc2cnc3ccccc3n12</td>\n",
+       "      <td>4.40000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CHEMBL1358313</td>\n",
+       "      <td>N#CCCn1c(=O)c(-c2cccc(C#N)c2)nc2cnc(Oc3ccccc3)...</td>\n",
+       "      <td>4.40000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CHEMBL11684</td>\n",
+       "      <td>CC1(C)Oc2ccc(C#N)cc2[C@H](N2CCCC2=O)[C@H]1O</td>\n",
+       "      <td>4.40000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CHEMBL1357940</td>\n",
+       "      <td>CS(=O)(=O)N1CCC2(CCN(c3ccccc3)CC2)CC1</td>\n",
+       "      <td>4.40000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CHEMBL302213</td>\n",
+       "      <td>NC[C@@H](CC(=O)O)c1ccc(Cl)cc1</td>\n",
+       "      <td>4.40000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2871</th>\n",
+       "      <td>CHEMBL1568083</td>\n",
+       "      <td>O=C(O)c1cscc1Cc1cccs1</td>\n",
+       "      <td>8.60206</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2872</th>\n",
+       "      <td>CHEMBL1517793</td>\n",
+       "      <td>C[C@H]1CCC[C@@H](C)N1</td>\n",
+       "      <td>8.60206</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2873</th>\n",
+       "      <td>CHEMBL1513940</td>\n",
+       "      <td>COc1ccc2c3c([nH]c2c1)[C@@H]1C[C@H]2C(C(=O)O)[C...</td>\n",
+       "      <td>8.60206</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2874</th>\n",
+       "      <td>CHEMBL1513508</td>\n",
+       "      <td>CCOC(=O)OCC1OC(C#Cc2ccc(C(C)(C)C)cc2)C=CC1OC(=...</td>\n",
+       "      <td>8.60206</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2875</th>\n",
+       "      <td>CHEMBL1337541</td>\n",
+       "      <td>CCCCCCCCCC(=O)N[C@@H](CN1CCOCC1)[C@@H](O)c1ccccc1</td>\n",
+       "      <td>8.79588</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2876 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 ID                                             SMILES  \\\n",
+       "0      CHEMBL598952                              O=c1onc2cnc3ccccc3n12   \n",
+       "1     CHEMBL1358313  N#CCCn1c(=O)c(-c2cccc(C#N)c2)nc2cnc(Oc3ccccc3)...   \n",
+       "2       CHEMBL11684        CC1(C)Oc2ccc(C#N)cc2[C@H](N2CCCC2=O)[C@H]1O   \n",
+       "3     CHEMBL1357940              CS(=O)(=O)N1CCC2(CCN(c3ccccc3)CC2)CC1   \n",
+       "4      CHEMBL302213                      NC[C@@H](CC(=O)O)c1ccc(Cl)cc1   \n",
+       "...             ...                                                ...   \n",
+       "2871  CHEMBL1568083                              O=C(O)c1cscc1Cc1cccs1   \n",
+       "2872  CHEMBL1517793                              C[C@H]1CCC[C@@H](C)N1   \n",
+       "2873  CHEMBL1513940  COc1ccc2c3c([nH]c2c1)[C@@H]1C[C@H]2C(C(=O)O)[C...   \n",
+       "2874  CHEMBL1513508  CCOC(=O)OCC1OC(C#Cc2ccc(C(C)(C)C)cc2)C=CC1OC(=...   \n",
+       "2875  CHEMBL1337541  CCCCCCCCCC(=O)N[C@@H](CN1CCOCC1)[C@@H](O)c1ccccc1   \n",
+       "\n",
+       "        VALUE  MEASUREMENT  \n",
+       "0     4.40000            1  \n",
+       "1     4.40000            1  \n",
+       "2     4.40000            1  \n",
+       "3     4.40000            1  \n",
+       "4     4.40000            1  \n",
+       "...       ...          ...  \n",
+       "2871  8.60206            1  \n",
+       "2872  8.60206            1  \n",
+       "2873  8.60206            1  \n",
+       "2874  8.60206            1  \n",
+       "2875  8.79588            1  \n",
+       "\n",
+       "[2876 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "def generateStandarizedSmiles(smilesfile, smiles_column):\n",
-    "\n",
-    "    pool = mp.Pool(8)   # set number of cores for parallelization\n",
-    "    stsmi_list = pool.starmap(standardize_rdkit, [(smi, smiles_column) for idx, smi in smilesfile.iterrows()])\n",
-    "    pool.close()\n",
-    "    \n",
-    "    smilesfile[smilesfile.columns[smiles_column]] = stsmi_list\n",
-    "\n",
-    "    return (smilesfile)"
+    "df, infile = get_processed_assay_df(assay_chembl_id)\n",
+    "df"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Discard duplicate SMILES\n",
-    "\n",
-    "- Keep the one with the highest value, i.e. most active one"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def merge_duplicate_smiles(df):\n",
-    "    df = df.sort_values('VALUE').drop_duplicates(subset=['SMILES'], keep='last') \n",
-    "    \n",
-    "    return(df)"
+    "# <font color='crimson'>  STEP II"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Remove molecules with > 70 heavy atoms"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def discarding_heavy_mols(smi, min_size = 0, max_size = 70):\n",
-    "    try:\n",
-    "        mol = Chem.MolFromSmiles(smi, sanitize=False)\n",
-    "        if min_size <= mol.GetNumHeavyAtoms() <= max_size:\n",
-    "            return False\n",
-    "        else:\n",
-    "            return True\n",
-    "    except:\n",
-    "        return True \n",
-    "    \n",
-    "def removeHeavyMols(df, smiles_column):\n",
-    "    idx = []\n",
-    "    discard = []\n",
-    "    for index, row in df.iterrows():\n",
-    "    #for smi in df.iloc[:,smiles_column]:\n",
-    "        if discarding_heavy_mols(row[smiles_column]):\n",
-    "            idx.append(index)\n",
-    "            discard.append(row.values.tolist())\n",
+    "# NAA\n",
     "\n",
-    "    df.drop(idx, inplace=True)\n",
-    "    return (df)"
+    "- Code available from gitHub by Christian Kramer: https://github.com/KramerChristian/NonadditivityAnalysis\n",
+    "- Corresponding publication: https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b00631"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Apply all of the above functionalities and safe file"
+    "### Run NAA on cleanup data set"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#cmpds:  3082\n",
-      "#unique cmpds:  2933\n",
-      "#cpds with SMILES:  3047\n",
-      "#cpds with values:  3047\n",
-      "#cpds after merging multi measurements:  2893\n"
+      "Identifier Column found: ID\n",
+      "Smiles column found: SMILES\n",
+      "Activity column #1: VALUE\n",
+      "Generating MMP Fragments\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "RDKit ERROR: [17:00:49] Can't kekulize mol.  Unkekulized atoms: 0 3 6\n",
-      "RDKit ERROR: \n",
-      "RDKit ERROR: [17:00:49] Can't kekulize mol.  Unkekulized atoms: 0 3 6\n",
-      "RDKit ERROR: \n",
-      "RDKit ERROR: [17:00:49] Can't kekulize mol.  Unkekulized atoms: 0 3 6\n",
-      "RDKit ERROR: \n",
-      "RDKit ERROR: [17:00:49] Can't kekulize mol.  Unkekulized atoms: 0 3 6\n",
-      "RDKit ERROR: \n",
-      "RDKit ERROR: [17:00:49] Can't kekulize mol.  Unkekulized atoms: 0 3 6\n",
-      "RDKit ERROR: \n",
-      "RDKit ERROR: [17:00:49] Can't kekulize mol.  Unkekulized atoms: 0 3 6\n",
-      "RDKit ERROR: \n"
+      "                                   \r"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#cpds after merging duplicate SMILES:  2877\n",
-      "#cpds < 70 HA:  2876\n"
+      "Indexing MMP Fragments\n"
      ]
-    }
-   ],
-   "source": [
-    "df = rearrange(data)\n",
-    "print('#cmpds: ', len(df['COMPOUND_NAME']))\n",
-    "print('#unique cmpds: ', len(df['COMPOUND_NAME'].value_counts()))\n",
-    "\n",
-    "# Counting how many times compounds were measured in tests\n",
-    "df['MEASUREMENT'] = df.groupby(['COMPOUND_NAME'])['COMPOUND_NAME'].transform('count')\n",
-    "\n",
-    "# Discard cmpds without SMILES\n",
-    "df = discard_nan_smiles(df)\n",
-    "print('#cpds with SMILES: ', len(df.iloc[:,0]))\n",
-    "\n",
-    "# Discard ambiguous data\n",
-    "df = discard_uncertain_values(df)\n",
-    "print('#cpds with values: ', len(df.iloc[:,0]))\n",
-    "\n",
-    "# Convert IC50 to pIC50\n",
-    "df = create_conversion_column(df) \n",
-    "\n",
-    "# Calculate average values and discard cpds with > 2.5 log unit measurement differences\n",
-    "df = calculate_average(df)\n",
-    "df = discard_ambiguous_compound_measurements(df)\n",
-    "print('#cpds after merging multi measurements: ', len(df.iloc[:,0]))\n",
-    "\n",
-    "# standardize SMILES, merge duplicates and retain higher active one\n",
-    "smiles_column = 0\n",
-    "df = generateStandarizedSmiles(df, smiles_column)\n",
-    "df = df[df['SMILES'] != 'none']\n",
-    "df = merge_duplicate_smiles(df)\n",
-    "print('#cpds after merging duplicate SMILES: ', len(df.iloc[:,0]))\n",
-    "\n",
-    "# Remove cpds with > 70 HA\n",
-    "smiles_column = 0\n",
-    "df = removeHeavyMols(df, smiles_column)\n",
-    "print('#cpds < 70 HA: ', len(df.iloc[:,0]))\n",
-    "\n",
-    "# Rename columns for subsequent NAA\n",
-    "df = df.rename(columns=({'COMPOUND_NAME':'ID'}))\n",
-    "df = df[['ID', 'SMILES', 'VALUE', 'MEASUREMENT']]\n",
-    "\n",
-    "# safe file\n",
-    "df.to_csv(my_path+my_name+'_curated.csv', index = False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# <font color='crimson'>  STEP II"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# NAA\n",
-    "\n",
-    "- Code available from gitHub by Christian Kramer: https://github.com/KramerChristian/NonadditivityAnalysis\n",
-    "- Corresponoding publication: https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b00631"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In order to run NAA out of the jupyter notebook, the system variable for the NAA path has to be set.\n",
-    "\n",
-    "If this is not done automatically, do the following in your commandline (Linux):\n",
-    "> cd $CONDA_PREFIX\n",
-    "\n",
-    "> mkdir -p ./etc/conda/activate.d\n",
-    "\n",
-    "> mkdir -p ./etc/conda/deactivate.d\n",
-    "\n",
-    "> touch ./etc/conda/activate.d/env_vars.sh\n",
-    "\n",
-    "> touch ./etc/conda/deactivate.d/env_vars.sh\n",
-    "\n",
-    "- change activate.d/env_vars.sh\n",
-    "> export NNA=/path/to/nna/\n",
-    "- change deactivate.d/env_vars.sh\n",
-    "> unset NNA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def init_naa():\n",
-    "    # save system variable in a local python variable, otherwise the !python call doesn't work\n",
-    "    naapath = !echo $NAA\n",
-    "    naapath = naapath[0]\n",
-    "    \n",
-    "    return naapath"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def naa(infile, outfile, myprops, myunit, mydelim='comma', myseries='', naapath=init_naa()):\n",
-    "    print (\"\\nanalyzing: \", infile)\n",
-    "\n",
-    "    if (myseries != '') :\n",
-    "        print ('\\n\\n', naapath+'Nonadditivity_Analysis.py -in ', infile ,' -delimiter ', mydelim , ' -series_column ', myseries, ' -props ', myprops ,' -units ', myunit ,' -out ', outfile, '\\n\\n')\n",
-    "        !python {naapath}/Nonadditivity_Analysis.py -in {infile} -delimiter {mydelim} -series_column {myseries} -props {myprops} -units {myunit} -out {outfile}\n",
-    "    else:    \n",
-    "        print ('\\n\\n', naapath+'Nonadditivity_Analysis.py -in ', infile ,' -delimiter ', mydelim ,' -props ', myprops ,' -units ', myunit ,' -out ', outfile, '\\n\\n')\n",
-    "        !python {naapath}/Nonadditivity_Analysis.py -in {infile} -delimiter {mydelim} -props {myprops} -units {myunit} -out {outfile}\n",
-    "    \n",
-    "    print (\"Done analysing.\\n\")\n",
-    "    return outfile"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Run NAA on cleanup data set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                       \r"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "analyzing:  ./naa/ChEMBL_1614027/ChEMBL_1614027_curated.csv\n",
-      "Identifier Column found: ID\n",
-      "Smiles column found: SMILES\n",
-      "Activity column #1: VALUE\n",
-      "Generating MMP Fragments for ./naa/ChEMBL_1614027/ChEMBL_1614027_curated.csv\n",
-      "Indexing MMP Fragments for ./naa/ChEMBL_1614027/ChEMBL_1614027_curated.csv\n",
-      "WARNING: Neither ujson nor cjson installed. Falling back to Python's slower built-in json decoder.\n",
-      "Analyzing neighborhoods                \n",
+      "Analyzing neighborhoods\n",
       "Assembling circles\n",
       "Writing Output.\n",
       "Estimated Experimental Uncertainty\n",
-      "for property:  VALUE\n",
+      "for property:  VALUE\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Output: 100%|██████████████████████████████████████████████████████████| 4086/4086 [00:00<00:00, 53079.47it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "based on  4086  cycles.\n",
       "0.36  from normal SD\n",
-      "0.30  from MAD\n",
+      "0.30  from MAD\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "0.30  from Median of Medians\n",
-      "\n",
-      "Done analysing.\n",
       "\n"
      ]
     },
     {
      "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>Compound1</th>\n",
+       "      <th>Compound2</th>\n",
+       "      <th>Compound3</th>\n",
+       "      <th>Compound4</th>\n",
+       "      <th>SMILES1</th>\n",
+       "      <th>SMILES2</th>\n",
+       "      <th>SMILES3</th>\n",
+       "      <th>SMILES4</th>\n",
+       "      <th>Series</th>\n",
+       "      <th>Transformation1</th>\n",
+       "      <th>Transformation2</th>\n",
+       "      <th>Property</th>\n",
+       "      <th>Prop_Cpd1</th>\n",
+       "      <th>Prop_Cpd2</th>\n",
+       "      <th>Prop_Cpd3</th>\n",
+       "      <th>Prop_Cpd4</th>\n",
+       "      <th>Nonadditivity</th>\n",
+       "      <th>Circle_ID</th>\n",
+       "      <th>Theo_Quantile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CHEMBL1531070</td>\n",
+       "      <td>CHEMBL1442087</td>\n",
+       "      <td>CHEMBL1555369</td>\n",
+       "      <td>CHEMBL1330718</td>\n",
+       "      <td>CCC/C=C(\\CCC)C(NS(=O)(=O)c1ccc(C(F)(F)F)cc1)c1...</td>\n",
+       "      <td>CCC/C=C(\\CCC)C(NS(=O)(=O)c1ccc(C(F)(F)F)cc1)c1...</td>\n",
+       "      <td>CCC/C=C(\\CCC)C(NS(=O)(=O)c1ccc(Cl)cc1)c1ccc(C(...</td>\n",
+       "      <td>CCC/C=C(\\CCC)C(NS(=O)(=O)c1ccc(Cl)cc1)c1ccccc1</td>\n",
+       "      <td></td>\n",
+       "      <td>[*:1][H]&gt;&gt;[*:1]C(=O)OC</td>\n",
+       "      <td>[*:1]C(F)(F)F&gt;&gt;[*:1]Cl</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>-0.5</td>\n",
+       "      <td>CHEMBL1531070_CHEMBL1442087_CHEMBL1555369_CHEM...</td>\n",
+       "      <td>-0.872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CHEMBL1566556</td>\n",
+       "      <td>CHEMBL1398066</td>\n",
+       "      <td>CHEMBL1486399</td>\n",
+       "      <td>CHEMBL1396358</td>\n",
+       "      <td>CCCC/C=C/C(NC(=O)c1ccccc1)c1ccccc1</td>\n",
+       "      <td>CCCC[C@@H]1C[C@H]1C(NC(=O)c1ccccc1)c1ccccc1</td>\n",
+       "      <td>CCCC[C@@H]1C[C@H]1C(NC(=O)c1ccco1)c1ccccc1</td>\n",
+       "      <td>CCCC/C=C/C(NC(=O)c1ccco1)c1ccccc1</td>\n",
+       "      <td></td>\n",
+       "      <td>[*:1]/C=C/CCCC&gt;&gt;[*:1][C@@H]1C[C@H]1CCCC</td>\n",
+       "      <td>[*:1]c1ccccc1&gt;&gt;[*:1]c1ccco1</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>CHEMBL1566556_CHEMBL1398066_CHEMBL1486399_CHEM...</td>\n",
+       "      <td>-0.160</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CHEMBL1592533</td>\n",
+       "      <td>CHEMBL1359291</td>\n",
+       "      <td>CHEMBL1437906</td>\n",
+       "      <td>CHEMBL1403280</td>\n",
+       "      <td>COc1ccc(C(=O)N2CCC3(CC2)CCN(c2ccccn2)CC3)cc1</td>\n",
+       "      <td>Cn1cccc1C(=O)N1CCC2(CC1)CCN(c1ccccn1)CC2</td>\n",
+       "      <td>Cn1cccc1C(=O)N1CCC2(CCN(Cc3ccncc3)CC2)CC1</td>\n",
+       "      <td>COc1ccc(C(=O)N2CCC3(CCN(Cc4ccncc4)CC3)CC2)cc1</td>\n",
+       "      <td></td>\n",
+       "      <td>[*:1]c1ccc(OC)cc1&gt;&gt;[*:1]c1cccn1C</td>\n",
+       "      <td>[*:1]c1ccccn1&gt;&gt;[*:1]Cc1ccncc1</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>CHEMBL1592533_CHEMBL1359291_CHEMBL1437906_CHEM...</td>\n",
+       "      <td>-0.327</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CHEMBL1592533</td>\n",
+       "      <td>CHEMBL1315700</td>\n",
+       "      <td>CHEMBL1564545</td>\n",
+       "      <td>CHEMBL1403280</td>\n",
+       "      <td>COc1ccc(C(=O)N2CCC3(CC2)CCN(c2ccccn2)CC3)cc1</td>\n",
+       "      <td>O=C(c1ccncc1)N1CCC2(CC1)CCN(c1ccccn1)CC2</td>\n",
+       "      <td>O=C(c1ccncc1)N1CCC2(CCN(Cc3ccncc3)CC2)CC1</td>\n",
+       "      <td>COc1ccc(C(=O)N2CCC3(CCN(Cc4ccncc4)CC3)CC2)cc1</td>\n",
+       "      <td></td>\n",
+       "      <td>[*:1]c1ccc(OC)cc1&gt;&gt;[*:1]c1ccncc1</td>\n",
+       "      <td>[*:1]c1ccccn1&gt;&gt;[*:1]Cc1ccncc1</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>CHEMBL1592533_CHEMBL1315700_CHEMBL1564545_CHEM...</td>\n",
+       "      <td>-1.530</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CHEMBL1437906</td>\n",
+       "      <td>CHEMBL1359291</td>\n",
+       "      <td>CHEMBL1315700</td>\n",
+       "      <td>CHEMBL1564545</td>\n",
+       "      <td>Cn1cccc1C(=O)N1CCC2(CCN(Cc3ccncc3)CC2)CC1</td>\n",
+       "      <td>Cn1cccc1C(=O)N1CCC2(CC1)CCN(c1ccccn1)CC2</td>\n",
+       "      <td>O=C(c1ccncc1)N1CCC2(CC1)CCN(c1ccccn1)CC2</td>\n",
+       "      <td>O=C(c1ccncc1)N1CCC2(CCN(Cc3ccncc3)CC2)CC1</td>\n",
+       "      <td></td>\n",
+       "      <td>[*:1]Cc1ccncc1&gt;&gt;[*:1]c1ccccn1</td>\n",
+       "      <td>[*:1]c1cccn1C&gt;&gt;[*:1]c1ccncc1</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>CHEMBL1437906_CHEMBL1359291_CHEMBL1315700_CHEM...</td>\n",
+       "      <td>1.330</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4081</th>\n",
+       "      <td>CHEMBL1515287</td>\n",
+       "      <td>CHEMBL1592760</td>\n",
+       "      <td>CHEMBL1494529</td>\n",
+       "      <td>CHEMBL1365979</td>\n",
+       "      <td>O=c1c(CCc2ccccc2)nc2cncnc2n1-c1ccccc1</td>\n",
+       "      <td>O=c1c(CCc2ccccc2)nc2cncnc2n1C1CC1</td>\n",
+       "      <td>Cc1nc2cncnc2n(C2CC2)c1=O</td>\n",
+       "      <td>Cc1nc2cncnc2n(-c2ccccc2)c1=O</td>\n",
+       "      <td></td>\n",
+       "      <td>[*:1]c1ccccc1&gt;&gt;[*:1]C1CC1</td>\n",
+       "      <td>[*:1]CCc1ccccc1&gt;&gt;[*:1]C</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>CHEMBL1515287_CHEMBL1592760_CHEMBL1494529_CHEM...</td>\n",
+       "      <td>1.180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4082</th>\n",
+       "      <td>CHEMBL1494529</td>\n",
+       "      <td>CHEMBL1365979</td>\n",
+       "      <td>CHEMBL1490139</td>\n",
+       "      <td>CHEMBL1358588</td>\n",
+       "      <td>Cc1nc2cncnc2n(C2CC2)c1=O</td>\n",
+       "      <td>Cc1nc2cncnc2n(-c2ccccc2)c1=O</td>\n",
+       "      <td>O=c1c(-c2cccs2)nc2cncnc2n1-c1ccccc1</td>\n",
+       "      <td>O=c1c(-c2cccs2)nc2cncnc2n1C1CC1</td>\n",
+       "      <td></td>\n",
+       "      <td>[*:1]C1CC1&gt;&gt;[*:1]c1ccccc1</td>\n",
+       "      <td>[*:1]C&gt;&gt;[*:1]c1cccs1</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>CHEMBL1494529_CHEMBL1365979_CHEMBL1490139_CHEM...</td>\n",
+       "      <td>1.420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4083</th>\n",
+       "      <td>CHEMBL1490139</td>\n",
+       "      <td>CHEMBL1358588</td>\n",
+       "      <td>CHEMBL1592760</td>\n",
+       "      <td>CHEMBL1515287</td>\n",
+       "      <td>O=c1c(-c2cccs2)nc2cncnc2n1-c1ccccc1</td>\n",
+       "      <td>O=c1c(-c2cccs2)nc2cncnc2n1C1CC1</td>\n",
+       "      <td>O=c1c(CCc2ccccc2)nc2cncnc2n1C1CC1</td>\n",
+       "      <td>O=c1c(CCc2ccccc2)nc2cncnc2n1-c1ccccc1</td>\n",
+       "      <td></td>\n",
+       "      <td>[*:1]c1ccccc1&gt;&gt;[*:1]C1CC1</td>\n",
+       "      <td>[*:1]c1cccs1&gt;&gt;[*:1]CCc1ccccc1</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>CHEMBL1490139_CHEMBL1358588_CHEMBL1592760_CHEM...</td>\n",
+       "      <td>0.357</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4084</th>\n",
+       "      <td>CHEMBL1326100</td>\n",
+       "      <td>CHEMBL1512693</td>\n",
+       "      <td>CHEMBL1358588</td>\n",
+       "      <td>CHEMBL1494529</td>\n",
+       "      <td>Cc1nc2cnc(N(C)C)nc2n(C2CC2)c1=O</td>\n",
+       "      <td>CN(C)c1ncc2nc(-c3cccs3)c(=O)n(C3CC3)c2n1</td>\n",
+       "      <td>O=c1c(-c2cccs2)nc2cncnc2n1C1CC1</td>\n",
+       "      <td>Cc1nc2cncnc2n(C2CC2)c1=O</td>\n",
+       "      <td></td>\n",
+       "      <td>[*:1]C&gt;&gt;[*:1]c1cccs1</td>\n",
+       "      <td>[*:1]N(C)C&gt;&gt;[*:1][H]</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>CHEMBL1326100_CHEMBL1512693_CHEMBL1358588_CHEM...</td>\n",
+       "      <td>-1.490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4085</th>\n",
+       "      <td>CHEMBL1316562</td>\n",
+       "      <td>CHEMBL1355909</td>\n",
+       "      <td>CHEMBL1490139</td>\n",
+       "      <td>CHEMBL1358588</td>\n",
+       "      <td>O=c1c(-c2cccs2)nc2cnc(N3CCNCC3)nc2n1C1CC1</td>\n",
+       "      <td>O=c1c(-c2cccs2)nc2cnc(N3CCNCC3)nc2n1-c1ccccc1</td>\n",
+       "      <td>O=c1c(-c2cccs2)nc2cncnc2n1-c1ccccc1</td>\n",
+       "      <td>O=c1c(-c2cccs2)nc2cncnc2n1C1CC1</td>\n",
+       "      <td></td>\n",
+       "      <td>[*:1]C1CC1&gt;&gt;[*:1]c1ccccc1</td>\n",
+       "      <td>[*:1]N1CCNCC1&gt;&gt;[*:1][H]</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>CHEMBL1316562_CHEMBL1355909_CHEMBL1490139_CHEM...</td>\n",
+       "      <td>2.590</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4086 rows × 19 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "'./naa/ChEMBL_1614027/ChEMBL_1614027_NAA_output.csv'"
+       "          Compound1      Compound2      Compound3      Compound4  \\\n",
+       "0     CHEMBL1531070  CHEMBL1442087  CHEMBL1555369  CHEMBL1330718   \n",
+       "1     CHEMBL1566556  CHEMBL1398066  CHEMBL1486399  CHEMBL1396358   \n",
+       "2     CHEMBL1592533  CHEMBL1359291  CHEMBL1437906  CHEMBL1403280   \n",
+       "3     CHEMBL1592533  CHEMBL1315700  CHEMBL1564545  CHEMBL1403280   \n",
+       "4     CHEMBL1437906  CHEMBL1359291  CHEMBL1315700  CHEMBL1564545   \n",
+       "...             ...            ...            ...            ...   \n",
+       "4081  CHEMBL1515287  CHEMBL1592760  CHEMBL1494529  CHEMBL1365979   \n",
+       "4082  CHEMBL1494529  CHEMBL1365979  CHEMBL1490139  CHEMBL1358588   \n",
+       "4083  CHEMBL1490139  CHEMBL1358588  CHEMBL1592760  CHEMBL1515287   \n",
+       "4084  CHEMBL1326100  CHEMBL1512693  CHEMBL1358588  CHEMBL1494529   \n",
+       "4085  CHEMBL1316562  CHEMBL1355909  CHEMBL1490139  CHEMBL1358588   \n",
+       "\n",
+       "                                                SMILES1  \\\n",
+       "0     CCC/C=C(\\CCC)C(NS(=O)(=O)c1ccc(C(F)(F)F)cc1)c1...   \n",
+       "1                    CCCC/C=C/C(NC(=O)c1ccccc1)c1ccccc1   \n",
+       "2          COc1ccc(C(=O)N2CCC3(CC2)CCN(c2ccccn2)CC3)cc1   \n",
+       "3          COc1ccc(C(=O)N2CCC3(CC2)CCN(c2ccccn2)CC3)cc1   \n",
+       "4             Cn1cccc1C(=O)N1CCC2(CCN(Cc3ccncc3)CC2)CC1   \n",
+       "...                                                 ...   \n",
+       "4081              O=c1c(CCc2ccccc2)nc2cncnc2n1-c1ccccc1   \n",
+       "4082                           Cc1nc2cncnc2n(C2CC2)c1=O   \n",
+       "4083                O=c1c(-c2cccs2)nc2cncnc2n1-c1ccccc1   \n",
+       "4084                    Cc1nc2cnc(N(C)C)nc2n(C2CC2)c1=O   \n",
+       "4085          O=c1c(-c2cccs2)nc2cnc(N3CCNCC3)nc2n1C1CC1   \n",
+       "\n",
+       "                                                SMILES2  \\\n",
+       "0     CCC/C=C(\\CCC)C(NS(=O)(=O)c1ccc(C(F)(F)F)cc1)c1...   \n",
+       "1           CCCC[C@@H]1C[C@H]1C(NC(=O)c1ccccc1)c1ccccc1   \n",
+       "2              Cn1cccc1C(=O)N1CCC2(CC1)CCN(c1ccccn1)CC2   \n",
+       "3              O=C(c1ccncc1)N1CCC2(CC1)CCN(c1ccccn1)CC2   \n",
+       "4              Cn1cccc1C(=O)N1CCC2(CC1)CCN(c1ccccn1)CC2   \n",
+       "...                                                 ...   \n",
+       "4081                  O=c1c(CCc2ccccc2)nc2cncnc2n1C1CC1   \n",
+       "4082                       Cc1nc2cncnc2n(-c2ccccc2)c1=O   \n",
+       "4083                    O=c1c(-c2cccs2)nc2cncnc2n1C1CC1   \n",
+       "4084           CN(C)c1ncc2nc(-c3cccs3)c(=O)n(C3CC3)c2n1   \n",
+       "4085      O=c1c(-c2cccs2)nc2cnc(N3CCNCC3)nc2n1-c1ccccc1   \n",
+       "\n",
+       "                                                SMILES3  \\\n",
+       "0     CCC/C=C(\\CCC)C(NS(=O)(=O)c1ccc(Cl)cc1)c1ccc(C(...   \n",
+       "1            CCCC[C@@H]1C[C@H]1C(NC(=O)c1ccco1)c1ccccc1   \n",
+       "2             Cn1cccc1C(=O)N1CCC2(CCN(Cc3ccncc3)CC2)CC1   \n",
+       "3             O=C(c1ccncc1)N1CCC2(CCN(Cc3ccncc3)CC2)CC1   \n",
+       "4              O=C(c1ccncc1)N1CCC2(CC1)CCN(c1ccccn1)CC2   \n",
+       "...                                                 ...   \n",
+       "4081                           Cc1nc2cncnc2n(C2CC2)c1=O   \n",
+       "4082                O=c1c(-c2cccs2)nc2cncnc2n1-c1ccccc1   \n",
+       "4083                  O=c1c(CCc2ccccc2)nc2cncnc2n1C1CC1   \n",
+       "4084                    O=c1c(-c2cccs2)nc2cncnc2n1C1CC1   \n",
+       "4085                O=c1c(-c2cccs2)nc2cncnc2n1-c1ccccc1   \n",
+       "\n",
+       "                                             SMILES4 Series  \\\n",
+       "0     CCC/C=C(\\CCC)C(NS(=O)(=O)c1ccc(Cl)cc1)c1ccccc1          \n",
+       "1                  CCCC/C=C/C(NC(=O)c1ccco1)c1ccccc1          \n",
+       "2      COc1ccc(C(=O)N2CCC3(CCN(Cc4ccncc4)CC3)CC2)cc1          \n",
+       "3      COc1ccc(C(=O)N2CCC3(CCN(Cc4ccncc4)CC3)CC2)cc1          \n",
+       "4          O=C(c1ccncc1)N1CCC2(CCN(Cc3ccncc3)CC2)CC1          \n",
+       "...                                              ...    ...   \n",
+       "4081                    Cc1nc2cncnc2n(-c2ccccc2)c1=O          \n",
+       "4082                 O=c1c(-c2cccs2)nc2cncnc2n1C1CC1          \n",
+       "4083           O=c1c(CCc2ccccc2)nc2cncnc2n1-c1ccccc1          \n",
+       "4084                        Cc1nc2cncnc2n(C2CC2)c1=O          \n",
+       "4085                 O=c1c(-c2cccs2)nc2cncnc2n1C1CC1          \n",
+       "\n",
+       "                              Transformation1                Transformation2  \\\n",
+       "0                      [*:1][H]>>[*:1]C(=O)OC         [*:1]C(F)(F)F>>[*:1]Cl   \n",
+       "1     [*:1]/C=C/CCCC>>[*:1][C@@H]1C[C@H]1CCCC    [*:1]c1ccccc1>>[*:1]c1ccco1   \n",
+       "2            [*:1]c1ccc(OC)cc1>>[*:1]c1cccn1C  [*:1]c1ccccn1>>[*:1]Cc1ccncc1   \n",
+       "3            [*:1]c1ccc(OC)cc1>>[*:1]c1ccncc1  [*:1]c1ccccn1>>[*:1]Cc1ccncc1   \n",
+       "4               [*:1]Cc1ccncc1>>[*:1]c1ccccn1   [*:1]c1cccn1C>>[*:1]c1ccncc1   \n",
+       "...                                       ...                            ...   \n",
+       "4081                [*:1]c1ccccc1>>[*:1]C1CC1        [*:1]CCc1ccccc1>>[*:1]C   \n",
+       "4082                [*:1]C1CC1>>[*:1]c1ccccc1           [*:1]C>>[*:1]c1cccs1   \n",
+       "4083                [*:1]c1ccccc1>>[*:1]C1CC1  [*:1]c1cccs1>>[*:1]CCc1ccccc1   \n",
+       "4084                     [*:1]C>>[*:1]c1cccs1           [*:1]N(C)C>>[*:1][H]   \n",
+       "4085                [*:1]C1CC1>>[*:1]c1ccccc1        [*:1]N1CCNCC1>>[*:1][H]   \n",
+       "\n",
+       "     Property  Prop_Cpd1  Prop_Cpd2  Prop_Cpd3  Prop_Cpd4  Nonadditivity  \\\n",
+       "0       VALUE        5.1        5.5        5.5        5.6           -0.5   \n",
+       "1       VALUE        5.0        5.1        4.8        4.8           -0.1   \n",
+       "2       VALUE        4.4        4.5        5.0        5.1           -0.2   \n",
+       "3       VALUE        4.4        5.0        4.7        5.1           -1.0   \n",
+       "4       VALUE        5.0        4.5        5.0        4.7            0.8   \n",
+       "...       ...        ...        ...        ...        ...            ...   \n",
+       "4081    VALUE        4.6        4.6        5.1        4.4            0.7   \n",
+       "4082    VALUE        5.1        4.4        5.0        4.8            0.9   \n",
+       "4083    VALUE        5.0        4.8        4.6        4.6            0.2   \n",
+       "4084    VALUE        4.8        5.5        4.8        5.1           -1.0   \n",
+       "4085    VALUE        6.9        4.7        5.0        4.8            2.4   \n",
+       "\n",
+       "                                              Circle_ID  Theo_Quantile  \n",
+       "0     CHEMBL1531070_CHEMBL1442087_CHEMBL1555369_CHEM...         -0.872  \n",
+       "1     CHEMBL1566556_CHEMBL1398066_CHEMBL1486399_CHEM...         -0.160  \n",
+       "2     CHEMBL1592533_CHEMBL1359291_CHEMBL1437906_CHEM...         -0.327  \n",
+       "3     CHEMBL1592533_CHEMBL1315700_CHEMBL1564545_CHEM...         -1.530  \n",
+       "4     CHEMBL1437906_CHEMBL1359291_CHEMBL1315700_CHEM...          1.330  \n",
+       "...                                                 ...            ...  \n",
+       "4081  CHEMBL1515287_CHEMBL1592760_CHEMBL1494529_CHEM...          1.180  \n",
+       "4082  CHEMBL1494529_CHEMBL1365979_CHEMBL1490139_CHEM...          1.420  \n",
+       "4083  CHEMBL1490139_CHEMBL1358588_CHEMBL1592760_CHEM...          0.357  \n",
+       "4084  CHEMBL1326100_CHEMBL1512693_CHEMBL1358588_CHEM...         -1.490  \n",
+       "4085  CHEMBL1316562_CHEMBL1355909_CHEMBL1490139_CHEM...          2.590  \n",
+       "\n",
+       "[4086 rows x 19 columns]"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "infile = my_path+my_name+'_curated.csv'\n",
-    "outfile = my_path+my_name+'_NAA_output.csv'\n",
-    "\n",
-    "naa(infile, outfile, 'VALUE', 'noconv', 'comma')"
+    "MAIN, PC, _ = run_nonadd_calculation_helper(\n",
+    "    infile=infile,\n",
+    "    props=['VALUE'],\n",
+    "    units=['noconv'],\n",
+    ")\n",
+    "MAIN"
    ]
   },
   {
@@ -580,16 +804,6 @@
     "### Generate plots for analysing NAA output"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "curated = my_path+my_name+'_curated.csv'\n",
-    "curated = pd.read_csv(curated, sep=',')"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -601,185 +815,2785 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
     "add_thrs = 0\n",
     "exp_noise = 0.5\n",
-    "significant_thrs = 2*exp_noise\n",
-    "strong_thrs = 2*significant_thrs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "MAIN = my_path+my_name+'_NAA_output.csv'\n",
-    "MAIN = pd.read_csv(MAIN, sep='\\t')\n",
+    "significant_thrs = 2 * exp_noise\n",
+    "strong_thrs = 2 * significant_thrs\n",
     "\n",
     "MAIN['Nonadditivity_abs'] = MAIN['Nonadditivity'].abs()\n",
-    "MAIN_log0 = MAIN[MAIN['Nonadditivity_abs'] > add_thrs]\n",
-    "MAIN_log1 = MAIN[MAIN['Nonadditivity_abs'] > significant_thrs]\n",
-    "MAIN_log2 = MAIN_log1[MAIN_log1['Nonadditivity_abs'] > strong_thrs]"
+    "MAIN_log0 = MAIN[MAIN['Nonadditivity'].abs() > add_thrs]\n",
+    "MAIN_log1 = MAIN[MAIN['Nonadditivity'].abs() > significant_thrs]\n",
+    "MAIN_log2 = MAIN[MAIN['Nonadditivity'].abs() > strong_thrs]"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 21,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "PC = my_path+my_name+'_NAA_output_perCompound.txt'\n",
-    "PC = pd.read_csv(PC, sep='\\t')\n",
-    "\n",
-    "PC['Nonadd_abs'] = PC['Nonadd_pC'].abs()\n",
-    "PC['CI'] = (2*2*exp_noise/(np.sqrt(PC['nOccurence'])))\n",
-    "PC['CI_2'] = (2*2*exp_noise/(np.sqrt(PC['nOccurence'])))*3\n",
-    "PC = PC.sort_values(by=['CI'], ascending=False)\n",
-    "\n",
-    "PC_log0 = PC[PC['Nonadd_abs'] > add_thrs]\n",
-    "PC_log1 = PC[PC['Nonadd_abs'] > significant_thrs]\n",
-    "PC_log2 = PC_log1[PC_log1['Nonadd_abs'] > strong_thrs]"
+    "### Check for normality "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2876 compounds\n",
-      "4086 cycles\n",
-      "486 cycles with significant NA score ( 11.9 % )\n",
-      "76 unique compounds show significant NA shift ( 2.6 % )\n",
-      "13 unique compounds show strong NA ( 0.5 % )\n"
-     ]
+     "data": {
+      "text/plain": [
+       "DescribeResult(nobs=4086, minmax=(-4.2, 3.4), mean=-0.006044999812457169, variance=0.528509343779737, skewness=-0.24855197856455374, kurtosis=3.212947149974866)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(len(curated.iloc[:,0]), 'compounds')\n",
-    "print(len(MAIN.iloc[:,0]), 'cycles')\n",
-    "print(len(MAIN_log1.iloc[:,0]), 'cycles with significant NA score', '(',round(len(MAIN_log1.iloc[:,0])/len(MAIN.iloc[:,0])*100,1), '% )')\n",
-    "print(len(PC_log1['Compound_ID'].value_counts()), 'unique compounds show significant NA shift', '(',round(len(PC_log1['Compound_ID'])/len(curated.iloc[:,0])*100,1), '% )')\n",
-    "print(len(PC_log2['Compound_ID'].value_counts()), 'unique compounds show strong NA', '(',round(len(PC_log2['Compound_ID'])/len(curated.iloc[:,0])*100,1), '% )')"
+    "MAIN_array = MAIN['Nonadditivity'].values\n",
+    "stats.describe(MAIN_array, axis=0)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Check for normality "
+    "### Nonadditivity Distribution\n",
+    "\n",
+    "<font color='crimson'>normal distribution parameters depend on significant treshold"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/cthoyt/.virtualenvs/cheminf/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n",
+      "/Users/cthoyt/.virtualenvs/cheminf/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
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       "text/plain": [
-       "DescribeResult(nobs=4086, minmax=(-4.2, 3.7), mean=-0.008981839302667646, variance=0.5284652015619267, skewness=-0.18666235101379203, kurtosis=3.212527050178692)"
+       "<Figure size 432x252 with 1 Axes>"
       ]
      },
-     "execution_count": 23,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "MAIN_array = MAIN['Nonadditivity'].values\n",
-    "stats.describe(MAIN_array, axis=0)"
+    "fig, ax = NA_distribution(MAIN['Nonadditivity'], significant_thrs)\n",
+    "ax.set_title(f'{assay_chembl_id}\\n{ax.get_title()}')\n",
+    "# fig.savefig('nonadditivity_distribution.png', dpi=300)\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Statistics=335.764, p=0.000\n",
-      "Sample does not look Gaussian (reject H0)\n"
-     ]
+     "data": {
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+       "      <td>CHEMBL1370296</td>\n",
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+       "      <td>5.7</td>\n",
+       "      <td>0.310000</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.310000</td>\n",
+       "      <td>0.244339</td>\n",
+       "      <td>0.733017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>464</th>\n",
+       "      <td>CHEMBL1433704</td>\n",
+       "      <td>COCCn1c(=O)c(-c2ccccc2)nc2cnc(Oc3ccccc3)nc21</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>-0.210000</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>0.210000</td>\n",
+       "      <td>0.244339</td>\n",
+       "      <td>0.733017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>CHEMBL1481510</td>\n",
+       "      <td>O=c1c(-c2ccccc2)nc2cnc(N3CCNCC3)nc2n1C1CC1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>0.970000</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>0.970000</td>\n",
+       "      <td>0.244339</td>\n",
+       "      <td>0.733017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>470</th>\n",
+       "      <td>CHEMBL1472732</td>\n",
+       "      <td>COCCn1c(=O)c(-c2ccccc2)nc2cnc(N3CCNCC3)nc21</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>0.002900</td>\n",
+       "      <td>69</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.002900</td>\n",
+       "      <td>0.240772</td>\n",
+       "      <td>0.722315</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>545</th>\n",
+       "      <td>CHEMBL1396809</td>\n",
+       "      <td>COc1cccc(Cn2c(=O)c(CCc3ccccc3)nc3cncnc32)c1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>-0.047000</td>\n",
+       "      <td>73</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.047000</td>\n",
+       "      <td>0.234082</td>\n",
+       "      <td>0.702247</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>941 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Compound_ID                                             SMILES  Series  \\\n",
+       "0    CHEMBL1531070  CCC/C=C(\\CCC)C(NS(=O)(=O)c1ccc(C(F)(F)F)cc1)c1...     NaN   \n",
+       "297  CHEMBL1568735            COc1cccc(-c2nc(=NCc3ccccc3OC)cc[nH]2)c1     NaN   \n",
+       "281  CHEMBL1552519           c1ccc(CN=c2[nH]cnc3ccc(-c4ccoc4)cc23)cc1     NaN   \n",
+       "282  CHEMBL1370296          Cc1cccc(CN=c2[nH]cnc3ccc(-c4ccoc4)cc23)c1     NaN   \n",
+       "283   CHEMBL491771  Cc1cccc(CN=c2[nH]cnc3ccc(-c4cccc(NS(C)(=O)=O)c...     NaN   \n",
+       "..             ...                                                ...     ...   \n",
+       "531  CHEMBL1490528         COCCn1c(=O)c(-c2cccs2)nc2cnc(N3CCNCC3)nc21     NaN   \n",
+       "464  CHEMBL1433704       COCCn1c(=O)c(-c2ccccc2)nc2cnc(Oc3ccccc3)nc21     NaN   \n",
+       "428  CHEMBL1481510         O=c1c(-c2ccccc2)nc2cnc(N3CCNCC3)nc2n1C1CC1     NaN   \n",
+       "470  CHEMBL1472732        COCCn1c(=O)c(-c2ccccc2)nc2cnc(N3CCNCC3)nc21     NaN   \n",
+       "545  CHEMBL1396809        COc1cccc(Cn2c(=O)c(CCc3ccccc3)nc3cncnc32)c1     NaN   \n",
+       "\n",
+       "    Property  Operator  Measured  Nonadd_pC  nOccurence  Nonadd_SD  \\\n",
+       "0      VALUE       NaN       5.1  -0.500000           1       0.00   \n",
+       "297    VALUE       NaN       5.9  -1.800000           1       0.00   \n",
+       "281    VALUE       NaN       5.8   0.000047           1       0.00   \n",
+       "282    VALUE       NaN       6.0  -0.000047           1       0.00   \n",
+       "283    VALUE       NaN       6.3   1.700000           1       0.00   \n",
+       "..       ...       ...       ...        ...         ...        ...   \n",
+       "531    VALUE       NaN       5.7   0.310000          67       0.76   \n",
+       "464    VALUE       NaN       5.0  -0.210000          67       0.39   \n",
+       "428    VALUE       NaN       6.8   0.970000          67       0.84   \n",
+       "470    VALUE       NaN       5.5   0.002900          69       0.68   \n",
+       "545    VALUE       NaN       4.9  -0.047000          73       0.52   \n",
+       "\n",
+       "     Nonadd_abs        CI      CI_2  \n",
+       "0      0.500000  2.000000  6.000000  \n",
+       "297    1.800000  2.000000  6.000000  \n",
+       "281    0.000047  2.000000  6.000000  \n",
+       "282    0.000047  2.000000  6.000000  \n",
+       "283    1.700000  2.000000  6.000000  \n",
+       "..          ...       ...       ...  \n",
+       "531    0.310000  0.244339  0.733017  \n",
+       "464    0.210000  0.244339  0.733017  \n",
+       "428    0.970000  0.244339  0.733017  \n",
+       "470    0.002900  0.240772  0.722315  \n",
+       "545    0.047000  0.234082  0.702247  \n",
+       "\n",
+       "[941 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "stat, p = normaltest(MAIN_array)\n",
-    "print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
-    "\n",
-    "alpha = 0.05\n",
-    "if p > alpha:\n",
-    "    print('Sample looks Gaussian (fail to reject H0)')\n",
-    "else:\n",
-    "    print('Sample does not look Gaussian (reject H0)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Nonadditivity Distribution\n",
-    "\n",
-    "<font color='crimson'>normal distribution parameters depend on significant treshold"
+    "PC['Nonadd_abs'] = PC['Nonadd_pC'].abs()\n",
+    "PC['CI']   = (2*2*exp_noise/(np.sqrt(PC['nOccurence'])))\n",
+    "PC['CI_2'] = (2*2*exp_noise/(np.sqrt(PC['nOccurence']))) * 3\n",
+    "PC = PC.sort_values(by=['CI'], ascending=False)\n",
+    "PC"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def NA_distribution(x, hist=True, kde=True, ins=int(100/5), color='crimson', kde_kws={'shade': True, 'linewidth': 2}):\n",
-    "    sns.set_style('ticks')\n",
-    "    fig, ax = plt.subplots(1,1)\n",
-    "    fig.set_size_inches(10, 8)\n",
-    "    sns.distplot(x, hist=True, kde=True,\n",
-    "             bins=int(100/5), color='crimson',\n",
-    "             kde_kws={'shade': True, 'linewidth': 2})\n",
-    "    sns.distplot(normal_dist, hist=False, kde=True,\n",
-    "             color='grey',\n",
-    "             kde_kws={'shade': True, 'linewidth': 2})\n",
-    "\n",
-    "    legend = ['Real','Theoretical']\n",
-    "\n",
-    "    plt.legend(legend, prop={'size': 20}) #title = '')\n",
-    "    plt.title('',  size=30)\n",
-    "    plt.xlabel('Nonadditivity',  size=25)\n",
-    "    plt.ylabel('Density',  size=25)\n",
-    "    \n",
-    "    plt.show()"
+    "PC_log0 = PC[PC['Nonadd_pC'].abs() > add_thrs]\n",
+    "PC_log1 = PC[PC['Nonadd_pC'].abs() > significant_thrs]\n",
+    "PC_log2 = PC[PC['Nonadd_pC'].abs() > strong_thrs]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2876 compounds\n",
+      "4086 cycles\n",
+      "486 cycles with significant NA score ( 11.9 % )\n",
+      "76 unique compounds show significant NA shift ( 2.6 % )\n",
+      "13 unique compounds show strong NA ( 0.5 % )\n"
+     ]
     }
    ],
    "source": [
-    "n_of_cycles = len(MAIN.iloc[:,0])\n",
-    "normal_dist = np.random.normal(loc=0,scale=significant_thrs,size=n_of_cycles)\n",
-    "\n",
-    "NA_distribution(MAIN['Nonadditivity'])"
+    "print(len(df.iloc[:,0]), 'compounds')\n",
+    "print(len(MAIN.iloc[:,0]), 'cycles')\n",
+    "print(len(MAIN_log1.iloc[:,0]), 'cycles with significant NA score', '(',round(len(MAIN_log1.iloc[:,0])/len(MAIN.iloc[:,0])*100,1), '% )')\n",
+    "print(len(PC_log1['Compound_ID'].value_counts()), 'unique compounds show significant NA shift', '(',round(len(PC_log1['Compound_ID'])/len(df.iloc[:,0])*100,1), '% )')\n",
+    "print(len(PC_log2['Compound_ID'].value_counts()), 'unique compounds show strong NA', '(',round(len(PC_log2['Compound_ID'])/len(df.iloc[:,0])*100,1), '% )')"
    ]
   },
   {
@@ -791,39 +3605,528 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "395 compounds\n"
-     ]
+     "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>Compound_ID</th>\n",
+       "      <th>SMILES</th>\n",
+       "      <th>Series</th>\n",
+       "      <th>Property</th>\n",
+       "      <th>Operator</th>\n",
+       "      <th>Measured</th>\n",
+       "      <th>Nonadd_pC</th>\n",
+       "      <th>nOccurence</th>\n",
+       "      <th>Nonadd_SD</th>\n",
+       "      <th>Nonadd_abs</th>\n",
+       "      <th>CI</th>\n",
+       "      <th>CI_2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>862</th>\n",
+       "      <td>CHEMBL1396862</td>\n",
+       "      <td>O=S(=O)(c1ccccc1)N1CCC2(CCCN(Cc3ccncc3)C2)CC1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>863</th>\n",
+       "      <td>CHEMBL1473753</td>\n",
+       "      <td>O=S(=O)(c1ccccc1)N1CCC2(CCCN(c3ccncc3)C2)CC1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>-2.80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>864</th>\n",
+       "      <td>CHEMBL1553056</td>\n",
+       "      <td>CS(=O)(=O)N1CCC2(CCCN(c3ccncc3)C2)CC1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>865</th>\n",
+       "      <td>CHEMBL1435702</td>\n",
+       "      <td>CS(=O)(=O)N1CCC2(CCCN(Cc3ccncc3)C2)CC1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>-2.80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>612</th>\n",
+       "      <td>CHEMBL1316759</td>\n",
+       "      <td>COc1ncc2nc(C)c(=O)n(CCC#N)c2n1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.100000e-07</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>1.414214</td>\n",
+       "      <td>4.242641</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>532</th>\n",
+       "      <td>CHEMBL1554236</td>\n",
+       "      <td>COCCn1c(=O)c(-c2cc(F)cc(F)c2)nc2cnc(N3CCNCC3)nc21</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>0.66</td>\n",
+       "      <td>63</td>\n",
+       "      <td>7.800000e-01</td>\n",
+       "      <td>0.66</td>\n",
+       "      <td>0.251976</td>\n",
+       "      <td>0.755929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>CHEMBL1434801</td>\n",
+       "      <td>O=c1c(CCc2ccccc2)nc2cnc(N3CCNCC3)nc2n1C1CC1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>-0.50</td>\n",
+       "      <td>63</td>\n",
+       "      <td>9.900000e-01</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>0.251976</td>\n",
+       "      <td>0.755929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>559</th>\n",
+       "      <td>CHEMBL1405464</td>\n",
+       "      <td>COc1cccc(Cn2c(=O)c(-c3cccs3)nc3cncnc32)c1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>64</td>\n",
+       "      <td>7.600000e-01</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>531</th>\n",
+       "      <td>CHEMBL1490528</td>\n",
+       "      <td>COCCn1c(=O)c(-c2cccs2)nc2cnc(N3CCNCC3)nc21</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>0.31</td>\n",
+       "      <td>67</td>\n",
+       "      <td>7.600000e-01</td>\n",
+       "      <td>0.31</td>\n",
+       "      <td>0.244339</td>\n",
+       "      <td>0.733017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>CHEMBL1481510</td>\n",
+       "      <td>O=c1c(-c2ccccc2)nc2cnc(N3CCNCC3)nc2n1C1CC1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>VALUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>0.97</td>\n",
+       "      <td>67</td>\n",
+       "      <td>8.400000e-01</td>\n",
+       "      <td>0.97</td>\n",
+       "      <td>0.244339</td>\n",
+       "      <td>0.733017</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>168 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Compound_ID                                             SMILES  Series  \\\n",
+       "862  CHEMBL1396862      O=S(=O)(c1ccccc1)N1CCC2(CCCN(Cc3ccncc3)C2)CC1     NaN   \n",
+       "863  CHEMBL1473753       O=S(=O)(c1ccccc1)N1CCC2(CCCN(c3ccncc3)C2)CC1     NaN   \n",
+       "864  CHEMBL1553056              CS(=O)(=O)N1CCC2(CCCN(c3ccncc3)C2)CC1     NaN   \n",
+       "865  CHEMBL1435702             CS(=O)(=O)N1CCC2(CCCN(Cc3ccncc3)C2)CC1     NaN   \n",
+       "612  CHEMBL1316759                     COc1ncc2nc(C)c(=O)n(CCC#N)c2n1     NaN   \n",
+       "..             ...                                                ...     ...   \n",
+       "532  CHEMBL1554236  COCCn1c(=O)c(-c2cc(F)cc(F)c2)nc2cnc(N3CCNCC3)nc21     NaN   \n",
+       "447  CHEMBL1434801        O=c1c(CCc2ccccc2)nc2cnc(N3CCNCC3)nc2n1C1CC1     NaN   \n",
+       "559  CHEMBL1405464          COc1cccc(Cn2c(=O)c(-c3cccs3)nc3cncnc32)c1     NaN   \n",
+       "531  CHEMBL1490528         COCCn1c(=O)c(-c2cccs2)nc2cnc(N3CCNCC3)nc21     NaN   \n",
+       "428  CHEMBL1481510         O=c1c(-c2ccccc2)nc2cnc(N3CCNCC3)nc2n1C1CC1     NaN   \n",
+       "\n",
+       "    Property  Operator  Measured  Nonadd_pC  nOccurence     Nonadd_SD  \\\n",
+       "862    VALUE       NaN       7.1       2.80           1  0.000000e+00   \n",
+       "863    VALUE       NaN       5.4      -2.80           1  0.000000e+00   \n",
+       "864    VALUE       NaN       7.1       2.80           1  0.000000e+00   \n",
+       "865    VALUE       NaN       6.0      -2.80           1  0.000000e+00   \n",
+       "612    VALUE       NaN       6.5       2.80           2  3.100000e-07   \n",
+       "..       ...       ...       ...        ...         ...           ...   \n",
+       "532    VALUE       NaN       5.9       0.66          63  7.800000e-01   \n",
+       "447    VALUE       NaN       5.0      -0.50          63  9.900000e-01   \n",
+       "559    VALUE       NaN       5.6       0.36          64  7.600000e-01   \n",
+       "531    VALUE       NaN       5.7       0.31          67  7.600000e-01   \n",
+       "428    VALUE       NaN       6.8       0.97          67  8.400000e-01   \n",
+       "\n",
+       "     Nonadd_abs        CI      CI_2  \n",
+       "862        2.80  2.000000  6.000000  \n",
+       "863        2.80  2.000000  6.000000  \n",
+       "864        2.80  2.000000  6.000000  \n",
+       "865        2.80  2.000000  6.000000  \n",
+       "612        2.80  1.414214  4.242641  \n",
+       "..          ...       ...       ...  \n",
+       "532        0.66  0.251976  0.755929  \n",
+       "447        0.50  0.251976  0.755929  \n",
+       "559        0.36  0.250000  0.750000  \n",
+       "531        0.31  0.244339  0.733017  \n",
+       "428        0.97  0.244339  0.733017  \n",
+       "\n",
+       "[168 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "ambiguous_compounds = PC.loc[PC['Nonadd_abs'] > PC['CI']]\n",
-    "print(len(ambiguous_compounds.iloc[:,0]), 'compounds')\n",
-    "outliers = ambiguous_compounds[['SMILES']]\n",
-    "\n",
-    "#outliers.to_csv(my_path+'outliers.csv', index = False)"
+    "ambiguous_compounds = PC.loc[PC['Nonadd_pC'].abs() > PC['CI']]\n",
+    "ambiguous_compounds"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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+       "</svg>\n"
+      ],
       "text/plain": [
-       "<rdkit.Chem.rdchem.Mol at 0x7ff5c256cb20>"
+       "<rdkit.Chem.rdchem.Mol at 0x16bc54940>"
       ]
      },
+     "execution_count": 16,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -831,19 +4134,7172 @@
     "SMILES = list(ambiguous_compounds['SMILES'])\n",
     "strangest = SMILES[-2]\n",
     "\n",
-    "display(Chem.MolFromSmiles(strangest))"
+    "Chem.MolFromSmiles(strangest)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
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      },
      "metadata": {},
@@ -851,12 +11307,11 @@
     }
    ],
    "source": [
-    "fig = plt.figure(figsize = (10,8))\n",
-    "ax = fig.add_subplot(1,1,1)\n",
+    "fig, ax = plt.subplots()\n",
     "\n",
-    "ax.set_xlabel('# cycles', fontsize=20)\n",
-    "ax.set_ylabel('Shift pActivity', fontsize=20)\n",
-    "ax.set_title('Additivity Shift per Compound', fontsize=25)\n",
+    "ax.set_xlabel('# cycles')\n",
+    "ax.set_ylabel('Shift pActivity')\n",
+    "ax.set_title('Additivity Shift per Compound')\n",
     "\n",
     "x = PC['nOccurence'].values\n",
     "y = PC['Nonadd_pC'].values\n",
@@ -867,11 +11322,12 @@
     "y2 = ambiguous_compounds['Nonadd_pC'].values\n",
     "e2 = ambiguous_compounds['Nonadd_SD'].values\n",
     "\n",
-    "ax.errorbar(x, y, zorder=1, linestyle='None', yerr=e, alpha=0.5, capsize=3, color='goldenrod', marker='.')\n",
+    "ax.errorbar(x, y, zorder=1, linestyle='None', yerr=e, alpha=0.35, capsize=3, color='goldenrod', marker='.')\n",
     "ax.plot(x,y_CI, zorder=2, color='crimson', alpha=0.8, label= 'CI')\n",
-    "plt.text(25, 0.3, 'CI', fontsize=23, color='crimson')\n",
+    "# plt.text(25, 0.3, 'CI', fontsize=23, color='crimson')\n",
     "ax.plot(x,y_CI*(-1), zorder=3, color='crimson', alpha=0.8)\n",
     "ax.errorbar(x2, y2, zorder=1, linestyle='None', capsize=3, color='crimson', marker='.')\n",
+    "ax.set_title(f'{assay_chembl_id}\\n{ax.get_title()}')\n",
     "\n",
     "plt.show()"
    ]
@@ -888,46 +11344,1752 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "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>ID</th>\n",
+       "      <th>VALUE</th>\n",
+       "      <th>nOccurence</th>\n",
+       "      <th>Nonadd_pC</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CHEMBL1490528</td>\n",
+       "      <td>5.699992</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CHEMBL1556431</td>\n",
+       "      <td>4.899998</td>\n",
+       "      <td>49</td>\n",
+       "      <td>0.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CHEMBL1513347</td>\n",
+       "      <td>4.899998</td>\n",
+       "      <td>46</td>\n",
+       "      <td>-0.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CHEMBL1590397</td>\n",
+       "      <td>5.599998</td>\n",
+       "      <td>43</td>\n",
+       "      <td>0.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CHEMBL1516787</td>\n",
+       "      <td>5.299998</td>\n",
+       "      <td>38</td>\n",
+       "      <td>-0.33</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              ID     VALUE  nOccurence  Nonadd_pC\n",
+       "0  CHEMBL1490528  5.699992          67       0.31\n",
+       "1  CHEMBL1556431  4.899998          49       0.31\n",
+       "2  CHEMBL1513347  4.899998          46      -0.31\n",
+       "3  CHEMBL1590397  5.599998          43       0.32\n",
+       "4  CHEMBL1516787  5.299998          38      -0.33"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ambiguous_compounds = ambiguous_compounds[['Compound_ID', 'Measured', 'nOccurence', 'Nonadd_pC']]\n",
     "ambiguous_compounds = ambiguous_compounds.rename(columns=({'Compound_ID':'ID'}))\n",
-    "ambiguous_compounds = pd.merge(ambiguous_compounds, curated, on='ID')\n",
-    "ambiguous_compounds = ambiguous_compounds[['ID', 'VALUE', 'nOccurence', 'Nonadd_pC']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_title = 'Outliers'\n",
-    "def plot_outliers(x, hist=True, kde=True, ins=int(100/5), color='crimson', kde_kws={'shade': True, 'linewidth': 2}):\n",
-    "    sns.set_style('ticks')\n",
-    "    fig, ax = plt.subplots(1,1)\n",
-    "    fig.set_size_inches(10, 8)\n",
-    "    sns.distplot(x, hist=True, kde=True,\n",
-    "                 bins=int(100/5), color='crimson',\n",
-    "                 kde_kws={'shade': True, 'linewidth': 2})\n",
-    "\n",
-    "    plt.title(my_title,  size=30)\n",
-    "    plt.xlabel('pActivity Value',  size=25)\n",
-    "    plt.ylabel('Density',  size=25)"
+    "ambiguous_compounds = pd.merge(ambiguous_compounds, df, on='ID')\n",
+    "ambiguous_compounds = ambiguous_compounds[['ID', 'VALUE', 'nOccurence', 'Nonadd_pC']]\n",
+    "ambiguous_compounds.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/cthoyt/.virtualenvs/cheminf/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
     {
      "data": {
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       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -935,7 +13097,9 @@
     }
    ],
    "source": [
-    "plot_outliers(ambiguous_compounds['VALUE'])"
+    "fig, ax = plot_outliers(ambiguous_compounds['VALUE'], 'Outliers')\n",
+    "ax.set_title(f'{assay_chembl_id}\\n{ax.get_title()}')\n",
+    "plt.show()"
    ]
   },
   {
@@ -948,26 +13112,1866 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
     "sign_NA_PC = PC_log1[['Compound_ID', 'Nonadd_pC']]\n",
     "sign_NA_PC = sign_NA_PC.rename(columns=({'Compound_ID':'ID'}))\n",
-    "sign_NA_PC = pd.merge(sign_NA_PC, curated, on='ID')\n",
+    "sign_NA_PC = pd.merge(sign_NA_PC, df, on='ID')\n",
     "sign_NA_PC = sign_NA_PC[['ID', 'VALUE', 'Nonadd_pC']]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/cthoyt/.virtualenvs/cheminf/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
     {
      "data": {
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\n",
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+       "     <use x=\"1233.984375\" xlink:href=\"#ArialMT-20\"/>\n",
+       "     <use x=\"1261.767578\" xlink:href=\"#ArialMT-4e\"/>\n",
+       "     <use x=\"1333.984375\" xlink:href=\"#ArialMT-41\"/>\n",
+       "     <use x=\"1395.183594\" xlink:href=\"#ArialMT-20\"/>\n",
+       "     <use x=\"1422.966797\" xlink:href=\"#ArialMT-53\"/>\n",
+       "     <use x=\"1489.666016\" xlink:href=\"#ArialMT-68\"/>\n",
+       "     <use x=\"1545.28125\" xlink:href=\"#ArialMT-69\"/>\n",
+       "     <use x=\"1567.498047\" xlink:href=\"#ArialMT-66\"/>\n",
+       "     <use x=\"1595.28125\" xlink:href=\"#ArialMT-74\"/>\n",
+       "    </g>\n",
+       "   </g>\n",
+       "  </g>\n",
+       " </g>\n",
+       " <defs>\n",
+       "  <clipPath id=\"p5e94f87e2e\">\n",
+       "   <rect height=\"217.44\" width=\"334.8\" x=\"46.923438\" y=\"34.657125\"/>\n",
+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -975,30 +14979,1869 @@
     }
    ],
    "source": [
-    "my_title = 'Compounds with Significant NA Shift'\n",
-    "plot_outliers(sign_NA_PC['VALUE'])"
+    "fig, ax = plot_outliers(sign_NA_PC['VALUE'], 'Compounds with Significant NA Shift')\n",
+    "ax.set_title(f'{assay_chembl_id}\\n{ax.get_title()}')\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, '# Compounds')"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/cthoyt/.virtualenvs/cheminf/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1008,8 +16851,7 @@
    "source": [
     "sns.set_style('ticks')\n",
     "fig, ax = plt.subplots(1,1)\n",
-    "fig.set_size_inches(10, 8)\n",
-    "sns.distplot(curated['VALUE'], hist=True, norm_hist=False, kde=False,\n",
+    "sns.distplot(df['VALUE'], hist=True, norm_hist=False, kde=False,\n",
     "             bins=int(100/5), color='goldenrod',\n",
     "             kde_kws={'shade': True, 'linewidth': 2})\n",
     "sns.distplot(ambiguous_compounds['VALUE'], hist=True, norm_hist=False, kde=False,\n",
@@ -1021,10 +16863,12 @@
     "\n",
     "legend = ['All','Outliers', 'NA shift']\n",
     "\n",
-    "plt.legend(legend, prop={'size': 20}) #title = '')\n",
-    "plt.title('',  size=30)\n",
-    "plt.xlabel('Logged Value',  size=25)\n",
-    "plt.ylabel('# Compounds',  size=25)"
+    "plt.legend(legend)\n",
+    "plt.title(assay_chembl_id)\n",
+    "plt.xlabel('Logged Value')\n",
+    "plt.ylabel('# Compounds')\n",
+    "plt.yscale('log')\n",
+    "plt.show()"
    ]
   },
   {
@@ -1038,7 +16882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1050,14 +16894,1786 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<svg height=\"288.704937pt\" version=\"1.1\" viewBox=\"0 0 383.3625 288.704937\" width=\"383.3625pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       " <metadata>\n",
+       "  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n",
+       "   <cc:Work>\n",
+       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
+       "    <dc:date>2021-08-08T15:51:28.724847</dc:date>\n",
+       "    <dc:format>image/svg+xml</dc:format>\n",
+       "    <dc:creator>\n",
+       "     <cc:Agent>\n",
+       "      <dc:title>Matplotlib v3.4.1, https://matplotlib.org/</dc:title>\n",
+       "     </cc:Agent>\n",
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       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1065,8 +18681,9 @@
     }
    ],
    "source": [
-    "my_title = 'Compounds with Strong NA Shift'\n",
-    "plot_outliers(strong_NA_PC['VALUE'])"
+    "fig, ax = plot_outliers(strong_NA_PC['VALUE'], 'Compounds with Strong NA Shift')\n",
+    "ax.set_title(f'{assay_chembl_id}\\n{ax.get_title()}')\n",
+    "plt.show()"
    ]
   },
   {
@@ -1078,26 +18695,1819 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 25,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
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\n",
-      "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 720x252 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -1108,25 +20518,25 @@
     "insignificant = len(MAIN.iloc[:,0]) - len(MAIN_log1.iloc[:,0])\n",
     "treashold_1 = len(MAIN_log1.iloc[:,0]) - len(MAIN_log2.iloc[:,0])\n",
     "treashold_2 = len(MAIN_log2.iloc[:,0])\n",
-    "labels = [\"Insignificant NA\", \"2 > NA > 1\", \"NA > 2\"]   \n",
+    "\n",
+    "fix, (lax, rax) = plt.subplots(1, 2, figsize=(10, 3.5))\n",
+    "\n",
+    "\n",
+    "labels = [\"Insig. NA\", \"2 > NA > 1\", \"NA > 2\"]   \n",
     "sizes = [insignificant, treashold_1, treashold_2]\n",
     "explode = [0,0.1,0.2]\n",
     "colors = ['cornsilk', 'crimson', 'red']  \n",
-    "fig = plt.figure(figsize = (8, 8))\n",
-    "plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', textprops={'fontsize': 20})\n",
-    "plt.axis('equal')\n",
-    "plt.show()\n",
     "\n",
-    "hist = MAIN_log1[MAIN_log1['Nonadditivity_abs'] < 3]\n",
+    "lax.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', textprops={'fontsize': 20})\n",
     "\n",
-    "ax = hist.hist(column= 'Nonadditivity_abs', bins=10, figsize=(10,8), color='crimson', alpha=0.6, zorder=10, rwidth=0.9)\n",
+    "upper_limit = 3.0\n",
+    "hist = MAIN_log1[MAIN_log1['Nonadditivity_abs'] < upper_limit]\n",
+    "hist.hist(column='Nonadditivity_abs', color='crimson', alpha=0.6, zorder=10, rwidth=0.9, ax=rax)\n",
     "\n",
-    "ax = ax[0]\n",
-    "for x in ax:\n",
-    "    x.grid(alpha=0)\n",
-    "    x.set_title(\"\", size=25)\n",
-    "    x.set_xlabel(\"Nonadditivity Distribution\", size=25)\n",
-    "    x.set_ylabel(\"# Cycles\", size=25)"
+    "rax.set_title(f\"Distribution of ${significant_thrs} < |Nonadditivity| < {upper_limit}$\")\n",
+    "rax.set_xlabel(\"|Nonadditivity|\")\n",
+    "rax.set_ylabel(\"# Cycles\")\n",
+    "plt.tight_layout()"
    ]
   },
   {
@@ -1138,26 +20548,1504 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 26,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<svg height=\"245.329688pt\" version=\"1.1\" viewBox=\"0 0 686.554303 245.329688\" width=\"686.554303pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       " <metadata>\n",
+       "  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n",
+       "   <cc:Work>\n",
+       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
+       "    <dc:date>2021-08-08T15:51:29.244746</dc:date>\n",
+       "    <dc:format>image/svg+xml</dc:format>\n",
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       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 720x252 with 2 Axes>"
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      },
      "metadata": {},
@@ -1165,7 +22053,9 @@
     }
    ],
    "source": [
-    "No_NA = len(curated['ID'].value_counts()) - len(PC['Compound_ID'].value_counts())\n",
+    "fig, (lax, rax) = plt.subplots(1, 2, figsize=(10, 3.5))\n",
+    "\n",
+    "No_NA = len(df['ID'].value_counts()) - len(PC['Compound_ID'].value_counts())\n",
     "No_significant =  len(PC['Compound_ID'].value_counts()) - len(PC_log1['Compound_ID'].value_counts()) + No_NA\n",
     "treashold_1 = len(PC_log1['Compound_ID'].value_counts()) - len(PC_log2['Compound_ID'].value_counts())\n",
     "treashold_2 = len(PC_log2['Compound_ID'].value_counts())\n",
@@ -1175,21 +22065,17 @@
     "colors = ['cornsilk', 'crimson', 'red']\n",
     "explode = [0,0.1,0.15]\n",
     "\n",
-    "fig = plt.figure(figsize = (8, 8))\n",
-    "plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', textprops={'fontsize': 20})\n",
-    "plt.axis('equal')\n",
-    "plt.show()\n",
+    "\n",
+    "lax.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%')\n",
     "\n",
     "PC_hist = PC_log1[PC_log1['Nonadd_abs'] < 3]\n",
+    "PC_hist.hist(column= 'Nonadd_abs', color='crimson', alpha=0.6, zorder=10, rwidth=0.9, ax=rax)\n",
     "\n",
-    "ax = PC_hist.hist(column= 'Nonadd_abs', bins=10, figsize=(10,8), color='crimson', alpha=0.6, zorder=10, rwidth=0.9)\n",
+    "rax.set_title(\"\")\n",
+    "rax.set_xlabel(\"Additivity Shift Distribution Per Compound\")\n",
+    "rax.set_ylabel(\"# Compounds\")\n",
     "\n",
-    "ax = ax[0]\n",
-    "for x in ax:\n",
-    "    x.grid(alpha=0)\n",
-    "    x.set_title(\"\", size=25)\n",
-    "    x.set_xlabel(\"Additivity Shift Distribution Per Compound\", size=25)\n",
-    "    x.set_ylabel(\"# Compounds\", size=25)"
+    "fig.tight_layout()"
    ]
   },
   {
@@ -1203,181 +22089,234 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def draw_image(ids, smiles, tsmarts, pActs, Acts, qualifiers, nonadd, mcss_tot, image_file, target=''):\n",
-    "    \"\"\"\n",
-    "    Draw Nonadditivity Circle to Image file\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    cpds = [Chem.MolFromSmiles(i) for i in smiles]\n",
-    "\n",
-    "    #########\n",
-    "    # Compute Coordinates of local MCSS, aligned with global MCSS\n",
-    "    \n",
-    "    mcss_loc = Chem.MolFromSmarts(rdFMCS.FindMCS(cpds, completeRingsOnly=True, timeout=60).smartsString)\n",
-    "    Chem.GetSymmSSSR(mcss_loc)\n",
-    "    \n",
-    "    if mcss_tot:\n",
-    "        mcss_tot_coords = [mcss_tot.GetConformer().GetAtomPosition(x) for x in range(mcss_tot.GetNumAtoms())]\n",
-    "        coords2D_tot = [Geometry.Point2D(pt.x, pt.y) for pt in mcss_tot_coords]\n",
-    "    \n",
-    "        mcss_match = mcss_loc.GetSubstructMatch(mcss_tot)\n",
-    "    \n",
-    "        coordDict = {}\n",
-    "        for i, coord in enumerate(coords2D_tot):\n",
-    "            coordDict[mcss_match[i]] = coord\n",
-    "        \n",
-    "        AllChem.Compute2DCoords(mcss_loc, coordMap=coordDict)\n",
-    "    else:\n",
-    "        AllChem.Compute2DCoords(mcss_loc)\n",
-    "    \n",
-    "    #########\n",
-    "    # Align circle compounds to local MCSS\n",
-    "    \n",
-    "    matchVs = [x.GetSubstructMatch(mcss_loc) for x in cpds]\n",
-    "\n",
-    "    # compute reference coordinates:\n",
-    "    mcss_loc_coords = [mcss_loc.GetConformer().GetAtomPosition(x) for x in range(mcss_loc.GetNumAtoms())]\n",
-    "    coords2D_loc = [Geometry.Point2D(pt.x, pt.y) for pt in mcss_loc_coords]\n",
-    "\n",
-    "    # generate coords for the other molecules using the common substructure\n",
-    "    for molIdx in range(4):\n",
-    "        mol = cpds[molIdx]\n",
-    "        coordDict = {}\n",
-    "        for i, coord in enumerate(coords2D_loc):\n",
-    "            coordDict[matchVs[molIdx][i]] = coord\n",
-    "        AllChem.Compute2DCoords(mol, coordMap=coordDict)\n",
-    "\n",
-    "    ##########\n",
-    "    # Assemble Image\n",
-    "\n",
-    "    qualifiers_inv = [\"\" for i in range(4)]\n",
-    "    for i in range(4):\n",
-    "        if qualifiers[i] == \">\":\n",
-    "            qualifiers_inv[i] = \"<\"\n",
-    "        elif qualifiers[i] == \"<\":\n",
-    "            qualifiers_inv[i] = \">\"\n",
-    "        else:\n",
-    "            continue\n",
-    "\n",
-    "    new_im = Image.new(\"RGB\", size=(650, 670), color=(255, 255, 255, 0))\n",
-    "    if Acts[0] != \"\":\n",
-    "        new_im.paste(Draw.MolToImage(cpds[0],\n",
-    "                                     size=(300, 300),\n",
-    "                                     legend=ids[0] + \"        \" + qualifiers_inv[0] + Acts[0] + \" (\"\n",
-    "                                     + qualifiers[0] + pActs[0] + \")\"),\n",
-    "                     (0, 0))\n",
-    "        new_im.paste(Draw.MolToImage(cpds[1],\n",
-    "                                     size=(300, 300),\n",
-    "                                     legend=ids[1] + \"        \" + qualifiers_inv[1] + Acts[1] + \" (\"\n",
-    "                                     + qualifiers[1] + pActs[1] + \")\"),\n",
-    "                     (350, 0))\n",
-    "        new_im.paste(Draw.MolToImage(cpds[2],\n",
-    "                                     size=(300, 300),\n",
-    "                                     legend=ids[2] + \"        \" + qualifiers_inv[2] + Acts[2] + \" (\"\n",
-    "                                     + qualifiers[2] + pActs[2]+\")\"),\n",
-    "                     (350, 350))\n",
-    "        new_im.paste(Draw.MolToImage(cpds[3],\n",
-    "                                     size=(300, 300),\n",
-    "                                     legend=ids[3] + \"        \" + qualifiers_inv[3] + Acts[3] + \" (\"\n",
-    "                                     + qualifiers[3] + pActs[3] + \")\"),\n",
-    "                     (0, 350))\n",
-    "\n",
-    "        draw = ImageDraw.Draw(new_im)\n",
-    "        #font = ImageFont.truetype(font_path, 14)\n",
-    "        draw.text((260, 330), \"Nonadditivity: \" + nonadd, fill=(0, 0, 0, 0))#, font=font)\n",
-    "\n",
-    "        #font = ImageFont.truetype(font_path, 10)\n",
-    "        if target != '':\n",
-    "            draw.text((10, 650), \"[uM]  (-log10[M])  Activity in Assay: \" + target, fill=(0, 0, 0, 0))#, font=font)\n",
-    "    else:\n",
-    "        new_im.paste(Draw.MolToImage(cpds[0],\n",
-    "                                     size=(300, 300),\n",
-    "                                     legend=ids[0]+\"        \"+qualifiers[0]+pActs[0]),\n",
-    "                     (0, 0))\n",
-    "        new_im.paste(Draw.MolToImage(cpds[1],\n",
-    "                                     size=(300, 300),\n",
-    "                                     legend=ids[1]+\"        \"+qualifiers[1]+pActs[1]),\n",
-    "                     (350, 0))\n",
-    "        new_im.paste(Draw.MolToImage(cpds[2],\n",
-    "                                     size=(300, 300),\n",
-    "                                     legend=ids[2]+\"        \"+qualifiers[2]+pActs[2]),\n",
-    "                     (350, 350))\n",
-    "        new_im.paste(Draw.MolToImage(cpds[3],\n",
-    "                                     size=(300, 300),\n",
-    "                                     legend=ids[3]+\"        \"+qualifiers[3]+pActs[3]),\n",
-    "                     (0, 350))\n",
-    "\n",
-    "        draw = ImageDraw.Draw(new_im)\n",
-    "        #font = ImageFont.truetype(font_path, 14)\n",
-    "        draw.text((260, 330), \"Nonadditivity: \" + nonadd, fill=(0, 0, 0, 0))#, font=font)\n",
-    "\n",
-    "        #font = ImageFont.truetype(font_path, 10)\n",
-    "        if target != '':\n",
-    "            draw.text((10, 650), \"Activity in Assay: \" + target, fill=(0, 0, 0, 0))#, font=font)\n",
-    "\n",
-    "    # Draw Arrows\n",
-    "    draw.line((300, 150, 350, 150), fill=0, width=2)\n",
-    "    draw.line((340, 145, 350, 150), fill=0, width=2)\n",
-    "    draw.line((340, 155, 350, 150), fill=0, width=2)\n",
-    "\n",
-    "    draw.line((300, 500, 350, 500), fill=0, width=2)\n",
-    "    draw.line((340, 495, 350, 500), fill=0, width=2)\n",
-    "    draw.line((340, 505, 350, 500), fill=0, width=2)\n",
-    "\n",
-    "    draw.line((150, 300, 150, 350), fill=0, width=2)\n",
-    "    draw.line((145, 340, 150, 350), fill=0, width=2)\n",
-    "    draw.line((155, 340, 150, 350), fill=0, width=2)\n",
-    "\n",
-    "    draw.line((500, 300, 500, 350), fill=0, width=2)\n",
-    "    draw.line((495, 340, 500, 350), fill=0, width=2)\n",
-    "    draw.line((505, 340, 500, 350), fill=0, width=2)\n",
-    "\n",
-    "    # Add Reaction Parts\n",
-    "    b = Chem.MolFromSmiles(tsmarts[0][:tsmarts[0].index(\">\")])\n",
-    "    new_im.paste(Draw.MolToImage(b, size=(50, 50)), (300, 90))\n",
-    "\n",
-    "    b = Chem.MolFromSmiles(tsmarts[0][tsmarts[0].index(\">\")+2:])\n",
-    "    new_im.paste(Draw.MolToImage(b, size=(50, 50)), (300, 160))\n",
-    "\n",
-    "    b = Chem.MolFromSmiles(tsmarts[0][:tsmarts[0].index(\">\")])\n",
-    "    new_im.paste(Draw.MolToImage(b, size=(50, 50)), (300, 440))\n",
-    "\n",
-    "    b = Chem.MolFromSmiles(tsmarts[0][tsmarts[0].index(\">\")+2:])\n",
-    "    new_im.paste(Draw.MolToImage(b, size=(50, 50)), (300, 510))\n",
-    "\n",
-    "    b = Chem.MolFromSmiles(tsmarts[1][:tsmarts[1].index(\">\")])\n",
-    "    new_im.paste(Draw.MolToImage(b, size=(50, 50)), (80, 300))\n",
-    "\n",
-    "    b = Chem.MolFromSmiles(tsmarts[1][tsmarts[1].index(\">\")+2:])\n",
-    "    new_im.paste(Draw.MolToImage(b, size=(50, 50)), (170, 300))\n",
-    "\n",
-    "    b = Chem.MolFromSmiles(tsmarts[1][:tsmarts[1].index(\">\")])\n",
-    "    new_im.paste(Draw.MolToImage(b, size=(50, 50)), (430, 300))\n",
-    "\n",
-    "    b = Chem.MolFromSmiles(tsmarts[1][tsmarts[1].index(\">\")+2:])\n",
-    "    new_im.paste(Draw.MolToImage(b, size=(50, 50)), (520, 300))\n",
-    "\n",
-    "    new_im.save(image_file)\n",
-    "\n",
-    "    return"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 27,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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       "text/plain": [
-       "<rdkit.Chem.rdchem.Mol at 0x7ff5c1ad9030>"
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       ]
      },
      "metadata": {},
@@ -1387,15 +22326,220 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "RDKit WARNING: [17:05:21] WARNING: not removing hydrogen atom with dummy atom neighbors\n",
-      "RDKit WARNING: [17:05:21] WARNING: not removing hydrogen atom with dummy atom neighbors\n"
+      "RDKit WARNING: [15:51:30] WARNING: not removing hydrogen atom with dummy atom neighbors\n",
+      "[15:51:30] WARNING: not removing hydrogen atom with dummy atom neighbors\n",
+      "RDKit WARNING: [15:51:30] WARNING: not removing hydrogen atom with dummy atom neighbors\n",
+      "[15:51:30] WARNING: not removing hydrogen atom with dummy atom neighbors\n"
      ]
     },
     {
      "data": {
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\n",
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\n",
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+       "' fill='#0000FF'/>\n",
+       "</svg>\n"
+      ],
       "text/plain": [
-       "<rdkit.Chem.rdchem.Mol at 0x7ff5c1909800>"
+       "<rdkit.Chem.rdchem.Mol at 0x16b83a5d0>"
       ]
      },
      "metadata": {},
@@ -1415,15 +22743,220 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "RDKit WARNING: [17:05:23] WARNING: not removing hydrogen atom with dummy atom neighbors\n",
-      "RDKit WARNING: [17:05:23] WARNING: not removing hydrogen atom with dummy atom neighbors\n"
+      "RDKit WARNING: [15:51:35] WARNING: not removing hydrogen atom with dummy atom neighbors\n",
+      "[15:51:35] WARNING: not removing hydrogen atom with dummy atom neighbors\n",
+      "RDKit WARNING: [15:51:35] WARNING: not removing hydrogen atom with dummy atom neighbors\n",
+      "[15:51:35] WARNING: not removing hydrogen atom with dummy atom neighbors\n"
      ]
     },
     {
      "data": {
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\n",
+      "image/png": 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+      ],
       "text/plain": [
-       "<rdkit.Chem.rdchem.Mol at 0x7ff5c3f7c1c0>"
+       "<rdkit.Chem.rdchem.Mol at 0x16b83a7b0>"
       ]
      },
      "metadata": {},
@@ -1433,15 +22966,179 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "RDKit WARNING: [17:05:23] WARNING: not removing hydrogen atom with dummy atom neighbors\n",
-      "RDKit WARNING: [17:05:23] WARNING: not removing hydrogen atom with dummy atom neighbors\n"
+      "RDKit WARNING: [15:51:35] WARNING: not removing hydrogen atom with dummy atom neighbors\n",
+      "[15:51:35] WARNING: not removing hydrogen atom with dummy atom neighbors\n",
+      "RDKit WARNING: [15:51:35] WARNING: not removing hydrogen atom with dummy atom neighbors\n",
+      "[15:51:35] WARNING: not removing hydrogen atom with dummy atom neighbors\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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+       "' fill='#FF0000'/>\n",
+       "</svg>\n"
+      ],
       "text/plain": [
-       "<rdkit.Chem.rdchem.Mol at 0x7ff5c3f73c60>"
+       "<rdkit.Chem.rdchem.Mol at 0x16bc54c10>"
       ]
      },
      "metadata": {},
@@ -1472,23 +23169,20 @@
     "    Chem.GetSymmSSSR(mcss_tot)\n",
     "    display(mcss_tot)\n",
     "\n",
-    "    image_file =my_path+'image_' + str('-'.join(ids)) + '.png'\n",
-    "    draw_image(ids, smiles, tsmarts, pActs, Acts, qualifiers, nonadd, mcss_tot, image_file, target)"
+    "    image_file = f'{assay_chembl_id}_image_' + str('-'.join(ids)) + '.png'\n",
+    "    draw_image(\n",
+    "        ids, smiles, tsmarts, pActs, Acts,\n",
+    "        qualifiers, nonadd, \n",
+    "        mcss_tot=mcss_tot, image_file=image_file, target=target,\n",
+    "    )"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "mmpdbEnv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "mmpdbenv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1500,7 +23194,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.2"
   }
  },
  "nbformat": 4,
diff --git a/README.md b/README.md
index a3abad5..d3fa3e8 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,9 @@
 # NonadditivityAnalysis
-Notebook for standardization of actvity data, nonadditivity analysis and its evaluation.
 
-A jupyter notebook for:
+Notebook for standardization of activity data, nonadditivity analysis and its evaluation.
+
+A Python package and corresponding Jupyter notebook for:
+
 1. Cleaning and standardizing ChEMBL activity data
 2. Running nonadditivity analysis based on NAA code published by C. Kramer [1]
 3. Evaluating the nonadditivity results
@@ -9,78 +11,27 @@ A jupyter notebook for:
 [1] Kramer C (2019) Nonadditivity Analysis. J Chem Inf Model 59:4034–4042. 
     https://doi.org/10.1021/acs.jcim.9b00631
 
+## Installation
 
---------------------
-
-## Requirements
-
-
-Installation requirements are the same as for the published NAA code:
-
-- A copy of the RDKit cheminformatics toolkit, available
-from http://rdkit.org/ 
-
-- A running version of mmpdb, a matched molecular pairs
-database generation and analysis toolkit, available from
-http://github.com/rdkit/mmpdb
-    ```shell
-    git clone https://github.com/rdkit/mmpdb.git
-    cd mmpdb
-    python setup.py install
-    ```
-
-- A running version of NAA, nonadditivity analysis code, available from
-https://github.com/KramerChristian/NonadditivityAnalysis
-    ```shell
-    git clone https://github.com/KramerChristian/NonadditivityAnalysis.git
-    cd NonadditivityAnalysis
-    # Add mmpdb path to line 44 of NonadditivityAnalysis.py
-    ```
-
-In order to run the NAA code directly from jupyter notebook, 
-you need to set an environmental variable.
-Therefore, on command line, generate the following directories and files:
+First, install a copy of the RDKit cheminformatics toolkit, available
+from http://rdkit.org/. The easiest way is to install via PyPI with
+`pip install rdkit-pypi`.
 
-```shell
-cd $CONDA_PREFIX
-mkdir -p ./etc/conda/activate.d
-mkdir -p ./etc/conda/deactivate.d
-touch ./etc/conda/activate.d/env_vars.sh
-touch ./etc/conda/deactivate.d/env_vars.sh
-```
+Install directly from source with:
 
-then edit ./etc/conda/activate.d/env_vars.sh as follows:
-```shell
-#!/bin/sh
-export NAA='/path/to/naa/code/'
+```bash
+$ pip install git+https://github.com/MolecularAI/NonadditivityAnalysis.git
 ```
 
-and edit ./etc/conda/deactivate.d/env_vars.sh as follows:
-```shell
-#!/bin/sh
-unset NAA
-```
+Install the code in development mode with:
 
-Apart from this, standard scientific python libraries like scipy and 
-numpy are required as well as seaborn and matplotlib for plot generation in the analysis part.
-```shell
-conda install -c anaconda scipy
-conda install -c anaconda numpy
-conda install -c conda-forge matplotlib
-conda install seaborn
+```bash
+$ git clone git+https://github.com/MolecularAI/NonadditivityAnalysis.git
+$ cd NonadditivityAnalysis
+$ pip install -e .
 ```
 
--------------------
-
 ## Usage
 
-
-The jupyter notebook can be run directly with gzipped activity data downloaded from ChEMBL, 
-as an example the activity data for ChEMBL1614027 (ChEMBL Version 27) is included in this package.
-'my_path' and 'my_name' has to be adjusted at the beginning of the jupyter notebook to 
-reflect the user's specific path and output name.
-
-
-
-
-
+The Jupyter notebook can be run for any ChEMBL assay via the `get_processed_assay_df()` function.
+While `ChEMBL1614027` is used as an example, any can be used by changing the `assay_chembl_id` variable.
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 0000000..d297eb0
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,40 @@
+##########################
+# Setup.py Configuration #
+##########################
+# Configuring setup()
+[metadata]
+name = nonadditivity_az
+version = 1.0.0
+description = An extension of the nonadditivity analysis from Kramer (2019)
+long_description = file: README.md
+long_description_content_type = text/markdown
+
+[options]
+install_requires =
+    # Scientific python stack
+    scipy
+    numpy
+    pandas
+    # Plotting
+    matplotlib
+    seaborn
+    # Cheminformatics tools
+    mmpdb
+    nonadditivity
+    chembl_downloader
+
+zip_safe = false
+include_package_data = True
+python_requires = >=3.6
+
+# Where is my code
+packages = find:
+package_dir =
+    = src
+
+[options.packages.find]
+where = src
+
+[options.extras_require]
+rdkit =
+    rdkit-pypi
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..a78fbfd
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,8 @@
+# -*- coding: utf-8 -*-
+
+"""Setup module."""
+
+import setuptools
+
+if __name__ == '__main__':
+    setuptools.setup()
diff --git a/src/nonadditivity_az/__init__.py b/src/nonadditivity_az/__init__.py
new file mode 100644
index 0000000..73ed64d
--- /dev/null
+++ b/src/nonadditivity_az/__init__.py
@@ -0,0 +1 @@
+"""Extended Nonadditivity Analysis."""
diff --git a/src/nonadditivity_az/plotting.py b/src/nonadditivity_az/plotting.py
new file mode 100644
index 0000000..5ff65cf
--- /dev/null
+++ b/src/nonadditivity_az/plotting.py
@@ -0,0 +1,304 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+from PIL import Image, ImageDraw
+from rdkit import Chem, Geometry
+from rdkit.Chem import AllChem, Draw, rdFMCS
+from scipy.stats import normaltest
+
+
+def NA_distribution(x, significant_thrs, alpha: float = 0.05):
+    sns.set_style("ticks")
+    fig, ax = plt.subplots(1, 1, figsize=(6, 3.5))
+    sns.distplot(
+        x,
+        hist=True,
+        kde=True,
+        bins=int(100 / 5),
+        color="crimson",
+        kde_kws={"shade": True, "linewidth": 2},
+        ax=ax,
+        label="Real",
+    )
+    n_of_cycles = len(x)
+    normal_dist = np.random.normal(loc=0, scale=significant_thrs, size=n_of_cycles)
+    sns.distplot(
+        normal_dist,
+        hist=False,
+        kde=True,
+        color="grey",
+        kde_kws={"shade": True, "linewidth": 2},
+        ax=ax,
+        label="Theoretical",
+    )
+    plt.legend()
+
+    stat, p = normaltest(x)
+    if p > alpha:
+        plt.title(f"Nonadditivity is Gaussian ($p<{p:.2e}$)")
+    else:
+        plt.title(f"Nonadditivity is not Gaussian ($p<{p:.2e}$)")
+
+    plt.xlabel("Nonadditivity")
+    plt.ylabel("Density")
+
+    return fig, ax
+
+
+def plot_outliers(x, title):
+    sns.set_style("ticks")
+    fig, ax = plt.subplots(1, 1)
+    sns.distplot(
+        x,
+        hist=True,
+        kde=True,
+        color="crimson",
+        kde_kws={"shade": True, "linewidth": 2},
+        ax=ax,
+    )
+    ax.set_title(title)
+    ax.set_xlabel("pActivity Value")
+    ax.set_ylabel("Density")
+    return fig, ax
+
+
+def draw_image(
+    ids,
+    smiles,
+    tsmarts,
+    pActs,
+    Acts,
+    qualifiers,
+    nonadd,
+    mcss_tot,
+    image_file,
+    target="",
+):
+    """
+    Draw Nonadditivity Circle to Image file
+    """
+
+    cpds = [Chem.MolFromSmiles(i) for i in smiles]
+
+    #########
+    # Compute Coordinates of local MCSS, aligned with global MCSS
+
+    mcss_loc = Chem.MolFromSmarts(
+        rdFMCS.FindMCS(cpds, completeRingsOnly=True, timeout=60).smartsString
+    )
+    Chem.GetSymmSSSR(mcss_loc)
+
+    if mcss_tot:
+        mcss_tot_coords = [
+            mcss_tot.GetConformer().GetAtomPosition(x)
+            for x in range(mcss_tot.GetNumAtoms())
+        ]
+        coords2D_tot = [Geometry.Point2D(pt.x, pt.y) for pt in mcss_tot_coords]
+
+        mcss_match = mcss_loc.GetSubstructMatch(mcss_tot)
+
+        coordDict = {}
+        for i, coord in enumerate(coords2D_tot):
+            coordDict[mcss_match[i]] = coord
+
+        AllChem.Compute2DCoords(mcss_loc, coordMap=coordDict)
+    else:
+        AllChem.Compute2DCoords(mcss_loc)
+
+    #########
+    # Align circle compounds to local MCSS
+
+    matchVs = [x.GetSubstructMatch(mcss_loc) for x in cpds]
+
+    # compute reference coordinates:
+    mcss_loc_coords = [
+        mcss_loc.GetConformer().GetAtomPosition(x)
+        for x in range(mcss_loc.GetNumAtoms())
+    ]
+    coords2D_loc = [Geometry.Point2D(pt.x, pt.y) for pt in mcss_loc_coords]
+
+    # generate coords for the other molecules using the common substructure
+    for molIdx in range(4):
+        mol = cpds[molIdx]
+        coordDict = {}
+        for i, coord in enumerate(coords2D_loc):
+            coordDict[matchVs[molIdx][i]] = coord
+        AllChem.Compute2DCoords(mol, coordMap=coordDict)
+
+    ##########
+    # Assemble Image
+
+    qualifiers_inv = ["" for i in range(4)]
+    for i in range(4):
+        if qualifiers[i] == ">":
+            qualifiers_inv[i] = "<"
+        elif qualifiers[i] == "<":
+            qualifiers_inv[i] = ">"
+        else:
+            continue
+
+    new_im = Image.new("RGB", size=(650, 670), color=(255, 255, 255, 0))
+    if Acts[0] != "":
+        new_im.paste(
+            Draw.MolToImage(
+                cpds[0],
+                size=(300, 300),
+                legend=ids[0]
+                + "        "
+                + qualifiers_inv[0]
+                + Acts[0]
+                + " ("
+                + qualifiers[0]
+                + pActs[0]
+                + ")",
+            ),
+            (0, 0),
+        )
+        new_im.paste(
+            Draw.MolToImage(
+                cpds[1],
+                size=(300, 300),
+                legend=ids[1]
+                + "        "
+                + qualifiers_inv[1]
+                + Acts[1]
+                + " ("
+                + qualifiers[1]
+                + pActs[1]
+                + ")",
+            ),
+            (350, 0),
+        )
+        new_im.paste(
+            Draw.MolToImage(
+                cpds[2],
+                size=(300, 300),
+                legend=ids[2]
+                + "        "
+                + qualifiers_inv[2]
+                + Acts[2]
+                + " ("
+                + qualifiers[2]
+                + pActs[2]
+                + ")",
+            ),
+            (350, 350),
+        )
+        new_im.paste(
+            Draw.MolToImage(
+                cpds[3],
+                size=(300, 300),
+                legend=ids[3]
+                + "        "
+                + qualifiers_inv[3]
+                + Acts[3]
+                + " ("
+                + qualifiers[3]
+                + pActs[3]
+                + ")",
+            ),
+            (0, 350),
+        )
+
+        draw = ImageDraw.Draw(new_im)
+        # font = ImageFont.truetype(font_path, 14)
+        draw.text(
+            (260, 330), "Nonadditivity: " + nonadd, fill=(0, 0, 0, 0)
+        )  # , font=font)
+
+        # font = ImageFont.truetype(font_path, 10)
+        if target != "":
+            draw.text(
+                (10, 650),
+                "[uM]  (-log10[M])  Activity in Assay: " + target,
+                fill=(0, 0, 0, 0),
+            )  # , font=font)
+    else:
+        new_im.paste(
+            Draw.MolToImage(
+                cpds[0],
+                size=(300, 300),
+                legend=ids[0] + "        " + qualifiers[0] + pActs[0],
+            ),
+            (0, 0),
+        )
+        new_im.paste(
+            Draw.MolToImage(
+                cpds[1],
+                size=(300, 300),
+                legend=ids[1] + "        " + qualifiers[1] + pActs[1],
+            ),
+            (350, 0),
+        )
+        new_im.paste(
+            Draw.MolToImage(
+                cpds[2],
+                size=(300, 300),
+                legend=ids[2] + "        " + qualifiers[2] + pActs[2],
+            ),
+            (350, 350),
+        )
+        new_im.paste(
+            Draw.MolToImage(
+                cpds[3],
+                size=(300, 300),
+                legend=ids[3] + "        " + qualifiers[3] + pActs[3],
+            ),
+            (0, 350),
+        )
+
+        draw = ImageDraw.Draw(new_im)
+        # font = ImageFont.truetype(font_path, 14)
+        draw.text(
+            (260, 330), "Nonadditivity: " + nonadd, fill=(0, 0, 0, 0)
+        )  # , font=font)
+
+        # font = ImageFont.truetype(font_path, 10)
+        if target != "":
+            draw.text(
+                (10, 650), "Activity in Assay: " + target, fill=(0, 0, 0, 0)
+            )  # , font=font)
+
+    # Draw Arrows
+    draw.line((300, 150, 350, 150), fill=0, width=2)
+    draw.line((340, 145, 350, 150), fill=0, width=2)
+    draw.line((340, 155, 350, 150), fill=0, width=2)
+
+    draw.line((300, 500, 350, 500), fill=0, width=2)
+    draw.line((340, 495, 350, 500), fill=0, width=2)
+    draw.line((340, 505, 350, 500), fill=0, width=2)
+
+    draw.line((150, 300, 150, 350), fill=0, width=2)
+    draw.line((145, 340, 150, 350), fill=0, width=2)
+    draw.line((155, 340, 150, 350), fill=0, width=2)
+
+    draw.line((500, 300, 500, 350), fill=0, width=2)
+    draw.line((495, 340, 500, 350), fill=0, width=2)
+    draw.line((505, 340, 500, 350), fill=0, width=2)
+
+    # Add Reaction Parts
+    b = Chem.MolFromSmiles(tsmarts[0][: tsmarts[0].index(">")])
+    new_im.paste(Draw.MolToImage(b, size=(50, 50)), (300, 90))
+
+    b = Chem.MolFromSmiles(tsmarts[0][tsmarts[0].index(">") + 2 :])
+    new_im.paste(Draw.MolToImage(b, size=(50, 50)), (300, 160))
+
+    b = Chem.MolFromSmiles(tsmarts[0][: tsmarts[0].index(">")])
+    new_im.paste(Draw.MolToImage(b, size=(50, 50)), (300, 440))
+
+    b = Chem.MolFromSmiles(tsmarts[0][tsmarts[0].index(">") + 2 :])
+    new_im.paste(Draw.MolToImage(b, size=(50, 50)), (300, 510))
+
+    b = Chem.MolFromSmiles(tsmarts[1][: tsmarts[1].index(">")])
+    new_im.paste(Draw.MolToImage(b, size=(50, 50)), (80, 300))
+
+    b = Chem.MolFromSmiles(tsmarts[1][tsmarts[1].index(">") + 2 :])
+    new_im.paste(Draw.MolToImage(b, size=(50, 50)), (170, 300))
+
+    b = Chem.MolFromSmiles(tsmarts[1][: tsmarts[1].index(">")])
+    new_im.paste(Draw.MolToImage(b, size=(50, 50)), (430, 300))
+
+    b = Chem.MolFromSmiles(tsmarts[1][tsmarts[1].index(">") + 2 :])
+    new_im.paste(Draw.MolToImage(b, size=(50, 50)), (520, 300))
+
+    new_im.save(image_file)
diff --git a/src/nonadditivity_az/utils.py b/src/nonadditivity_az/utils.py
new file mode 100644
index 0000000..bae62e1
--- /dev/null
+++ b/src/nonadditivity_az/utils.py
@@ -0,0 +1,237 @@
+import multiprocessing as mp
+from pathlib import Path
+from typing import Tuple
+
+import numpy as np
+import pandas as pd
+import pystow
+from rdkit import Chem
+from rdkit.Chem import MolStandardize
+from rdkit.Chem.MolStandardize import rdMolStandardize
+
+import chembl_downloader
+from chembl_downloader.queries import get_assay_sql
+
+MODULE = pystow.module("nonadditivity")
+
+
+def get_processed_assay_df(assay_chembl_id: str) -> Tuple[pd.DataFrame, Path]:
+    submodule = MODULE.submodule(assay_chembl_id)
+    assay_raw_path = submodule.join(name="raw.tsv")
+    assay_processed_path = submodule.join(name="processed.tsv")
+
+    if assay_processed_path.is_file():
+        return pd.read_csv(assay_processed_path, sep="\t"), assay_processed_path
+
+    if assay_raw_path.is_file():
+        data = pd.read_csv(assay_raw_path, sep="\t")
+    else:
+        data = chembl_downloader.query(get_assay_sql(assay_chembl_id))
+        data.columns = [
+            "Smiles",
+            "Molecule ChEMBL ID",
+            "Standard Type",
+            "Standard Relation",
+            "Standard Value",
+            "Standard Units",
+        ]
+        data.to_csv(assay_raw_path, sep="\t", index=False)
+
+    df = process(data)
+    df.to_csv(assay_processed_path, sep="\t", index=False)
+    return df, assay_processed_path
+
+
+def discard_nan_smiles(df):
+    """Remove NaNs from SMILES column"""
+    df = df.dropna(subset=["SMILES"])
+    return df
+
+
+def discard_uncertain_values(df):
+    """Deleting uncertain values"""
+    df = df[df["Standard Relation"] != "'>'"]
+    df = df[df["Standard Relation"] != "'<'"]
+    df["VALUE"] = df["VALUE"].astype(float)
+    df = df[df["VALUE"] > 0]  # in case one needs to delete negative values
+    df = df.drop(columns=["Standard Relation"])
+    return df
+
+
+unit_conversion = {
+    "M": 1,
+    "mM": 1000,
+    "uM": 1000000,
+    "nM": 1000000000,
+    "pM": 1000000000000,
+    "fM": 1000000000000000,
+}
+
+
+def log_converstion(x, UNIT):
+    if UNIT not in unit_conversion:
+        return x
+    x = -1 * np.log10(x / unit_conversion[UNIT])
+    return x
+
+
+def create_conversion_column(df):
+    """Converting values to logged ones"""
+    arr = [log_converstion(x["VALUE"], x["UNIT"]) for idx, x in df.iterrows()]
+
+    df["NEW_VALUE"] = arr
+    df = df[df["NEW_VALUE"] > 0]
+    df = df.drop(columns=["VALUE"])
+
+    # deleting the values that are more than 10 mM and less than 1 fM
+
+    df = df[df["NEW_VALUE"] > 2]
+    df = df[df["NEW_VALUE"] < 11]
+
+    return df
+
+
+def calculate_average(df):
+    """Calculating the avarage of the activity and calculating the median"""
+    df["Median_Value"] = df.groupby(["COMPOUND_NAME"])["NEW_VALUE"].transform("median")
+    df["max_value"] = df.groupby(["COMPOUND_NAME"])["NEW_VALUE"].transform("max")
+    df["min_value"] = df.groupby(["COMPOUND_NAME"])["NEW_VALUE"].transform("min")
+    df["difference"] = df.max_value - df.min_value
+
+    df = df.drop_duplicates(subset=["COMPOUND_NAME"], keep="first")
+
+    return df
+
+
+def discard_ambiguous_compound_measurements(df, max_thrs=2.5):
+    """Delete compounds that have been measured several times in one test and differ more than 2.5 log units"""
+    df = df[df.difference < max_thrs]
+
+    df = df[["SMILES", "COMPOUND_NAME", "ENDPOINT", "Median_Value", "MEASUREMENT"]]
+    df = df.rename(columns=({"Median_Value": "VALUE"}))
+
+    return df
+
+
+def standardize_rdkit(row, col):
+    """Standardize molecules using RDkit"""
+    smi = row[col]
+
+    try:
+        mol = Chem.MolFromSmiles(smi)  # sanitization is done by default
+        fmol = rdMolStandardize.FragmentParent(mol)  # returns largest fragment
+        cmol = rdMolStandardize.ChargeParent(fmol)  # uncharges the largest fragment
+        smi = Chem.MolToSmiles(cmol)
+        ssmi = MolStandardize.canonicalize_tautomer_smiles(
+            smi
+        )  # returns the canonicalized tautomer
+        tsmi = MolStandardize.rdMolStandardize.StandardizeSmiles(ssmi)  # standardize
+    except:
+        tsmi = "none"
+
+    return tsmi
+
+
+def generateStandarizedSmiles(smilesfile, smiles_column):
+    # set number of cores for parallelization
+    pool = mp.Pool(8)
+    stsmi_list = pool.starmap(
+        standardize_rdkit, [(smi, smiles_column) for idx, smi in smilesfile.iterrows()]
+    )
+    pool.close()
+
+    smilesfile[smilesfile.columns[smiles_column]] = stsmi_list
+
+    return smilesfile
+
+
+def merge_duplicate_smiles(df):
+    """Discard duplicate SMILES
+
+    - Keep the one with the highest value, i.e. most active one
+    """
+    df = df.sort_values("VALUE").drop_duplicates(subset=["SMILES"], keep="last")
+    return df
+
+
+def discarding_heavy_mols(smi, min_size=0, max_size=70):
+    """Remove molecules with > 70 heavy atoms"""
+    try:
+        mol = Chem.MolFromSmiles(smi, sanitize=False)
+        if min_size <= mol.GetNumHeavyAtoms() <= max_size:
+            return False
+        else:
+            return True
+    except Exception:
+        return True
+
+
+def removeHeavyMols(df, smiles_column):
+    idx = []
+    discard = []
+    for index, row in df.iterrows():
+        # for smi in df.iloc[:,smiles_column]:
+        if discarding_heavy_mols(row[smiles_column]):
+            idx.append(index)
+            discard.append(row.values.tolist())
+
+    df.drop(idx, inplace=True)
+    return df
+
+
+def process(data: pd.DataFrame):
+    df = data.rename(
+        columns=(
+            {
+                "Molecule ChEMBL ID": "COMPOUND_NAME",
+                "Smiles": "SMILES",
+                "Standard Value": "VALUE",
+                "Standard Units": "UNIT",
+                "Standard Type": "ENDPOINT",
+            }
+        )
+    )
+    df = df[
+        ["SMILES", "COMPOUND_NAME", "ENDPOINT", "Standard Relation", "VALUE", "UNIT"]
+    ]
+
+    print("#cmpds: ", len(df["COMPOUND_NAME"]))
+    print("#unique cmpds: ", len(df["COMPOUND_NAME"].value_counts()))
+
+    # Counting how many times compounds were measured in tests
+    df["MEASUREMENT"] = df.groupby(["COMPOUND_NAME"])["COMPOUND_NAME"].transform(
+        "count"
+    )
+
+    # Discard cmpds without SMILES
+    df = discard_nan_smiles(df)
+    print("#cpds with SMILES: ", len(df.iloc[:, 0]))
+
+    # Discard ambiguous data
+    df = discard_uncertain_values(df)
+    print("#cpds with values: ", len(df.iloc[:, 0]))
+
+    # Convert IC50 to pIC50
+    df = create_conversion_column(df)
+
+    # Calculate average values and discard cpds with > 2.5 log unit measurement differences
+    df = calculate_average(df)
+    df = discard_ambiguous_compound_measurements(df)
+    print("#cpds after merging multi measurements: ", len(df.iloc[:, 0]))
+
+    # standardize SMILES, merge duplicates and retain higher active one
+    smiles_column = 0
+    df = generateStandarizedSmiles(df, smiles_column)
+    df = df[df["SMILES"] != "none"]
+    df = merge_duplicate_smiles(df)
+    print("#cpds after merging duplicate SMILES: ", len(df.iloc[:, 0]))
+
+    # Remove cpds with > 70 HA
+    smiles_column = 0
+    df = removeHeavyMols(df, smiles_column)
+    print("#cpds < 70 HA: ", len(df.iloc[:, 0]))
+
+    # Rename columns for subsequent NAA
+    df = df.rename(columns=({"COMPOUND_NAME": "ID"}))
+    df = df[["ID", "SMILES", "VALUE", "MEASUREMENT"]]
+    return df