vignettes/Binary Class Example.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "## Package Description\n",
    "\n",
    "`bmdrc` is a python library for calculating **B**ench**m**ark **D**ose **R**esponse **C**urves for dichotomous (binary) and light photomotor (continuous converted to dichotomous) data. This \n",
    "tutorial will take users through the library using a dichotomous (binary response) example. `bmdrc` is broken up into five modules \n",
    "\n",
    "\n",
    "![five_modules](../bmdrc.png): \n",
    "\n",
    "\n",
    "1. *Input Data Module:* Import data into the python library\n",
    "\n",
    "2. *Pre-Processing Module:* Combine and remove endpoints as needed\n",
    "\n",
    "3. *Filtering Modules:* Apply the EPA recommendations for filtering \n",
    "\n",
    "4. *Model Fitting Modules:* Fit EPA-recommended models to data\n",
    "\n",
    "5. *Output Modules:* Select tables to output as csvs. View plots in a HTML report.\n",
    "\n",
    "First, install the package from pip using: \n",
    "\n",
    "`pip install bmdrc`\n",
    "\n",
    "Or from github using: \n",
    "\n",
    "`pip install git+https://github.com/PNNL-CompBio/bmdrc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load local libraries\n",
    "import bmdrc \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Module 1: Input Data Modules \n",
    "\n",
    "The first step to using the bmdrc python library is to properly format the data. For dichotmous data, use the `BinaryClass` object which can be imported using `from bmdrc.BinaryClass import BinaryClass`. Next, read the data as a pandas dataframe. Note that the data can be in either wide or long format, but if provided in wide format, there are some assumptions made about data structure. \n",
    "\n",
    "### Long Format\n",
    "\n",
    "Here is an example of the data in long format, with a column for the chemical (chemical.id), concentration (concentration), plate ID (plate.id), well name (well), endpoint measured (endpoint), and value (0 for No and 1 for Yes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chemical.id</th>\n",
       "      <th>concentration</th>\n",
       "      <th>plate.id</th>\n",
       "      <th>well</th>\n",
       "      <th>endpoint</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>DP24</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>DP24</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>DP24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>DP24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   chemical.id  concentration plate.id  well endpoint  value\n",
       "0            1            0.0        A     1     DP24      1\n",
       "1            1            0.0        A     2     DP24      1\n",
       "2            1            0.0        A     3     DP24      1\n",
       "3            1            0.0        A     4     DP24      0\n",
       "4            1            0.0        A     5     DP24      0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"../data/Binary_Simplified_Long.csv\").head().drop(\"Notes\", axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's how you create a binary class object with this data and information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bmdrc.BinaryClass import BinaryClass\n",
    "\n",
    "Long = BinaryClass(\n",
    "    df = pd.read_csv(\"../data/Binary_Simplified_Long.csv\").drop(\"Notes\", axis = 1), # Input is a pandas DataFrame\n",
    "    chemical = \"chemical.id\", # The name of the chemical column \n",
    "    plate = \"plate.id\", # The name of the plate ID column\n",
    "    well = \"well\", # The name of the column with well names\n",
    "    concentration = \"concentration\", # The name of the concentration column\n",
    "    endpoint = \"endpoint\", # The name of the column with endpoints\n",
    "    value = \"value\", # The name of the column with values\n",
    "    format = \"long\" # The format of the input data, either 'long' or 'wide' is accepted\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wide Format\n",
    "\n",
    "In wide format, the columns are chemical (chemical.id), concentration (conc), plate ID (plate.id), well name (well), and the remaining columns are assumed to be endpoints. Please remove any columns where this isn't the case. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chemical.id</th>\n",
       "      <th>conc</th>\n",
       "      <th>plate.id</th>\n",
       "      <th>well</th>\n",
       "      <th>MO24</th>\n",
       "      <th>DP24</th>\n",
       "      <th>SM24</th>\n",
       "      <th>NC24</th>\n",
       "      <th>MORT</th>\n",
       "      <th>YSE_</th>\n",
       "      <th>...</th>\n",
       "      <th>SOMI</th>\n",
       "      <th>PFIN</th>\n",
       "      <th>CFIN</th>\n",
       "      <th>PIG_</th>\n",
       "      <th>CIRC</th>\n",
       "      <th>TRUN</th>\n",
       "      <th>SWIM</th>\n",
       "      <th>NC__</th>\n",
       "      <th>TR__</th>\n",
       "      <th>DNC_</th>\n",
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       "      <th>0</th>\n",
       "      <td>3757</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19877</td>\n",
       "      <td>H02</td>\n",
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       "      <th>2</th>\n",
       "      <td>3757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19877</td>\n",
       "      <td>H03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19877</td>\n",
       "      <td>H04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19877</td>\n",
       "      <td>H05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   chemical.id  conc  plate.id well  MO24  DP24  SM24  NC24  MORT  YSE_  ...  \\\n",
       "0         3757   0.0     19877  H01   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "1         3757   0.0     19877  H02   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "2         3757   0.0     19877  H03   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "3         3757   0.0     19877  H04   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "4         3757   0.0     19877  H05   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "\n",
       "   SOMI  PFIN  CFIN  PIG_  CIRC  TRUN  SWIM  NC__  TR__  DNC_  \n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     0  \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     0  \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     0  \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     0  \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"../data/Binary_Morphology_Wide.csv\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our functions are built to convert wide format to long to reduce computational times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>chemical.id</th>\n",
       "      <th>conc</th>\n",
       "      <th>plate.id</th>\n",
       "      <th>well</th>\n",
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       "      <th>1</th>\n",
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       "      <td>MO24</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>3757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19877</td>\n",
       "      <td>H04</td>\n",
       "      <td>MO24</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>3757</td>\n",
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       "      <td>H05</td>\n",
       "      <td>MO24</td>\n",
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      "text/plain": [
       "   chemical.id  conc  plate.id well endpoint  value\n",
       "0         3757   0.0     19877  H01     MO24    0.0\n",
       "1         3757   0.0     19877  H02     MO24    0.0\n",
       "2         3757   0.0     19877  H03     MO24    0.0\n",
       "3         3757   0.0     19877  H04     MO24    0.0\n",
       "4         3757   0.0     19877  H05     MO24    0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Wide = BinaryClass(\n",
    "    df = pd.read_csv(\"../data/Binary_Morphology_Wide.csv\"),\n",
    "    chemical = \"chemical.id\",\n",
    "    plate = \"plate.id\",\n",
    "    well = \"well\",\n",
    "    concentration = \"conc\",\n",
    "    endpoint = \"endpoint\",\n",
    "    value = \"value\",\n",
    "    format = \"wide\"\n",
    ")\n",
    "\n",
    "Wide.df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attributes \n",
    "\n",
    "All column names and the original format (wide or long) is stored in the attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The LONG dataset has the following attributes: chemical.id, concentration, endpoint, long, plate.id, value, well\n",
      "\n",
      "The WIDE dataset has the following attributes: chemical.id, conc, endpoint, wide, plate.id, value, well\n"
     ]
    }
   ],
   "source": [
    "print(\"The LONG dataset has the following attributes: \" + Long.chemical, Long.concentration, Long.endpoint, Long.format, Long.plate, Long.value, Long.well, sep = \", \")\n",
    "print()\n",
    "print(\"The WIDE dataset has the following attributes: \" + Wide.chemical, Wide.concentration, Wide.endpoint, Wide.format, Wide.plate, Wide.value, Wide.well, sep = \", \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Module 2: Pre-Processing Modules\n",
    "\n",
    "These modules allow for the creation of new endpoints from existing ones, the removal of wells with specific variables (like \"DNC\" which stands for \"Do Not Count\"), and the removal of endpoints that shouldn't be modeled. \n",
    "\n",
    "### Combine Endpoints\n",
    "\n",
    "To combine endpoints, first create a python dictionary where the resulting term is the key and the endpoints to combine are in a list as the key's value. Then use the function `.combine_and_create_new_endpoints` to make the new endpoints. Note that the combination is an \"or\" statement, so if there is a 1 in any considered endpoint, the resulting value will be a 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chemical.id</th>\n",
       "      <th>concentration</th>\n",
       "      <th>plate.id</th>\n",
       "      <th>well</th>\n",
       "      <th>endpoint</th>\n",
       "      <th>value</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>ANY24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>ANY24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>ANY24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>ANY24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>ANY24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   chemical.id  concentration plate.id  well endpoint  value\n",
       "0            1            0.0        A     1    ANY24      1\n",
       "1            1            0.0        A     2    ANY24      1\n",
       "2            1            0.0        A     3    ANY24      1\n",
       "3            1            0.0        A     4    ANY24      0\n",
       "4            1            0.0        A     5    ANY24      0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dictionary of terms to add\n",
    "endpoint_dict = {\"ANY24\":[\"NC24\", \"DP24\", \"SM24\"], \n",
    "                 \"ANY\":[\"NC24\", \"DP24\", \"SM24\", \"JAW\"]}\n",
    "\n",
    "# Add new endpoint\n",
    "Long.combine_and_create_new_endpoints(endpoint_dict)\n",
    "\n",
    "Long.df[Long.df[\"endpoint\"] == \"ANY24\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ANY24': ['NC24', 'DP24', 'SM24'], 'ANY': ['NC24', 'DP24', 'SM24', 'JAW']}\n"
     ]
    }
   ],
   "source": [
    "# The `report_combination` attribute stores the endpoint dictionary\n",
    "print(Long.report_combination)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binary Class: Set wells to NA\n",
    "\n",
    "Certain wells need to be set to NA, including wells that are marked \"DNC\" for do not count. Here, specify the endpoint_name with the term to flag, the end_point value that should be considered for removal (0 or 1), and any endpoints where this rule does not apply. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>chemical.id</th>\n",
       "      <th>concentration</th>\n",
       "      <th>plate.id</th>\n",
       "      <th>well</th>\n",
       "      <th>endpoint</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>DP24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>DP24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>SM24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>SM24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>JAW</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>JAW</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>DNC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>DNC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>ANY24</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>ANY24</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>ANY</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>ANY</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     chemical.id  concentration plate.id  well endpoint  value\n",
       "42             1            3.0        B     1     DP24    NaN\n",
       "54             1            6.0        A     1     DP24    NaN\n",
       "186            1            3.0        B     1     SM24    NaN\n",
       "198            1            6.0        A     1     SM24    NaN\n",
       "294            1            3.0        B     1      JAW    NaN\n",
       "306            1            6.0        A     1      JAW    NaN\n",
       "402            1            3.0        B     1      DNC    NaN\n",
       "414            1            6.0        A     1      DNC    NaN\n",
       "42             1            3.0        B     1    ANY24    1.0\n",
       "54             1            6.0        A     1    ANY24    1.0\n",
       "42             1            3.0        B     1      ANY    NaN\n",
       "54             1            6.0        A     1      ANY    NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set specific endpoints to NA\n",
    "Long.set_well_to_na(endpoint_name = \"DNC\", endpoint_value = 1, except_endpoint = [\"ANY24\"])\n",
    "\n",
    "# These should all be NA, except for ANY\n",
    "Long.df[Long.df[\"bmdrc.Well.ID\"].isin([\"1 3.0 B 1\", \"1 6.0 A 1\"])].iloc[:, :6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1 0.0 B 6',\n",
       " '1 0.0 C 6',\n",
       " '1 1.5 A 2',\n",
       " '1 1.5 B 6',\n",
       " '1 1.5 C 6',\n",
       " '1 3.0 B 1',\n",
       " '1 6.0 A 1'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Long.set_well_to_na(endpoint_name = [\"DNC\", \"NC24\"], endpoint_value = 1, except_endpoint = [\"JAW\"])\n",
    "\n",
    "set(Long.df[(Long.df[Long.endpoint].isin([\"DNC\", \"NC24\"])) & (Long.df[Long.value] != 0)][\"bmdrc.Well.ID\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The endpoint_name, endpoint_value, and except_endpoint variables are also stored in the attributes for reporting purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[['DNC'], [1], ['ANY24']]]\n"
     ]
    }
   ],
   "source": [
    "print(Long.report_well_na)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binary Class: Remove Endpoint\n",
    "\n",
    "Some endpoints need to be removed completely. Information regarding the removed endpoints is also stored in the results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['DP24', 'NC24', 'SM24', 'JAW', 'ANY24', 'ANY'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DNC should be removed\n",
    "Long.remove_endpoints(\"DNC\")\n",
    "\n",
    "# 'DNC' is no longer in the endpoints\n",
    "Long.df[\"endpoint\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Module 3: Filtering Modules\n",
    "\n",
    "The EPA has recommendations for filters and filter values to minimize potential errors when calculating benchmark dose response curves. Filter parameters and filtered data are tracked in attributes for report-building purposes. \n",
    "\n",
    "### Negative Control Filter\n",
    "\n",
    "The EPA recommends no more than 50% response in the negative controls, which are wells with a concentration of 0 for their chemicals. Any plates with an unusually high response in their controls should be tossed. Default is set to 50%. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the percentage and build the diagnostic plot, but don't actually apply the filter. \n",
    "Long.filter_negative_control(percentage = 50, apply = False, diagnostic_plot = False)\n",
    "\n",
    "# View the plot\n",
    "Long.filter_negative_control_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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>Response</th>\n",
       "      <th>Count</th>\n",
       "      <th>Filter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>Keep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>Keep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.666667</td>\n",
       "      <td>11</td>\n",
       "      <td>Keep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.333333</td>\n",
       "      <td>4</td>\n",
       "      <td>Keep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>Filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>66.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>Filter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Response  Count  Filter\n",
       "0   0.000000     12    Keep\n",
       "1   8.333333      1    Keep\n",
       "2  16.666667     11    Keep\n",
       "3  33.333333      4    Keep\n",
       "4  50.000000      2  Filter\n",
       "5  66.666667      1  Filter"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the filter summary dataframe\n",
    "Long.filter_negative_control_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Apply the negative control filter\n",
    "Long.filter_negative_control(percentage = 50, apply = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Minimum Concentration Filter\n",
    "\n",
    "Another recommendation from the EPA is that any endpoints with less than 3 measurements for a specific endpoint should be tossed entirely. The default is 3. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W8XrIj//+978qW7asnn766WzDsrbnr7/+Wp6enho8eLDT8OHDh8sYo2+++capvU2bNqpSpYrjcd26dRUYGOh4PjMzM7V48WJ16tQpx7vuZc33k08+UbNmzVSqVCmn57JNmzbKyMjIdurM/fffr1KlSjkeN2vWTJLytc3cd999jtPmsnh6ejquS8rMzNSJEyd08eJFNWrUSFu2bMnztK3yuy+qVauWY3kkKSQkRDVq1HBatqvdj2Rx1bad1+3BWufl9rVXU+e17hey/Pzzz0pMTNSQIUOyXRNordnHx8fx//nz53X8+HHddtttknRV21R+p5fTvvXvv/927EOy1vmlr/khQ4bkqZ5PPvlENWvW1M033+y0Dlq1aiVJ2dbBlfYZGRkZWrZsmbp06aKKFSs6+tWsWVOxsbE51nD77bcrJibG8bhixYrq3Lmzli1bpoyMjALZp/r7++uhhx5yPPby8lLjxo2v+X0K158b+4pTuKXAwEBJ0pkzZ/LUf//+/fLw8FDVqlWd2suXL6/g4OA8BZrcWHfckhwf1vJyDc748ePVuXNnVa9eXbVr11a7du308MMPq27dupcdb//+/br11luztdesWdMxvHbt2nldhDzx9vbO9iGyVKlSTsu5Z88enT59WqGhoTlO4+jRowUyn/zKaZpBQUGqUKFCtg9iQUFBOc6rWrVqTo/9/f0VFhbm+B2TrA+oWR8GLpW1zWYpUaKEKlSocMXajx07ptTUVNWoUSPbsJo1ayozM1MHDhxQdHT0FadllZCQoBo1alz2pgL79+9XeHh4ti8jrNuZ1aWvBcl53R07dkzJyclX3Db37NmjX375Jds6y3LpdnQtr8EslSpVyrF9/vz5evXVV7Vr1y5duHDhiv2vJL/7ois9p9LV70eyuHLbzsv2kOVK6zm/dRbEfiFL1nV8V1qWEydOKC4uTh9++GG27fj06dOXHbcgpne55zAwMNCxfVqDi6Qc9z852bNnj3bu3HnVr92smqz7jHPnzmXbRrNqygp1Vjn1rV69ulJTUx3XsF3rPjWnbaRUqVL65Zdfch0HxRMhCW4nMDBQ4eHh2r59e77Gu5Yfnrv0BgVZPD09c2w3l1zYnpPmzZsrISFBS5Ys0fLly/Xvf/9br7/+umbPnu30Dac7yG05rTIzMxUaGqr3338/x+G5vXHmdz65rcf8rqNrWXeXyrqhwIIFC1S+fPlswy8NI3a7XR4exetAfUE9n5mZmWrbtq1GjRqV4/Dq1asX+Hyt38hn+b//+z/17dtXXbp00ciRIxUaGipPT09NmjTJ6eYWVyOv+6K8LFth70fcZdu+0nOR3zqLYr9wqR49euiHH37QyJEjVb9+ffn7+yszM1Pt2rVzuilJYU2vMJdN+mcd1KlTR6+99lqOwyMiIoq0nsJyvdaNgkdIglvq2LGj5s6dqx9//FG33377ZftGRkYqMzNTe/bscXwLLklHjhzRqVOnHHcFk/75NujUqVNO46enp+vQoUNXXevlPhCVLl1a/fr1U79+/ZSSkqLmzZtr3Lhxl/1wExkZqd27d2dr37Vrl2N4QdaYV1WqVNH//vc/NWnSJMcPnQUl69vPS9fTtRwRvJI9e/aoZcuWjscpKSk6dOiQOnToIEmOb15DQ0PVpk2bAptvSEiIfH19c13fHh4e2T545EWVKlX0008/6cKFC06nWllFRkbqf//7n86cOeN0NOlqt7OQkBAFBgZe8cuNKlWqKCUlpUCfx6vZvhctWqTKlSvr008/dRp/7NixVz3t/OyL8uNq9iNZXLlt52V7yKvCqjM/896+fXuu8z558qRWrFihuLg4vfjii472qz1NsqCnJ/3/7TPrSHOWnPY/OalSpYq2bdum1q1bF8h7SkhIiHx8fHJcptxqyqnv77//Ll9fX8cXdYWxT71UQSw/3F/x+qoTxcaoUaPk5+en/v3768iRI9mGJyQkOG69mvVmP336dKc+Wd923X333Y62KlWqZLvmYe7cubkepcgLPz+/bB/oJWW7Tau/v7+qVq2a7Vapl+rQoYM2bNigH3/80dF29uxZzZ07V1FRUapVq1a+a/T19ZWUPXjkR48ePZSRkaGXXnop27CLFy9e07StIiMj5enpmW09zZo1q0Cmn5O5c+c6nW4VHx+vixcvqn379pKk2NhYBQYGauLEiU79suR0q+K88PT01F133aUlS5Y4Tn+S/vlQvXDhQjVt2jTbaUR5cd999+n48eN66623sg3L+ja0Q4cOysjIyNbn9ddfl81mcyx7Xnl4eKhLly764osvtGnTplzn26NHD/34449atmxZtj6nTp3SxYsX8zVfSY7f6cnPNpj1bbH12+GffvrJ6XUn5e+1k599UV5d7X4ki6u27bxuD3lVWHXmRcOGDVWpUiVNnz4923aQtRw5bU9S9m0hrwp6epIc6/zNN9+8qmn26NFDf/31l95+++1sw86dO6ezZ8/mqx5PT0/FxsZq8eLFSkpKcrTv3Lkzx/2DJP34449O12MdOHBAS5Ys0V133SVPT89C26de6mr2Obj+cCQJbqlKlSpauHCh7r//ftWsWVO9e/dW7dq1lZ6erh9++EGffPKJ43eN6tWrpz59+mju3Lk6deqUWrRooQ0bNmj+/Pnq0qWL07eo/fv31xNPPKH77rtPbdu21bZt27Rs2TKVLVv2qmuNiYlRfHy8Xn75ZVWtWlWhoaFq1aqVatWqpTvvvFMxMTEqXbq0Nm3apEWLFumpp5667PRGjx6tDz74QO3bt9fgwYNVunRpzZ8/X4mJifrvf/97Vae6+Pj4qFatWvroo49UvXp1lS5dWrVr187XtU0tWrTQ448/rkmTJmnr1q266667VLJkSe3Zs0effPKJ3njjDXXr1i3ftV0qKChI3bt314wZM2Sz2VSlShV9+eWXebrm6Wqlp6erdevW6tGjh3bv3q1Zs2apadOmuueeeyT9cwpofHy8Hn74YTVs2FAPPPCAQkJClJSUpK+++kpNmjTJMZDkxcsvv6xvv/1WTZs21cCBA1WiRAnNmTNHaWlpTr/NkR+9e/fWe++9p2HDhmnDhg1q1qyZzp49q//9738aOHCgOnfurE6dOqlly5Z6/vnntW/fPtWrV0/Lly/XkiVLNGTIkGzXLeTFxIkTtXz5crVo0UIDBgxQzZo1dejQIX3yySdau3atgoODNXLkSH3++efq2LGj+vbtq5iYGJ09e1a//vqrFi1apH379uX79Zh1IffgwYMVGxsrT09PPfDAA5cdp2PHjvr000/VtWtX3X333UpMTNTs2bNVq1YtpaSkOPrl57WTn31RXl3tfiSLK7ftvGwPeVWYdV6Jh4eH4uPj1alTJ9WvX1/9+vVTWFiYdu3apR07dmjZsmUKDAxU8+bNNXXqVF24cEE33XSTli9frsTExKuaZ0FPT5Lq16+vnj17atasWTp9+rTuuOMOrVixQnv37s3T+A8//LA+/vhjPfHEE1q5cqWaNGmijIwM7dq1Sx9//LGWLVuW4006LicuLk5Lly5Vs2bNNHDgQF28eFEzZsxQdHR0jtcA1a5dW7GxsRo8eLDsdrvjy7O4uDhHn8LYp16qSpUqCg4O1uzZsxUQECA/Pz/deuutV309I9xUUd9OD8iP33//3Tz22GMmKirKeHl5mYCAANOkSRMzY8YMc/78eUe/CxcumLi4OFOpUiVTsmRJExERYcaMGePUx5h/fufh2WefNWXLljW+vr4mNjbW7N27N9dbgF9669qcbhl6+PBhc/fdd5uAgAAjyXE78Jdfftk0btzYBAcHGx8fH3PzzTebCRMmXPH3hIz553eQunXrZoKDg423t7dp3Lix+fLLL7P1Ux5vAW7MP78nExMTY7y8vJxu99qnTx/j5+eXrX9OvwFjjDFz5841MTExxsfHxwQEBJg6deqYUaNGmYMHD152/vmZz7Fjx8x9991nfH19TalSpczjjz9utm/fnuMtwHOaZosWLUx0dHS29sjISKfbKWet59WrV5sBAwaYUqVKGX9/f9OrVy/z999/Zxt/5cqVJjY21gQFBRlvb29TpUoV07dvX6db0uZW0+Vs2bLFxMbGGn9/f+Pr62tatmxpfvjhB6c++bkFuDH/3D74+eefd7wmypcvb7p16+Z0W9wzZ86YoUOHmvDwcFOyZElTrVo188orrzjd1tiY3LezS183xvxzS97evXubkJAQY7fbTeXKlc2gQYOcbrd95swZM2bMGFO1alXj5eVlypYta+644w4zbdo0x+vjcstr3X6N+ecWzU8//bQJCQkxNpvNsT1dbhqZmZlm4sSJJjIy0tjtdtOgQQPz5Zdfmj59+pjIyEinvrm9dnLadvO6L7p0W8zSokULp58UuNr9iLts21faHvKzr73WOvO6X8ht3mvXrjVt27Y1AQEBxs/Pz9StW9fpdtZ//vmn6dq1qwkODjZBQUGme/fu5uDBg7n+9MCVbgGe1+llbYfHjh1zGj+n+Zw7d84MHjzYlClTxvj5+ZlOnTqZAwcO5OkW4MYYk56ebqZMmWKio6ON3W43pUqVMjExMSYuLs6cPn3a0S8/+4zVq1c7Xl+VK1c2s2fPzvG1lTXN//u//zPVqlVzvG4vXU/G5G2fmtstwHPaRnLaLyxZssTUqlXLlChRgtuBF1M2Y7gSDQCA4mTevHnq16+fNm7cmO9v9wF3ZLPZNGjQoEI7YghcimuSAAAAAMCCkAQAAAAAFoQkAAAAALDgmiQAAAAAsOBIEgAAAABYEJIAAAAAwKLY/5hsZmamDh48qICAANlsNleXAwAAAMBFjDE6c+aMwsPD5eGR+/GiYh+SDh48qIiICFeXAQAAAMBNHDhwQBUqVMh1eLEPSQEBAZL+eSICAwNdXA0AAAAAV0lOTlZERIQjI+Sm2IekrFPsAgMDCUkAAAAArngZDjduAAAAAAALQhIAAAAAWBCSAAAAAMCi2F+TlBfGGF28eFEZGRmuLgWX8PT0VIkSJbh9OwAAAIrMDR+S0tPTdejQIaWmprq6FOTC19dXYWFh8vLycnUpAAAAuAHc0CEpMzNTiYmJ8vT0VHh4uLy8vDhi4UaMMUpPT9exY8eUmJioatWqXfZHvwAAAICCcEOHpPT0dGVmZioiIkK+vr6uLgc58PHxUcmSJbV//36lp6fL29vb1SUBAACgmONreYmjE26O9QMAAICixKdPAAAAALAgJAEAAACAxQ19TVJujp4+p9Op6UU2vyBfL4UG+RTZ/AAAAADkzqUhac2aNXrllVe0efNmHTp0SJ999pm6dOniGG6M0dixY/X222/r1KlTatKkieLj41WtWrVCq+no6XN6ZOYqXcjILLR5XKqkp4f+M+jOfAWlvn376tSpU1q8eLGjbdGiRXrooYc0YcIEDR8+vBAqBQAAAIo/l55ud/bsWdWrV08zZ87McfjUqVP15ptvavbs2frpp5/k5+en2NhYnT9/vtBqOp2aXqQBSZIuZGRe85Grf//73+rVq5fi4+MJSAAAAMA1cGlIat++vV5++WV17do12zBjjKZPn65//etf6ty5s+rWrav33ntPBw8edDp6gn/C5NNPP60PP/xQ/fr1kyQtWbJEDRs2lLe3typXrqy4uDhdvHjRMc6pU6fUv39/hYSEKDAwUK1atdK2bdscw8eNG6f69etrzpw5jluk9+jRQ6dPny7y5QMAAACKkttek5SYmKjDhw+rTZs2jragoCDdeuut+vHHH/XAAw/kOF5aWprS0tIcj5OTkwu9Vld69tlnNWvWLH355Zdq3bq1JOn7779X79699eabb6pZs2ZKSEjQgAEDJEljx46VJHXv3l0+Pj765ptvFBQUpDlz5qh169b6/fffVbp0aUnS3r179fHHH+uLL75QcnKyHn30UQ0cOFDvv/++axYWAIqxpKQkHT9+3NVlAEChKFu2rCpWrOjqMvLMbUPS4cOHJUnlypVzai9XrpxjWE4mTZqkuLi4Qq3NXXzzzTdasmSJVqxYoVatWjna4+LiNHr0aPXp00eSVLlyZb300ksaNWqUxo4dq7Vr12rDhg06evSo7Ha7JGnatGlavHixFi1a5AhU58+f13vvvaebbrpJkjRjxgzdfffdevXVV1W+fPkiXloAKL6SkpJU4+aaOn8u1dWlAECh8Pbx1e5dO6+boOS2IelqjRkzRsOGDXM8Tk5OVkREhAsrKjx169bV8ePHNXbsWDVu3Fj+/v6SpG3btmndunWaMGGCo29GRobOnz+v1NRUbdu2TSkpKSpTpozT9M6dO6eEhATH44oVKzoCkiTdfvvtyszM1O7duwlJAFCAjh8/rvPnUlXnvuHyCyme71kAblxnjx3Qr/99VcePHyckXausD+FHjhxRWFiYo/3IkSOqX79+ruPZ7XbH0ZHi7qabbtKiRYvUsmVLtWvXTt98840CAgKUkpKiuLg43XvvvdnG8fb2VkpKisLCwrRq1apsw4ODgwu/cABAjvxCIhQYXtXVZQDADc9tQ1KlSpVUvnx5rVixwhGKkpOT9dNPP+nJJ590bXFuJDIyUqtXr3YEpaVLl6phw4bavXu3qlbN+Y22YcOGOnz4sEqUKKGoqKhcp52UlKSDBw8qPDxckrR+/Xp5eHioRo0ahbEoAAAAgFtwaUhKSUnR3r17HY8TExO1detWlS5dWhUrVtSQIUP08ssvq1q1aqpUqZJeeOEFhYeHO/2WEqSIiAitWrVKLVu2VGxsrJ599ll169ZNFStWVLdu3eTh4aFt27Zp+/btevnll9WmTRvdfvvt6tKli6ZOnarq1avr4MGD+uqrr9S1a1c1atRI0j9Hnfr06aNp06YpOTlZgwcPVo8ePTjVDgAAAMWaS0PSpk2b1LJlS8fjrGuJ+vTpo3nz5mnUqFE6e/asBgwYoFOnTqlp06ZaunSpvL29C62mIF8vlfT0KPIfkw3y9bqmaVSoUMERlCZPnqxFixZp6tSpmjJlikqWLKmbb75Z/fv3lyTZbDZ9/fXXev7559WvXz8dO3ZM5cuXV/PmzZ1ulFG1alXde++96tChg06cOKGOHTtq1qxZ11QnAAAA4O5sxhjj6iIKU3JysoKCgnT69GkFBgY6DTt//rwSExNVqVIlp+B19PS5a/5x1/wI8vVSaJBPkc0vL8aNG6fFixdr69atri4l1/UEAMXFli1bFBMTo9uemM41SQCKneSDe7V+9hBt3rxZDRs2dG0tl8kGVm57TZIrhQb5uF1oAQAAAFA0PFxdAAAAAAC4E0IScjRu3Di3ONUOAAAAKGqEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALficpB6kHDyr9xIkim59X6dLyDQ8vsvkBAAAAyB0h6RKpBw9qZZs2ykxLK7J5etjtavm//xGUAAAAADfA6XaXSD9xokgDkiRlpqXl+8hV3759ZbPZZLPZVLJkSVWqVEmjRo3S+fPnC6lKAAAA4MbAkaTrWLt27fTuu+/qwoUL2rx5s/r06SObzaYpU6a4ujQAAADgusWRpOuY3W5X+fLlFRERoS5duqhNmzb69ttvJUmZmZmaNGmSKlWqJB8fH9WrV0+LFi1yjLtq1SrZbDYtW7ZMDRo0kI+Pj1q1aqWjR4/qm2++Uc2aNRUYGKgHH3xQqampjvHS0tI0ePBghYaGytvbW02bNtXGjRsd86xQoYLi4+Od6vz555/l4eGh/fv3S5JOnTql/v37KyQkRIGBgWrVqpW2bdtW2E8XAAAAkCeEpGJi+/bt+uGHH+Tl5SVJmjRpkt577z3Nnj1bO3bs0NChQ/XQQw9p9erVTuONGzdOb731ln744QcdOHBAPXr00PTp07Vw4UJ99dVXWr58uWbMmOHoP2rUKP33v//V/PnztWXLFlWtWlWxsbE6ceKEPDw81LNnTy1cuNBpHu+//76aNGmiyMhISVL37t0dYWzz5s1q2LChWrdurRNFeLMMAAAAIDeEpOvYl19+KX9/f3l7e6tOnTo6evSoRo4cqbS0NE2cOFH/+c9/FBsbq8qVK6tv37566KGHNGfOHKdpvPzyy2rSpIkaNGigRx99VKtXr1Z8fLwaNGigZs2aqVu3blq5cqUk6ezZs4qPj9crr7yi9u3bq1atWnr77bfl4+Ojd955R5LUq1cvrVu3TklJSZL+Obr04YcfqlevXpKktWvXasOGDfrkk0/UqFEjVatWTdOmTVNwcLDTkS4AAADAVbgm6TrWsmVLxcfH6+zZs3r99ddVokQJ3XfffdqxY4dSU1PVtm1bp/7p6elq0KCBU1vdunUd/5crV06+vr6qXLmyU9uGDRskSQkJCbpw4YKaNGniGF6yZEk1btxYO3fulCTVr19fNWvW1MKFCzV69GitXr1aR48eVffu3SVJ27ZtU0pKisqUKeNUx7lz55SQkFAAzwoAAABwbQhJ1zE/Pz9VrVpVkvSf//xH9erV0zvvvKPatWtLkr766ivddNNNTuPY7XanxyVLlnT8n3WnPCubzabMzMx81dWrVy9HSFq4cKHatWvnCEUpKSkKCwvTqlWrso0XHBycr/kAAAAAhYGQVEx4eHjoueee07Bhw/T777/LbrcrKSlJLVq0KLB5VKlSRV5eXlq3bp3j+qILFy5o48aNGjJkiKPfgw8+qH/961/avHmzFi1apNmzZzuGNWzYUIcPH1aJEiUUFRVVYLUBAAAABYWQVIx0795dI0eO1Jw5czRixAgNHTpUmZmZatq0qU6fPq1169YpMDBQffr0uarp+/n56cknn9TIkSNVunRpVaxYUVOnTlVqaqoeffRRR7+oqCjdcccdevTRR5WRkaF77rnHMaxNmza6/fbb1aVLF02dOlXVq1fXwYMH9dVXX6lr165q1KjRNT8PAAAAwLUgJF3Cq3RpedjtRfqDsh52u7xKl77m6ZQoUUJPPfWUpk6dqsTERIWEhGjSpEn6448/FBwcrIYNG+q55567pnlMnjxZmZmZevjhh3XmzBk1atRIy5YtU6lSpZz69erVSwMHDlTv3r3l4+PjaLfZbPr666/1/PPPq1+/fjp27JjKly+v5s2bq1y5ctdUGwAAAFAQbMYY4+oiClNycrKCgoJ0+vRpBQYGOg07f/68EhMTValSJXl7ezvaUw8eVHoR3o7aq3Rp+YaHF9n8rje5rScAKC62bNmimJgY3fbEdAWGV3V1OQBQoJIP7tX62UMcP/3i0loukw2sOJKUA9/wcEILAAAAcIPid5IAAAAAwIKQBAAAAAAWhCQAAAAAsCAkSSrm96647rF+AAAAUJRu6JBUsmRJSVJqaqqLK8HlZK2frPUFAAAAFKYb+u52np6eCg4O1tGjRyVJvr6+stlsLq4KWYwxSk1N1dGjRxUcHCxPT09XlwQAAIAbwA0dkiSpfPnykuQISnA/wcHBjvUEAAAAFLYbPiTZbDaFhYUpNDRUFy5ccHU5uETJkiU5ggQAAIAidcOHpCyenp58GAcAAABwY9+4AQAAAAAuRUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAICFW4ekjIwMvfDCC6pUqZJ8fHxUpUoVvfTSSzLGuLo0AAAAAMVUCVcXcDlTpkxRfHy85s+fr+joaG3atEn9+vVTUFCQBg8e7OryAAAAABRDbh2SfvjhB3Xu3Fl33323JCkqKkoffPCBNmzY4OLKAAAAABRXbn263R133KEVK1bo999/lyRt27ZNa9euVfv27XMdJy0tTcnJyU5/AAAAAJBXbn0kafTo0UpOTtbNN98sT09PZWRkaMKECerVq1eu40yaNElxcXFFWCUAAACA4sStjyR9/PHHev/997Vw4UJt2bJF8+fP17Rp0zR//vxcxxkzZoxOnz7t+Dtw4EARVgwAAADgeufWR5JGjhyp0aNH64EHHpAk1alTR/v379ekSZPUp0+fHMex2+2y2+1FWSYAAACAYsStjySlpqbKw8O5RE9PT2VmZrqoIgAAAADFnVsfSerUqZMmTJigihUrKjo6Wj///LNee+01PfLII64uDQAAAEAx5dYhacaMGXrhhRc0cOBAHT16VOHh4Xr88cf14osvuro0AAAAAMWUW4ekgIAATZ8+XdOnT3d1KQAAAABuEG59TRIAAAAAFDVCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAs3D4k/fXXX3rooYdUpkwZ+fj4qE6dOtq0aZOrywIAAABQTJVwdQGXc/LkSTVp0kQtW7bUN998o5CQEO3Zs0elSpVydWkAAAAAiim3DklTpkxRRESE3n33XUdbpUqVXFgRAAAAgOLOrU+3+/zzz9WoUSN1795doaGhatCggd5+++3LjpOWlqbk5GSnPwAAAADIK7cOSX/88Yfi4+NVrVo1LVu2TE8++aQGDx6s+fPn5zrOpEmTFBQU5PiLiIgowooBAAAAXO/cOiRlZmaqYcOGmjhxoho0aKABAwboscce0+zZs3MdZ8yYMTp9+rTj78CBA0VYMQAAAIDrnVuHpLCwMNWqVcuprWbNmkpKSsp1HLvdrsDAQKc/AAAAAMgrtw5JTZo00e7du53afv/9d0VGRrqoIgAAAADFnVuHpKFDh2r9+vWaOHGi9u7dq4ULF2ru3LkaNGiQq0sDAAAAUEy5dUi65ZZb9Nlnn+mDDz5Q7dq19dJLL2n69Onq1auXq0sDAAAAUEy59e8kSVLHjh3VsWNHV5cBAAAA4Abh1keSAAAAAKCoEZIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALPJ0C/DPP/88zxO85557rroYAAAAAHC1PIWkLl265GliNptNGRkZ11IPAAAAALhUnkJSZmZmYdcBAAAAAG6Ba5IAAAAAwCJPR5IudfbsWa1evVpJSUlKT093GjZ48OACKQwAAAAAXCHfIennn39Whw4dlJqaqrNnz6p06dI6fvy4fH19FRoaSkgCAAAAcF3L9+l2Q4cOVadOnXTy5En5+Pho/fr12r9/v2JiYjRt2rTCqBEAAAAAiky+Q9LWrVs1fPhweXh4yNPTU2lpaYqIiNDUqVP13HPPFUaNAAAAAFBk8h2SSpYsKQ+Pf0YLDQ1VUlKSJCkoKEgHDhwo2OoAAAAAoIjl+5qkBg0aaOPGjapWrZpatGihF198UcePH9eCBQtUu3btwqgRAAAAAIpMvo8kTZw4UWFhYZKkCRMmqFSpUnryySd17NgxzZkzp8ALBAAAAICilO8jSY0aNXL8HxoaqqVLlxZoQQAAAADgSvk+ktSqVSudOnUqW3tycrJatWpVEDUBAAAAgMvkOyStWrUq2w/IStL58+f1/fffF0hRAAAAAOAqeT7d7pdffnH8/9tvv+nw4cOOxxkZGVq6dKluuummgq0OAAAAAIpYnkNS/fr1ZbPZZLPZcjytzsfHRzNmzCjQ4gAAAACgqOU5JCUmJsoYo8qVK2vDhg0KCQlxDPPy8lJoaKg8PT0LpUgAAAAAKCp5DkmRkZGSpMzMzEIrBgAAAABcLd+3AJekhIQETZ8+XTt37pQk1apVS88884yqVKlSoMUBAAAAQFHL993tli1bplq1amnDhg2qW7eu6tatq59++knR0dH69ttvC6NGAAAAACgy+T6SNHr0aA0dOlSTJ0/O1v7ss8+qbdu2BVYcAAAAABS1fB9J2rlzpx599NFs7Y888oh+++23AikKAAAAAFwl3yEpJCREW7duzda+detWhYaGFkRNAAAAAOAyeT7dbvz48RoxYoQee+wxDRgwQH/88YfuuOMOSdK6des0ZcoUDRs2rNAKBQAAAICikOeQFBcXpyeeeEIvvPCCAgIC9Oqrr2rMmDGSpPDwcI0bN06DBw8utEIBAAAAoCjkOSQZYyRJNptNQ4cO1dChQ3XmzBlJUkBAQOFUBwAAAABFLF93t7PZbE6PCUcAAAAAipt8haTq1atnC0qXOnHixDUVBAAAAACulK+QFBcXp6CgoMKqBQAAAABcLl8h6YEHHuA23wAAAACKtTz/TtKVTrMDAAAAgOIgzyEp6+52AAAAAFCc5fl0u8zMzMKsAwAAAADcQp6PJAEAAADAjYCQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAIvrKiRNnjxZNptNQ4YMcXUpAAAAAIqp6yYkbdy4UXPmzFHdunVdXQoAAACAYuy6CEkpKSnq1auX3n77bZUqVcrV5QAAAAAoxq6LkDRo0CDdfffdatOmzRX7pqWlKTk52ekPAAAAAPKqhKsLuJIPP/xQW7Zs0caNG/PUf9KkSYqLiyvkqgAAAAAUV259JOnAgQN65pln9P7778vb2ztP44wZM0anT592/B04cKCQqwQAAABQnLj1kaTNmzfr6NGjatiwoaMtIyNDa9as0VtvvaW0tDR5eno6jWO322W324u6VAAAAADFhFuHpNatW+vXX391auvXr59uvvlmPfvss9kCEgAAAABcK7cOSQEBAapdu7ZTm5+fn8qUKZOtHQAAAAAKgltfkwQAAAAARc2tjyTlZNWqVa4uAQAAAEAxxpEkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFi4dUiaNGmSbrnlFgUEBCg0NFRdunTR7t27XV0WAAAAgGLMrUPS6tWrNWjQIK1fv17ffvutLly4oLvuuktnz551dWkAAAAAiqkSri7gcpYuXer0eN68eQoNDdXmzZvVvHlzF1UFAAAAoDhz65B0qdOnT0uSSpcunWuftLQ0paWlOR4nJycXel0AAAAAig+3Pt3OKjMzU0OGDFGTJk1Uu3btXPtNmjRJQUFBjr+IiIgirBIAAADA9e66CUmDBg3S9u3b9eGHH16235gxY3T69GnH34EDB4qoQgAAAADFwXVxut1TTz2lL7/8UmvWrFGFChUu29dut8tutxdRZQAAAACKG7cOScYYPf300/rss8+0atUqVapUydUlAQAAACjm3DokDRo0SAsXLtSSJUsUEBCgw4cPS5KCgoLk4+Pj4uoAAAAAFEdufU1SfHy8Tp8+rTvvvFNhYWGOv48++sjVpQEAAAAoptz6SJIxxtUlAAAAALjBuPWRJAAAAAAoaoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgUcLVBdxokpKSdPz4cVeXAQCFomzZsqpYsaKrywAA4JoQkopQUlKSataoodTz511dCgAUCl9vb+3cvZugBAC4rhGSitDx48eVev68hoWEKMLLy9XlAECBOpCerteOHdPx48cJSQCA6xohyQUivLxUxW53dRkAAAAAcsCNGwAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYHFdhKSZM2cqKipK3t7euvXWW7VhwwZXlwQAAACgmHL7kPTRRx9p2LBhGjt2rLZs2aJ69eopNjZWR48edXVpAAAAAIohtw9Jr732mh577DH169dPtWrV0uzZs+Xr66v//Oc/ri4NAAAAQDFUwtUFXE56ero2b96sMWPGONo8PDzUpk0b/fjjjzmOk5aWprS0NMfj06dPS5KSk5MLt9g8SElJkSTtPX9e5zMzXVwNABSsP9PTJf2zr3OHfe71JOv9IfngXl1MP+/iagCgYKUe/1OSe7w/ZM3fGHPZfm4dko4fP66MjAyVK1fOqb1cuXLatWtXjuNMmjRJcXFx2dojIiIKpcarMfPvv11dAgAUmhYtWri6hOvWb5+/5eoSAKDQuNP7w5kzZxQUFJTrcLcOSVdjzJgxGjZsmONxZmamTpw4oTJlyshms7mwMqDoJScnKyIiQgcOHFBgYKCrywEAuAHeG3AjM8bozJkzCg8Pv2w/tw5JZcuWlaenp44cOeLUfuTIEZUvXz7Hcex2u+x2u1NbcHBwYZUIXBcCAwN5IwQAOOG9ATeqyx1ByuLWN27w8vJSTEyMVqxY4WjLzMzUihUrdPvtt7uwMgAAAADFlVsfSZKkYcOGqU+fPmrUqJEaN26s6dOn6+zZs+rXr5+rSwMAAABQDLl9SLr//vt17Ngxvfjiizp8+LDq16+vpUuXZruZA4Ds7Ha7xo4dm+0UVADAjYv3BuDKbOZK978DAAAAgBuIW1+TBAAAAABFjZAEAAAAABaEJAAAAACwICQBAAAAgAUhCSiG1qxZo06dOik8PFw2m02LFy92dUkAABcbN26cbDab09/NN9/s6rIAt0RIAoqhs2fPql69epo5c6arSwEAuJHo6GgdOnTI8bd27VpXlwS4Jbf/nSQA+de+fXu1b9/e1WUAANxMiRIlVL58eVeXAbg9jiQBAADcIPbs2aPw8HBVrlxZvXr1UlJSkqtLAtwSIQkAAOAGcOutt2revHlaunSp4uPjlZiYqGbNmunMmTOuLg1wO5xuBwAAcAOwnoZdt25d3XrrrYqMjNTHH3+sRx991IWVAe6HI0kAAAA3oODgYFWvXl179+51dSmA2yEkAQAA3IBSUlKUkJCgsLAwV5cCuB1OtwOKoZSUFKdvBhMTE7V161aVLl1aFStWdGFlAABXGTFihDp16qTIyEgdPHhQY8eOlaenp3r27Onq0gC3Q0gCiqFNmzapZcuWjsfDhg2TJPXp00fz5s1zUVUAAFf6888/1bNnT/39998KCQlR06ZNtX79eoWEhLi6NMDt2IwxxtVFAAAAAIC74JokAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAuMHt27dPNptNW7dudXUpDrt27dJtt90mb29v1a9f39XlFBvjxo3j+QSAPCAkAYCL9e3bVzabTZMnT3ZqX7x4sWw2m4uqcq2xY8fKz89Pu3fv1ooVK3Ltd/jwYT399NOqXLmy7Ha7IiIi1KlTp8uO466ioqI0ffr0ApuezWbT4sWLndpGjBhxXT43AFDUCEkA4Aa8vb01ZcoUnTx50tWlFJj09PSrHjchIUFNmzZVZGSkypQpk2Offfv2KSYmRt99951eeeUV/frrr1q6dKlatmypQYMGXfW83VlGRoYyMzOvenx/f/9cn08AwP9HSAIAN9CmTRuVL19ekyZNyrVPTqdKTZ8+XVFRUY7Hffv2VZcuXTRx4kSVK1dOwcHBGj9+vC5evKiRI0eqdOnSqlChgt59991s09+1a5fuuOMOeXt7q3bt2lq9erXT8O3bt6t9+/by9/dXuXLl9PDDD+v48eOO4XfeeaeeeuopDRkyRGXLllVsbGyOy5GZmanx48erQoUKstvtql+/vpYuXeoYbrPZtHnzZo0fP142m03jxo3LcToDBw6UzWbThg0bdN9996l69eqKjo7WsGHDtH79eke/pKQkde7cWf7+/goMDFSPHj105MiRbM/rggULFBUVpaCgID3wwAM6c+aMU81Tp05V1apVZbfbVbFiRU2YMMEx/MCBA+rRo4eCg4NVunRpde7cWfv27cu2XqZNm6awsDCVKVNGgwYN0oULFxzP3f79+zV06FDZbDbHEcR58+YpODhYn3/+uWrVqiW73a6kpCRt3LhRbdu2VdmyZRUUFKQWLVpoy5YtjvllbRNdu3aVzWZzPL50G7rSusg6FfPTTz9Vy5Yt5evrq3r16unHH3909Nm/f786deqkUqVKyc/PT9HR0fr6669zXGcAcL0gJAGAG/D09NTEiRM1Y8YM/fnnn9c0re+++04HDx7UmjVr9Nprr2ns2LHq2LGjSpUqpZ9++klPPPGEHn/88WzzGTlypIYPH66ff/5Zt99+uzp16qS///5bknTq1Cm1atVKDRo00KZNm7R06VIdOXJEPXr0cJrG/Pnz5eXlpXXr1mn27Nk51vfGG2/o1Vdf1bRp0/TLL78oNjZW99xzj/bs2SNJOnTokKKjozV8+HAdOnRII0aMyDaNEydOaOnSpRo0aJD8/PyyDQ8ODpb0Twjo3LmzTpw4odWrV+vbb7/VH3/8ofvvv9+pf0JCghYvXqwvv/xSX375pVavXu10+uOYMWM0efJkvfDCC/rtt9+0cOFClStXTpJ04cIFxcbGKiAgQN9//73WrVsnf39/tWvXzulo2sqVK5WQkKCVK1dq/vz5mjdvnubNmydJ+vTTT1WhQgWNHz9ehw4d0qFDhxzjpaamasqUKfr3v/+tHTt2KDQ0VGfOnFGfPn20du1arV+/XtWqVVOHDh0cwW7jxo2SpHfffVeHDh1yPM7vusjy/PPPa8SIEdq6dauqV6+unj176uLFi5KkQYMGKS0tTWvWrNGvv/6qKVOmyN/fP8f5AcB1wwAAXKpPnz6mc+fOxhhjbrvtNvPII48YY4z57LPPjHU3PXbsWFOvXj2ncV9//XUTGRnpNK3IyEiTkZHhaKtRo4Zp1qyZ4/HFixeNn5+f+eCDD4wxxiQmJhpJZvLkyY4+Fy5cMBUqVDBTpkwxxhjz0ksvmbvuustp3gcOHDCSzO7du40xxrRo0cI0aNDgissbHh5uJkyY4NR2yy23mIEDBzoe16tXz4wdOzbXafz0009Gkvn0008vO6/ly5cbT09Pk5SU5GjbsWOHkWQ2bNhgjPnnefX19TXJycmOPiNHjjS33nqrMcaY5ORkY7fbzdtvv53jPBYsWGBq1KhhMjMzHW1paWnGx8fHLFu2zBjz/9fLxYsXHX26d+9u7r//fsfjyMhI8/rrrztN+9133zWSzNatWy+7nBkZGSYgIMB88cUXjjZJ5rPPPnPqd+k2dKV1kbVt/Pvf/3YMz3r+du7caYwxpk6dOmbcuHGXrQ8ArjccSQIANzJlyhTNnz9fO3fuvOppREdHy8Pj/+/ey5Urpzp16jgee3p6qkyZMjp69KjTeLfffrvj/xIlSqhRo0aOOrZt26aVK1fK39/f8XfzzTdL+ucoTJaYmJjL1pacnKyDBw+qSZMmTu1NmjTJ1zIbY/LUb+fOnYqIiFBERISjrVatWgoODnaaX1RUlAICAhyPw8LCHM/Pzp07lZaWptatW+c4j23btmnv3r0KCAhwPDelS5fW+fPnnZ6b6OhoeXp65jiPy/Hy8lLdunWd2o4cOaLHHntM1apVU1BQkAIDA5WSkqKkpKQrTi9LftaFdf5hYWGS5Kh98ODBevnll9WkSRONHTtWv/zyS55rAAB3VcLVBQAA/r/mzZsrNjZWY8aMUd++fZ2GeXh4ZAsHWde0WJUsWdLpsc1my7EtPzcASElJUadOnTRlypRsw7I+NEvK8dS3wlCtWjXZbDbt2rWrQKZ3uefHx8fnsuOmpKQoJiZG77//frZhISEheZrH5fj4+GS7y2GfPn30999/64033lBkZKTsdrtuv/32a7pZxuVYa8+qJav2/v37KzY2Vl999ZWWL1+uSZMm6dVXX9XTTz9dKLUAQFHgSBIAuJnJkyfriy++cLo4XvrnA/fhw4edglJB/raR9WYHFy9e1ObNm1WzZk1JUsOGDbVjxw5FRUWpatWqTn/5CUaBgYEKDw/XunXrnNrXrVunWrVq5Xk6pUuXVmxsrGbOnKmzZ89mG37q1ClJUs2aNXXgwAEdOHDAMey3337TqVOn8jy/atWqycfHJ9dbZzds2FB79uxRaGhotucmKCgoz8vk5eWljIyMPPVdt26dBg8erA4dOig6Olp2u93pJhrSP8HmctMrqHUhSREREXriiSf06aefavjw4Xr77bfzNT4AuBtCEgC4mTp16qhXr1568803ndrvvPNOHTt2TFOnTlVCQoJmzpypb775psDmO3PmTH322WfatWuXBg0apJMnT+qRRx6R9M/F+SdOnFDPnj21ceNGJSQkaNmyZerXr1+eP9hnGTlypKZMmaKPPvpIu3fv1ujRo7V161Y988wz+a43IyNDjRs31n//+1/t2bNHO3fu1Jtvvuk4dbBNmzaO53PLli3asGGDevfurRYtWqhRo0Z5mo+3t7eeffZZjRo1Su+9954SEhK0fv16vfPOO5KkXr16qWzZsurcubO+//57JSYmatWqVRo8eHC+bsIRFRWlNWvW6K+//soWeC5VrVo1LViwQDt37tRPP/2kXr16ZTviFRUVpRUrVujw4cO53lq+INbFkCFDtGzZMiUmJmrLli1auXKlI1wDwPWKkAQAbmj8+PHZTsWqWbOmZs2apZkzZ6pevXrasGFDjnd+u1qTJ0/W5MmTVa9ePa1du1aff/65ypYtK0mOIw4ZGRm66667VKdOHQ0ZMkTBwcFO1z/lxeDBgzVs2DANHz5cderU0dKlS/X555+rWrVq+ZpO5cqVtWXLFrVs2VLDhw9X7dq11bZtW61YsULx8fGS/jk1bMmSJSpVqpSaN2+uNm3aqHLlyvroo4/yNa8XXnhBw4cP14svvqiaNWvq/vvvd1yT4+vrqzVr1qhixYq69957VbNmTT366KM6f/68AgMD8zyP8ePHa9++fapSpYrTaXo5eeedd3Ty5Ek1bNhQDz/8sAYPHqzQ0FCnPq+++qq+/fZbRUREqEGDBjlOpyDWRUZGhgYNGqSaNWuqXbt2ql69umbNmpXn8QHAHdlMXq9+BQAAAIAbAEeSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsPh/4wCWeTyZM2sAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the percentage and build the diagnostic plot, but don't actually apply the filter. \n",
    "Long.filter_min_concentration(count = 3, apply = False, diagnostic_plot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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>NumConc</th>\n",
       "      <th>Count</th>\n",
       "      <th>Filter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>Keep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Remove</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NumConc  Count  Filter\n",
       "0        5     10    Keep\n",
       "1        1      1  Remove"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the resulting data.frame\n",
    "Long.filter_min_concentration_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the filter with defaults\n",
    "Long.filter_min_concentration(apply = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Score Filter\n",
    "\n",
    "To fit a curve, we expect the response to increase with the dose. The EPA recommends calculated a spearman correlation, and removing endpoints that fall below a correlation score of 0.2. This also saves time on fitting models, as these endpoints are not considred in the modeling step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# View filter \n",
    "Long.filter_correlation_score(score = 0.2, diagnostic_plot = True, apply = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "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>bmdrc.Endpoint.ID</th>\n",
       "      <th>Spearman</th>\n",
       "      <th>Filter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 ANY</td>\n",
       "      <td>0.666737</td>\n",
       "      <td>Keep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 ANY24</td>\n",
       "      <td>0.666737</td>\n",
       "      <td>Keep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1 DP24</td>\n",
       "      <td>0.971008</td>\n",
       "      <td>Keep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1 JAW</td>\n",
       "      <td>-0.142857</td>\n",
       "      <td>Remove</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1 SM24</td>\n",
       "      <td>-0.115954</td>\n",
       "      <td>Remove</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2 ANY</td>\n",
       "      <td>0.985611</td>\n",
       "      <td>Keep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2 ANY24</td>\n",
       "      <td>0.985611</td>\n",
       "      <td>Keep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2 DP24</td>\n",
       "      <td>0.942857</td>\n",
       "      <td>Keep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2 JAW</td>\n",
       "      <td>0.828571</td>\n",
       "      <td>Keep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2 SM24</td>\n",
       "      <td>0.753702</td>\n",
       "      <td>Keep</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  bmdrc.Endpoint.ID  Spearman  Filter\n",
       "0             1 ANY  0.666737    Keep\n",
       "1           1 ANY24  0.666737    Keep\n",
       "2            1 DP24  0.971008    Keep\n",
       "3             1 JAW -0.142857  Remove\n",
       "4            1 SM24 -0.115954  Remove\n",
       "5             2 ANY  0.985611    Keep\n",
       "6           2 ANY24  0.985611    Keep\n",
       "7            2 DP24  0.942857    Keep\n",
       "8             2 JAW  0.828571    Keep\n",
       "9            2 SM24  0.753702    Keep"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See a data.frame of spearman correlation values \n",
    "Long.filter_correlation_score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "Long.filter_correlation_score(score = 0.2, apply = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Module 4: Fit Models\n",
    "\n",
    "All 7 EPA recommended models, including one additional model (Quantal Linear) are fit to the curve. The best fit is then selected in this order:\n",
    "\n",
    "1. If the goodness-of-fit (GOF) is over the threshold (default of 0.1)\n",
    "\n",
    "2. The lowest Akaike Information Criterion (AIC) within 2\n",
    "\n",
    "3. If necessary, the lowest BMDL value \n",
    "\n",
    "The 7 recommended models are: Logistic, Log-Logistic, Probit, Log-Probit, Weibull, Gamma, and Multistage 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the model fits to the recommended GOF and AIC thresholds. Any models within the AIC threshold (within 2 of the lowest score, by default), are then decided by the model_selection method. \n",
    "# Currently, only \"lowest BMDL\" is supported. \n",
    "Long.fit_models(gof_threshold = 0.1, aic_threshold = 2, model_selection = \"lowest BMDL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>bmdrc.Endpoint.ID</th>\n",
       "      <th>Model</th>\n",
       "      <th>BMD10</th>\n",
       "      <th>BMDL</th>\n",
       "      <th>BMD50</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Min_Dose</th>\n",
       "      <th>Max_Dose</th>\n",
       "      <th>AUC_Norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 ANY</td>\n",
       "      <td>Log Probit</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.024599</td>\n",
       "      <td>19.025000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.792708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 ANY24</td>\n",
       "      <td>Log Probit</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.017991</td>\n",
       "      <td>19.125000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.796875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1 DP24</td>\n",
       "      <td>Weibull</td>\n",
       "      <td>1.798141</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.360780</td>\n",
       "      <td>10.475000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.436458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2 ANY</td>\n",
       "      <td>Quantal Linear</td>\n",
       "      <td>2.982072</td>\n",
       "      <td>2.042621</td>\n",
       "      <td>19.618498</td>\n",
       "      <td>87.083333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.870833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2 ANY24</td>\n",
       "      <td>Quantal Linear</td>\n",
       "      <td>2.982072</td>\n",
       "      <td>2.042621</td>\n",
       "      <td>19.618498</td>\n",
       "      <td>87.083333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.870833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2 DP24</td>\n",
       "      <td>Log Logistic</td>\n",
       "      <td>6.321510</td>\n",
       "      <td>0.797269</td>\n",
       "      <td>72.392449</td>\n",
       "      <td>45.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Log Probit</td>\n",
       "      <td>2.842871</td>\n",
       "      <td>2.390934</td>\n",
       "      <td>115.465467</td>\n",
       "      <td>66.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.662500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2 SM24</td>\n",
       "      <td>Gamma</td>\n",
       "      <td>57.192487</td>\n",
       "      <td>24.330544</td>\n",
       "      <td>78.809252</td>\n",
       "      <td>49.583333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.495833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  bmdrc.Endpoint.ID           Model      BMD10       BMDL       BMD50  \\\n",
       "0             1 ANY      Log Probit   0.000021        NaN    0.024599   \n",
       "1           1 ANY24      Log Probit   0.000011        NaN    0.017991   \n",
       "2            1 DP24         Weibull   1.798141        NaN   13.360780   \n",
       "3             2 ANY  Quantal Linear   2.982072   2.042621   19.618498   \n",
       "4           2 ANY24  Quantal Linear   2.982072   2.042621   19.618498   \n",
       "5            2 DP24    Log Logistic   6.321510   0.797269   72.392449   \n",
       "6             2 JAW      Log Probit   2.842871   2.390934  115.465467   \n",
       "7            2 SM24           Gamma  57.192487  24.330544   78.809252   \n",
       "\n",
       "         AUC  Min_Dose  Max_Dose  AUC_Norm  \n",
       "0  19.025000       0.0      24.0  0.792708  \n",
       "1  19.125000       0.0      24.0  0.796875  \n",
       "2  10.475000       0.0      24.0  0.436458  \n",
       "3  87.083333       0.0     100.0  0.870833  \n",
       "4  87.083333       0.0     100.0  0.870833  \n",
       "5  45.833333       0.0     100.0  0.458333  \n",
       "6  66.250000       0.0     100.0  0.662500  \n",
       "7  49.583333       0.0     100.0  0.495833  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See summary statistics as well as benchmark dose calculations \n",
    "Long.bmds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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>bmdrc.Endpoint.ID</th>\n",
       "      <th>Model</th>\n",
       "      <th>BMD10</th>\n",
       "      <th>BMDL</th>\n",
       "      <th>BMD50</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Min_Dose</th>\n",
       "      <th>Max_Dose</th>\n",
       "      <th>AUC_Norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 JAW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.452083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 NC24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1 SM24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.558333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  bmdrc.Endpoint.ID  Model  BMD10  BMDL  BMD50    AUC  Min_Dose  Max_Dose  \\\n",
       "0             1 JAW    NaN    NaN   NaN    NaN  10.85       0.0      24.0   \n",
       "1            1 NC24    NaN    NaN   NaN    NaN   0.25       0.0       1.5   \n",
       "2            1 SM24    NaN    NaN   NaN    NaN  13.40       0.0      24.0   \n",
       "\n",
       "   AUC_Norm  \n",
       "0  0.452083  \n",
       "1  0.166667  \n",
       "2  0.558333  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See summary statistics for filtered values \n",
    "Long.bmds_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a plot of curve fits\n",
    "Long.response_curve(chemical_name = 2, endpoint_name = \"JAW\", model = \"log probit\")\n",
    "Long._2_JAW_log_probit_curve_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "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>concentration</th>\n",
       "      <th>bmdrc.num.nonna</th>\n",
       "      <th>bmdrc.num.affected</th>\n",
       "      <th>bmdrc.frac.affected</th>\n",
       "      <th>Low</th>\n",
       "      <th>High</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>18</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.031020</td>\n",
       "      <td>0.327998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>18</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.090009</td>\n",
       "      <td>0.452146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.0</td>\n",
       "      <td>18</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.162788</td>\n",
       "      <td>0.562505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.0</td>\n",
       "      <td>18</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.245595</td>\n",
       "      <td>0.662836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80.0</td>\n",
       "      <td>18</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.437495</td>\n",
       "      <td>0.837212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100.0</td>\n",
       "      <td>18</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.203052</td>\n",
       "      <td>0.613810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   concentration  bmdrc.num.nonna  bmdrc.num.affected  bmdrc.frac.affected  \\\n",
       "0            0.0               18                 2.0             0.111111   \n",
       "1            5.0               18                 4.0             0.222222   \n",
       "2           20.0               18                 6.0             0.333333   \n",
       "3           40.0               18                 8.0             0.444444   \n",
       "4           80.0               18                12.0             0.666667   \n",
       "5          100.0               18                 7.0             0.388889   \n",
       "\n",
       "        Low      High  \n",
       "0  0.031020  0.327998  \n",
       "1  0.090009  0.452146  \n",
       "2  0.162788  0.562505  \n",
       "3  0.245595  0.662836  \n",
       "4  0.437495  0.837212  \n",
       "5  0.203052  0.613810  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the confidence intervals \n",
    "Long._2_JAW_log_probit_confidence_intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "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>Dose in uM</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.108849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.137739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.1111</td>\n",
       "      <td>0.157036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.6667</td>\n",
       "      <td>0.172374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2222</td>\n",
       "      <td>0.185353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dose in uM  Response\n",
       "0      0.0000  0.108849\n",
       "1      0.5556  0.137739\n",
       "2      1.1111  0.157036\n",
       "3      1.6667  0.172374\n",
       "4      2.2222  0.185353"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the curve\n",
    "Long._2_JAW_log_probit_curve.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Long.response_curve(chemical_name = 2, endpoint_name = \"JAW\", model = \"logistic\")\n",
    "Long._2_JAW_logistic_curve_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "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>chemical.id</th>\n",
       "      <th>concentration</th>\n",
       "      <th>plate.id</th>\n",
       "      <th>endpoint</th>\n",
       "      <th>bmdrc.num.tot</th>\n",
       "      <th>bmdrc.num.nonna</th>\n",
       "      <th>bmdrc.num.affected</th>\n",
       "      <th>bmdrc.Plate.ID</th>\n",
       "      <th>bmdrc.Endpoint.ID</th>\n",
       "      <th>bmdrc.filter</th>\n",
       "      <th>bmdrc.filter.reason</th>\n",
       "      <th>bmdrc.frac.affected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2 A JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2 B JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2 C JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2 A JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>B</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2 B JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>C</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2 C JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>A</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2 A JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>B</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2 B JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>C</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2 C JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>A</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2 A JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>B</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2 B JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>C</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2 C JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>A</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2 A JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2 B JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>C</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2 C JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>A</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2 A JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>B</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2 B JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>C</td>\n",
       "      <td>JAW</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2 C JAW</td>\n",
       "      <td>2 JAW</td>\n",
       "      <td>Keep</td>\n",
       "      <td></td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     chemical.id  concentration plate.id endpoint  bmdrc.num.tot  \\\n",
       "99             2            0.0        A      JAW              6   \n",
       "103            2            0.0        B      JAW              6   \n",
       "107            2            0.0        C      JAW              6   \n",
       "112            2            5.0        A      JAW              6   \n",
       "117            2            5.0        B      JAW              6   \n",
       "122            2            5.0        C      JAW              6   \n",
       "127            2           20.0        A      JAW              6   \n",
       "132            2           20.0        B      JAW              6   \n",
       "137            2           20.0        C      JAW              6   \n",
       "142            2           40.0        A      JAW              6   \n",
       "147            2           40.0        B      JAW              6   \n",
       "152            2           40.0        C      JAW              6   \n",
       "157            2           80.0        A      JAW              6   \n",
       "162            2           80.0        B      JAW              6   \n",
       "167            2           80.0        C      JAW              6   \n",
       "172            2          100.0        A      JAW              6   \n",
       "177            2          100.0        B      JAW              6   \n",
       "182            2          100.0        C      JAW              6   \n",
       "\n",
       "     bmdrc.num.nonna  bmdrc.num.affected bmdrc.Plate.ID bmdrc.Endpoint.ID  \\\n",
       "99                 6                 0.0        2 A JAW             2 JAW   \n",
       "103                6                 0.0        2 B JAW             2 JAW   \n",
       "107                6                 2.0        2 C JAW             2 JAW   \n",
       "112                6                 2.0        2 A JAW             2 JAW   \n",
       "117                6                 0.0        2 B JAW             2 JAW   \n",
       "122                6                 2.0        2 C JAW             2 JAW   \n",
       "127                6                 3.0        2 A JAW             2 JAW   \n",
       "132                6                 0.0        2 B JAW             2 JAW   \n",
       "137                6                 3.0        2 C JAW             2 JAW   \n",
       "142                6                 4.0        2 A JAW             2 JAW   \n",
       "147                6                 0.0        2 B JAW             2 JAW   \n",
       "152                6                 4.0        2 C JAW             2 JAW   \n",
       "157                6                 5.0        2 A JAW             2 JAW   \n",
       "162                6                 1.0        2 B JAW             2 JAW   \n",
       "167                6                 6.0        2 C JAW             2 JAW   \n",
       "172                6                 4.0        2 A JAW             2 JAW   \n",
       "177                6                 0.0        2 B JAW             2 JAW   \n",
       "182                6                 3.0        2 C JAW             2 JAW   \n",
       "\n",
       "    bmdrc.filter bmdrc.filter.reason  bmdrc.frac.affected  \n",
       "99          Keep                                 0.000000  \n",
       "103         Keep                                 0.000000  \n",
       "107         Keep                                 0.333333  \n",
       "112         Keep                                 0.333333  \n",
       "117         Keep                                 0.000000  \n",
       "122         Keep                                 0.333333  \n",
       "127         Keep                                 0.500000  \n",
       "132         Keep                                 0.000000  \n",
       "137         Keep                                 0.500000  \n",
       "142         Keep                                 0.666667  \n",
       "147         Keep                                 0.000000  \n",
       "152         Keep                                 0.666667  \n",
       "157         Keep                                 0.833333  \n",
       "162         Keep                                 0.166667  \n",
       "167         Keep                                 1.000000  \n",
       "172         Keep                                 0.666667  \n",
       "177         Keep                                 0.000000  \n",
       "182         Keep                                 0.500000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Long.plate_groups[Long.plate_groups[\"bmdrc.Endpoint.ID\"] == \"2 JAW\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\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>Logistic</th>\n",
       "      <th>Gamma</th>\n",
       "      <th>Weibull</th>\n",
       "      <th>Log Logistic</th>\n",
       "      <th>Probit</th>\n",
       "      <th>Log Probit</th>\n",
       "      <th>Multistage2</th>\n",
       "      <th>Quantal Linear</th>\n",
       "      <th>bmdrc.Endpoint.ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000375</td>\n",
       "      <td>0.050247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.019340</td>\n",
       "      <td>5.897959e-16</td>\n",
       "      <td>0.292654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>1 ANY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000328</td>\n",
       "      <td>0.047331</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005426</td>\n",
       "      <td>1.439406e-16</td>\n",
       "      <td>0.328666</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>1 ANY24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.469054</td>\n",
       "      <td>0.588875</td>\n",
       "      <td>0.595280</td>\n",
       "      <td>0.697117</td>\n",
       "      <td>2.690193e-01</td>\n",
       "      <td>0.723720</td>\n",
       "      <td>5.868656e-01</td>\n",
       "      <td>0.748461</td>\n",
       "      <td>1 DP24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.004244</td>\n",
       "      <td>0.065812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.662209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.493732</td>\n",
       "      <td>2 ANY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.004244</td>\n",
       "      <td>0.065812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.662209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.493732</td>\n",
       "      <td>2 ANY24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.124670</td>\n",
       "      <td>0.658049</td>\n",
       "      <td>0.677035</td>\n",
       "      <td>0.723900</td>\n",
       "      <td>1.129458e-05</td>\n",
       "      <td>0.729924</td>\n",
       "      <td>8.568215e-07</td>\n",
       "      <td>0.638903</td>\n",
       "      <td>2 DP24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.142444</td>\n",
       "      <td>0.304786</td>\n",
       "      <td>0.314802</td>\n",
       "      <td>0.326696</td>\n",
       "      <td>1.125893e-04</td>\n",
       "      <td>0.330841</td>\n",
       "      <td>1.392850e-01</td>\n",
       "      <td>0.240683</td>\n",
       "      <td>2 JAW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.225186</td>\n",
       "      <td>0.432344</td>\n",
       "      <td>0.263744</td>\n",
       "      <td>0.652906</td>\n",
       "      <td>2.188501e-01</td>\n",
       "      <td>0.652691</td>\n",
       "      <td>2.147414e-01</td>\n",
       "      <td>0.061959</td>\n",
       "      <td>2 SM24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Logistic     Gamma   Weibull  Log Logistic        Probit  Log Probit  \\\n",
       "0  0.000375  0.050247       NaN      0.019340  5.897959e-16    0.292654   \n",
       "1  0.000328  0.047331       NaN      0.005426  1.439406e-16    0.328666   \n",
       "2  0.469054  0.588875  0.595280      0.697117  2.690193e-01    0.723720   \n",
       "3  0.004244  0.065812       NaN      0.662209           NaN         NaN   \n",
       "4  0.004244  0.065812       NaN      0.662209           NaN         NaN   \n",
       "5  0.124670  0.658049  0.677035      0.723900  1.129458e-05    0.729924   \n",
       "6  0.142444  0.304786  0.314802      0.326696  1.125893e-04    0.330841   \n",
       "7  0.225186  0.432344  0.263744      0.652906  2.188501e-01    0.652691   \n",
       "\n",
       "    Multistage2  Quantal Linear bmdrc.Endpoint.ID  \n",
       "0           NaN        0.000192             1 ANY  \n",
       "1           NaN        0.000160           1 ANY24  \n",
       "2  5.868656e-01        0.748461            1 DP24  \n",
       "3  1.000000e+00        0.493732             2 ANY  \n",
       "4  1.000000e+00        0.493732           2 ANY24  \n",
       "5  8.568215e-07        0.638903            2 DP24  \n",
       "6  1.392850e-01        0.240683             2 JAW  \n",
       "7  2.147414e-01        0.061959            2 SM24  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Search for equivalent fits: p-values should be over 0.1\n",
    "Long.p_value_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Logistic</th>\n",
       "      <th>Gamma</th>\n",
       "      <th>Weibull</th>\n",
       "      <th>Log Logistic</th>\n",
       "      <th>Probit</th>\n",
       "      <th>Log Probit</th>\n",
       "      <th>Multistage2</th>\n",
       "      <th>Quantal Linear</th>\n",
       "      <th>bmdrc.Endpoint.ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>76.855413</td>\n",
       "      <td>65.448837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.457571</td>\n",
       "      <td>126.526706</td>\n",
       "      <td>62.958403</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.932544</td>\n",
       "      <td>1 ANY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77.632543</td>\n",
       "      <td>65.977218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.392880</td>\n",
       "      <td>129.431126</td>\n",
       "      <td>63.302153</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.749863</td>\n",
       "      <td>1 ANY24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>86.471958</td>\n",
       "      <td>86.862896</td>\n",
       "      <td>86.841449</td>\n",
       "      <td>86.361957</td>\n",
       "      <td>87.879481</td>\n",
       "      <td>86.250852</td>\n",
       "      <td>86.866241</td>\n",
       "      <td>84.866240</td>\n",
       "      <td>1 DP24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103.456849</td>\n",
       "      <td>97.664892</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.307264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.975839</td>\n",
       "      <td>2 ANY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103.456849</td>\n",
       "      <td>97.664892</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.307264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.975839</td>\n",
       "      <td>2 ANY24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>125.709901</td>\n",
       "      <td>121.733128</td>\n",
       "      <td>121.653766</td>\n",
       "      <td>121.457702</td>\n",
       "      <td>138.795561</td>\n",
       "      <td>121.437530</td>\n",
       "      <td>142.238967</td>\n",
       "      <td>120.600536</td>\n",
       "      <td>2 DP24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>137.328336</td>\n",
       "      <td>135.908417</td>\n",
       "      <td>135.828771</td>\n",
       "      <td>135.736550</td>\n",
       "      <td>150.061355</td>\n",
       "      <td>135.705915</td>\n",
       "      <td>137.772865</td>\n",
       "      <td>135.772821</td>\n",
       "      <td>2 JAW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>108.071025</td>\n",
       "      <td>107.131519</td>\n",
       "      <td>108.472431</td>\n",
       "      <td>105.933951</td>\n",
       "      <td>108.082124</td>\n",
       "      <td>105.934963</td>\n",
       "      <td>109.040748</td>\n",
       "      <td>112.185783</td>\n",
       "      <td>2 SM24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Logistic       Gamma     Weibull  Log Logistic      Probit  Log Probit  \\\n",
       "0   76.855413   65.448837         NaN     68.457571  126.526706   62.958403   \n",
       "1   77.632543   65.977218         NaN     68.392880  129.431126   63.302153   \n",
       "2   86.471958   86.862896   86.841449     86.361957   87.879481   86.250852   \n",
       "3  103.456849   97.664892         NaN     89.307264         NaN         NaN   \n",
       "4  103.456849   97.664892         NaN     89.307264         NaN         NaN   \n",
       "5  125.709901  121.733128  121.653766    121.457702  138.795561  121.437530   \n",
       "6  137.328336  135.908417  135.828771    135.736550  150.061355  135.705915   \n",
       "7  108.071025  107.131519  108.472431    105.933951  108.082124  105.934963   \n",
       "\n",
       "   Multistage2  Quantal Linear bmdrc.Endpoint.ID  \n",
       "0          NaN       74.932544             1 ANY  \n",
       "1          NaN       75.749863           1 ANY24  \n",
       "2    86.866241       84.866240            1 DP24  \n",
       "3          NaN       89.975839             2 ANY  \n",
       "4          NaN       89.975839           2 ANY24  \n",
       "5   142.238967      120.600536            2 DP24  \n",
       "6   137.772865      135.772821             2 JAW  \n",
       "7   109.040748      112.185783            2 SM24  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Search for an equivalent fit, AICs should be within 2 of the lowest value\n",
    "Long.aic_df # Both Weibull, Log Logistic, and several others have close fits "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Logistic</th>\n",
       "      <th>Gamma</th>\n",
       "      <th>Weibull</th>\n",
       "      <th>Log Logistic</th>\n",
       "      <th>Probit</th>\n",
       "      <th>Log Probit</th>\n",
       "      <th>Multistage2</th>\n",
       "      <th>Quantal Linear</th>\n",
       "      <th>bmdrc.Endpoint.ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.843053</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.460179</td>\n",
       "      <td>1 ANY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.836095</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.457716</td>\n",
       "      <td>1 ANY24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.883928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.184012</td>\n",
       "      <td>0.355818</td>\n",
       "      <td>2.702677</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.955967</td>\n",
       "      <td>1.304424</td>\n",
       "      <td>1 DP24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.029032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>7.262204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.042621</td>\n",
       "      <td>2 ANY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.029032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>7.262204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.042621</td>\n",
       "      <td>2 ANY24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19.985842</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.797269</td>\n",
       "      <td>13.805274</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.189281</td>\n",
       "      <td>7.982673</td>\n",
       "      <td>2 DP24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18.982997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.099766</td>\n",
       "      <td>2.390934</td>\n",
       "      <td>14.577799</td>\n",
       "      <td>9.696806</td>\n",
       "      <td>2 JAW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13.718414</td>\n",
       "      <td>24.330544</td>\n",
       "      <td>15.960706</td>\n",
       "      <td>51.680453</td>\n",
       "      <td>13.115241</td>\n",
       "      <td>65.310932</td>\n",
       "      <td>21.803398</td>\n",
       "      <td>6.399155</td>\n",
       "      <td>2 SM24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Logistic      Gamma    Weibull  Log Logistic     Probit  Log Probit  \\\n",
       "0   0.843053   0.000076   0.550000           NaN        NaN         NaN   \n",
       "1   0.836095   0.000075   0.550000           NaN        NaN         NaN   \n",
       "2   2.883928        NaN   0.184012      0.355818   2.702677         NaN   \n",
       "3   3.029032        NaN   0.550000      7.262204   0.000000    0.000000   \n",
       "4   3.029032        NaN   0.550000      7.262204   0.000000    0.000000   \n",
       "5  19.985842        NaN        NaN      0.797269  13.805274         NaN   \n",
       "6  18.982997        NaN        NaN           NaN  13.099766    2.390934   \n",
       "7  13.718414  24.330544  15.960706     51.680453  13.115241   65.310932   \n",
       "\n",
       "   Multistage2  Quantal Linear bmdrc.Endpoint.ID  \n",
       "0          NaN        0.460179             1 ANY  \n",
       "1          NaN        0.457716           1 ANY24  \n",
       "2     1.955967        1.304424            1 DP24  \n",
       "3          NaN        2.042621             2 ANY  \n",
       "4          NaN        2.042621           2 ANY24  \n",
       "5     5.189281        7.982673            2 DP24  \n",
       "6    14.577799        9.696806             2 JAW  \n",
       "7    21.803398        6.399155            2 SM24  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Search for a low bmdl value \n",
    "Long.bmdls_df # The best fit is log probit, and the second best fit is quantal linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Long.response_curve(chemical_name = 2, endpoint_name = \"JAW\", model = \"quantal linear\")\n",
    "Long._2_JAW_quantal_linear_curve_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Module 5: Output Modules\n",
    "\n",
    "### Benchmark Dose\n",
    "\n",
    "Build reports with the `.report()` function. See the \"example_report\" folder for an example. Otherwise, output a benchmark dose table with the function `.output_benchmark_dose()` and see the results with `.output_res_benchmark_dose`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "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>Chemical_ID</th>\n",
       "      <th>End_Point</th>\n",
       "      <th>Model</th>\n",
       "      <th>BMD10</th>\n",
       "      <th>BMDL</th>\n",
       "      <th>BMD50</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Min_Dose</th>\n",
       "      <th>Max_Dose</th>\n",
       "      <th>AUC_Norm</th>\n",
       "      <th>DataQC_Flag</th>\n",
       "      <th>BMD_Analysis_Flag</th>\n",
       "      <th>BMD10_Flag</th>\n",
       "      <th>BMD50_Flag</th>\n",
       "      <th>bmdrc.Endpoint.ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ANY</td>\n",
       "      <td>Log Probit</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.024599</td>\n",
       "      <td>19.025000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.792708</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1 ANY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ANY24</td>\n",
       "      <td>Log Probit</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.017991</td>\n",
       "      <td>19.125000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.796875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1 ANY24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>DP24</td>\n",
       "      <td>Weibull</td>\n",
       "      <td>1.798141</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.360780</td>\n",
       "      <td>10.475000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.436458</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1 DP24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>ANY</td>\n",
       "      <td>Quantal Linear</td>\n",
       "      <td>2.982072</td>\n",
       "      <td>2.042621</td>\n",
       "      <td>19.618498</td>\n",
       "      <td>87.083333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.870833</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2 ANY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>ANY24</td>\n",
       "      <td>Quantal Linear</td>\n",
       "      <td>2.982072</td>\n",
       "      <td>2.042621</td>\n",
       "      <td>19.618498</td>\n",
       "      <td>87.083333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.870833</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2 ANY24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>DP24</td>\n",
       "      <td>Log Logistic</td>\n",
       "      <td>6.321510</td>\n",
       "      <td>0.797269</td>\n",
       "      <td>72.392449</td>\n",
       "      <td>45.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2 DP24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>SM24</td>\n",
       "      <td>Gamma</td>\n",
       "      <td>57.192487</td>\n",
       "      <td>24.330544</td>\n",
       "      <td>78.809252</td>\n",
       "      <td>49.583333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.495833</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2 SM24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>JAW</td>\n",
       "      <td>Log Probit</td>\n",
       "      <td>2.842871</td>\n",
       "      <td>2.390934</td>\n",
       "      <td>115.465467</td>\n",
       "      <td>66.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.662500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2 JAW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>JAW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.850000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.452083</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1 JAW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NC24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1 NC24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>SM24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.558333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1 SM24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Chemical_ID End_Point           Model      BMD10       BMDL       BMD50  \\\n",
       "0           1       ANY      Log Probit   0.000021        NaN    0.024599   \n",
       "1           1     ANY24      Log Probit   0.000011        NaN    0.017991   \n",
       "2           1      DP24         Weibull   1.798141        NaN   13.360780   \n",
       "3           2       ANY  Quantal Linear   2.982072   2.042621   19.618498   \n",
       "4           2     ANY24  Quantal Linear   2.982072   2.042621   19.618498   \n",
       "5           2      DP24    Log Logistic   6.321510   0.797269   72.392449   \n",
       "7           2      SM24           Gamma  57.192487  24.330544   78.809252   \n",
       "6           2       JAW      Log Probit   2.842871   2.390934  115.465467   \n",
       "0           1       JAW             NaN        NaN        NaN         NaN   \n",
       "1           1      NC24             NaN        NaN        NaN         NaN   \n",
       "2           1      SM24             NaN        NaN        NaN         NaN   \n",
       "\n",
       "         AUC  Min_Dose  Max_Dose  AUC_Norm  DataQC_Flag  BMD_Analysis_Flag  \\\n",
       "0  19.025000       0.0      24.0  0.792708            1                  2   \n",
       "1  19.125000       0.0      24.0  0.796875            1                  2   \n",
       "2  10.475000       0.0      24.0  0.436458            1                  2   \n",
       "3  87.083333       0.0     100.0  0.870833            1                  2   \n",
       "4  87.083333       0.0     100.0  0.870833            1                  2   \n",
       "5  45.833333       0.0     100.0  0.458333            1                  2   \n",
       "7  49.583333       0.0     100.0  0.495833            1                  2   \n",
       "6  66.250000       0.0     100.0  0.662500            1                  1   \n",
       "0  10.850000       0.0      24.0  0.452083            0                  0   \n",
       "1   0.250000       0.0       1.5  0.166667            0                  0   \n",
       "2  13.400000       0.0      24.0  0.558333            0                  0   \n",
       "\n",
       "   BMD10_Flag  BMD50_Flag bmdrc.Endpoint.ID  \n",
       "0           1           1             1 ANY  \n",
       "1           1           1           1 ANY24  \n",
       "2           1           1            1 DP24  \n",
       "3           1           1             2 ANY  \n",
       "4           1           1           2 ANY24  \n",
       "5           1           1            2 DP24  \n",
       "7           1           1            2 SM24  \n",
       "6           1           0             2 JAW  \n",
       "0           0           0             1 JAW  \n",
       "1           0           0            1 NC24  \n",
       "2           0           0            1 SM24  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Long.output_benchmark_dose()\n",
    "Long.output_res_benchmark_dose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dose\n",
    "\n",
    "Similarly, see the dosage information with `.output_dose_table()` and see the actual table under `.output_res_dose_table`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Chemical_ID</th>\n",
       "      <th>End_Point</th>\n",
       "      <th>Dose</th>\n",
       "      <th>ids</th>\n",
       "      <th>num.affected</th>\n",
       "      <th>num.nonna</th>\n",
       "      <th>CI_Lo</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ANY</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1 ANY</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.162788</td>\n",
       "      <td>0.562505</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1.5</td>\n",
       "      <td>1 ANY</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.607780</td>\n",
       "      <td>0.941634</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>ANY</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1 ANY</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.468669</td>\n",
       "      <td>0.867200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>ANY</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1 ANY</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.589705</td>\n",
       "      <td>0.938089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>ANY</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1 ANY</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.672002</td>\n",
       "      <td>0.968980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2</td>\n",
       "      <td>SM24</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2 SM24</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.058366</td>\n",
       "      <td>0.392220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2</td>\n",
       "      <td>SM24</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2 SM24</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.090009</td>\n",
       "      <td>0.452146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2</td>\n",
       "      <td>SM24</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2 SM24</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.162788</td>\n",
       "      <td>0.562505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2</td>\n",
       "      <td>SM24</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2 SM24</td>\n",
       "      <td>10.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.337164</td>\n",
       "      <td>0.754405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2</td>\n",
       "      <td>SM24</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2 SM24</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.742427</td>\n",
       "      <td>0.990125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Chemical_ID End_Point   Dose     ids  num.affected  num.nonna     CI_Lo  \\\n",
       "0             1       ANY    0.0   1 ANY           6.0         18  0.162788   \n",
       "1             1       ANY    1.5   1 ANY          15.0         18  0.607780   \n",
       "2             1       ANY    3.0   1 ANY          12.0         17  0.468669   \n",
       "3             1       ANY    6.0   1 ANY          14.0         17  0.589705   \n",
       "4             1       ANY   12.0   1 ANY          16.0         18  0.672002   \n",
       "..          ...       ...    ...     ...           ...        ...       ...   \n",
       "57            2      SM24    5.0  2 SM24           3.0         18  0.058366   \n",
       "58            2      SM24   20.0  2 SM24           4.0         18  0.090009   \n",
       "59            2      SM24   40.0  2 SM24           6.0         18  0.162788   \n",
       "60            2      SM24   80.0  2 SM24          10.0         18  0.337164   \n",
       "61            2      SM24  100.0  2 SM24          17.0         18  0.742427   \n",
       "\n",
       "       CI_Hi  \n",
       "0   0.562505  \n",
       "1   0.941634  \n",
       "2   0.867200  \n",
       "3   0.938089  \n",
       "4   0.968980  \n",
       "..       ...  \n",
       "57  0.392220  \n",
       "58  0.452146  \n",
       "59  0.562505  \n",
       "60  0.754405  \n",
       "61  0.990125  \n",
       "\n",
       "[62 rows x 8 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Long.output_dose_table()\n",
    "Long.output_res_dose_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fits\n",
    "\n",
    "And finally, see the fit information with `.output_fits_table()` and see the actual table under `.output_res_fits_table`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Chemical_ID</th>\n",
       "      <th>End_Point</th>\n",
       "      <th>X_vals</th>\n",
       "      <th>Y_vals</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>0</th>\n",
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       "      <th>10</th>\n",
       "      <td>2</td>\n",
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       "      <td>91.1111</td>\n",
       "      <td>0.798929</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>SM24</td>\n",
       "      <td>93.3333</td>\n",
       "      <td>0.824558</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>SM24</td>\n",
       "      <td>95.5556</td>\n",
       "      <td>0.847904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>SM24</td>\n",
       "      <td>97.7778</td>\n",
       "      <td>0.868969</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>SM24</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.887809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>371 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Chemical_ID End_Point   X_vals    Y_vals\n",
       "0            1       ANY      0.0  0.166515\n",
       "0            1       ANY   0.1667  0.696162\n",
       "0            1       ANY   0.3333  0.734622\n",
       "0            1       ANY      0.5  0.756134\n",
       "0            1       ANY   0.6667  0.770887\n",
       "..         ...       ...      ...       ...\n",
       "10           2      SM24  91.1111  0.798929\n",
       "10           2      SM24  93.3333  0.824558\n",
       "10           2      SM24  95.5556  0.847904\n",
       "10           2      SM24  97.7778  0.868969\n",
       "10           2      SM24    100.0  0.887809\n",
       "\n",
       "[371 rows x 4 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Long.output_fits_table()\n",
    "Long.output_res_fits_table"
   ]
  }
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