vignettes/Simplified Class Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "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`\n",
    "\n",
    "## Data Types Description\n",
    "\n",
    "For datasets with plates and wells, use the `BinaryClass` object as it contains specific pre-processing and filtering functions that pertain to plates and well. Each value should be 0, 1, or NA, and `bmdrc` will calculate the response column. If your data does not contain plates and wells, use the `SimplifiedClass` object which only requires chemical, endpoint, concentration, and response information. The response must be between 0 and 1. If your data is light photomotor response use the `LPRClass` object. The continuous data will be transformed to dichotomous."
   ]
  },
  {
   "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 Module \n",
    "\n",
    "The first step to using the bmdrc python library is to properly format the data. For dichotmous data without plate or well information, use the `SimplifiedClass` object which can be imported using `from bmdrc.SimplifiedClass import SimplifiedClass`. Next, read the data as a pandas dataframe. The data must be in long format. You will need four columns - the chemical, the endpoint, the concentration, and the response value which must range between 0 and 1. Remove NAs before making the `SimplifiedClass` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bmdrc.SimplifiedClass import SimplifiedClass\n",
    "\n",
    "res_only = SimplifiedClass(\n",
    "    df = pd.read_table(\"../data/ToxExample_Long.txt\"), # Input is a pandas DataFrame\n",
    "    chemical = \"chemical.id\", # The name of the chemical column \n",
    "    endpoint = \"endpoint\", # The name of the column with endpoints\n",
    "    concentration = \"concentration\", # The name of the concentration column\n",
    "    response = \"response\" # The name of the column with response value ranging from 0 to 1\n",
    ")"
   ]
  },
  {
   "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. Here, only removing endpoints is applicable, as new endpoints cannot be created since they depend on the absence/presence (0/1) *and* wells cannot be set to NA as there is no well information.\n",
    "\n",
    "#### Remove Endpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['202992', 'Endpoint1', 'Endpoint2'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_only.remove_endpoints(endpoint_name = \"DNC\")\n",
    "res_only.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:** Not used, as the assumption is that plates with unusually high responses should be removed. Here, there is no plate information. \n",
    "\n",
    "#### Minimum Concentration Filter\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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "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",
    "res_only.filter_min_concentration(count = 3, apply = False, diagnostic_plot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>14</td>\n",
       "      <td>1</td>\n",
       "      <td>Keep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Keep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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       14      1    Keep\n",
       "1        5      2    Keep\n",
       "2        1      1  Remove"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_only.filter_min_concentration_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the filter with defaults\n",
    "res_only.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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View filter. There is no need to apply in this case. \n",
    "res_only.filter_correlation_score(score = 0.2, diagnostic_plot = True, apply = False)\n",
    "res_only.filter_correlation_score_plot"
   ]
  },
  {
   "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": 8,
   "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",
    "res_only.fit_models(gof_threshold = 0.1, aic_threshold = 2, model_selection = \"lowest BMDL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_only.response_curve(\"Chemical2\", \"Endpoint1\", \"logistic\")\n",
    "res_only._Chemical2_Endpoint1_logistic_curve_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Module 5: Output Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>Quantal Linear</td>\n",
       "      <td>20.332844</td>\n",
       "      <td>3.207443</td>\n",
       "      <td>133.765988</td>\n",
       "      <td>3.680000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.092000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chemical3</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chemical3 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.650000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.616250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chemical2 Endpoint1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DTXSID7020182</td>\n",
       "      <td>202992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.747014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.363856</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>DTXSID7020182 202992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Chemical_ID  End_Point           Model      BMD10      BMDL       BMD50  \\\n",
       "0      Chemical2  Endpoint2  Quantal Linear  20.332844  3.207443  133.765988   \n",
       "0      Chemical3  Endpoint2             NaN        NaN       NaN         NaN   \n",
       "0      Chemical2  Endpoint1             NaN        NaN       NaN         NaN   \n",
       "1  DTXSID7020182     202992             NaN        NaN       NaN         NaN   \n",
       "\n",
       "         AUC  Min_Dose  Max_Dose  AUC_Norm  DataQC_Flag  BMD_Analysis_Flag  \\\n",
       "0   3.680000       0.0      40.0  0.092000            2                  1   \n",
       "0   0.050000       0.0       1.0  0.050000            1                  0   \n",
       "0  24.650000       0.0      40.0  0.616250            0                  0   \n",
       "1  32.747014       0.0      90.0  0.363856            0                  0   \n",
       "\n",
       "   BMD10_Flag  BMD50_Flag     bmdrc.Endpoint.ID  \n",
       "0           1           0   Chemical2 Endpoint2  \n",
       "0           0           0   Chemical3 Endpoint2  \n",
       "0           0           0   Chemical2 Endpoint1  \n",
       "1           0           0  DTXSID7020182 202992  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_only.output_benchmark_dose()\n",
    "res_only.output_res_benchmark_dose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "    }\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>Dose</th>\n",
       "      <th>bmdrc.Endpoint.ID</th>\n",
       "      <th>num.affected</th>\n",
       "      <th>num.nonna</th>\n",
       "      <th>CI_Lo</th>\n",
       "      <th>CI_Hi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint1</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Chemical2 Endpoint1</td>\n",
       "      <td>0.0200</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.434482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint1</td>\n",
       "      <td>2.000</td>\n",
       "      <td>Chemical2 Endpoint1</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.434482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint1</td>\n",
       "      <td>8.000</td>\n",
       "      <td>Chemical2 Endpoint1</td>\n",
       "      <td>0.6300</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.434482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint1</td>\n",
       "      <td>10.000</td>\n",
       "      <td>Chemical2 Endpoint1</td>\n",
       "      <td>1.4300</td>\n",
       "      <td>5</td>\n",
       "      <td>0.036224</td>\n",
       "      <td>0.624465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint1</td>\n",
       "      <td>20.000</td>\n",
       "      <td>Chemical2 Endpoint1</td>\n",
       "      <td>3.5300</td>\n",
       "      <td>5</td>\n",
       "      <td>0.230724</td>\n",
       "      <td>0.882379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint1</td>\n",
       "      <td>40.000</td>\n",
       "      <td>Chemical2 Endpoint1</td>\n",
       "      <td>4.6100</td>\n",
       "      <td>5</td>\n",
       "      <td>0.375535</td>\n",
       "      <td>0.963776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.793451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>2.000</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.793451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>8.000</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.793451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>10.000</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.793451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>20.000</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.793451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>40.000</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "      <td>0.1500</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.793451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Chemical3</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Chemical3 Endpoint2</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.793451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Chemical3</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>1.000</td>\n",
       "      <td>Chemical3 Endpoint2</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.793451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>DTXSID7020182</td>\n",
       "      <td>202992</td>\n",
       "      <td>0.000</td>\n",
       "      <td>DTXSID7020182 202992</td>\n",
       "      <td>0.0348</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.561497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>DTXSID7020182</td>\n",
       "      <td>202992</td>\n",
       "      <td>0.003</td>\n",
       "      <td>DTXSID7020182 202992</td>\n",
       "      <td>0.0157</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.561497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DTXSID7020182</td>\n",
       "      <td>202992</td>\n",
       "      <td>0.007</td>\n",
       "      <td>DTXSID7020182 202992</td>\n",
       "      <td>0.0823</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.561497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>DTXSID7020182</td>\n",
       "      <td>202992</td>\n",
       "      <td>0.010</td>\n",
       "      <td>DTXSID7020182 202992</td>\n",
       "      <td>0.0840</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.561497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>DTXSID7020182</td>\n",
       "      <td>202992</td>\n",
       "      <td>0.030</td>\n",
       "      <td>DTXSID7020182 202992</td>\n",
       "      <td>0.1212</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.561497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>DTXSID7020182</td>\n",
       "      <td>202992</td>\n",
       "      <td>0.070</td>\n",
       "      <td>DTXSID7020182 202992</td>\n",
       "      <td>0.0097</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.657620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DTXSID7020182</td>\n",
       "      <td>202992</td>\n",
       "      <td>0.100</td>\n",
       "      <td>DTXSID7020182 202992</td>\n",
       "      <td>0.0670</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.561497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>DTXSID7020182</td>\n",
       "      <td>202992</td>\n",
       "      <td>0.300</td>\n",
       "      <td>DTXSID7020182 202992</td>\n",
       "      <td>0.0158</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.561497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>DTXSID7020182</td>\n",
       "      <td>202992</td>\n",
       "      <td>0.700</td>\n",
       "      <td>DTXSID7020182 202992</td>\n",
       "      <td>0.0165</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.561497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>DTXSID7020182</td>\n",
       "      <td>202992</td>\n",
       "      <td>2.000</td>\n",
       "      <td>DTXSID7020182 202992</td>\n",
       "      <td>0.1098</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.561497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>DTXSID7020182</td>\n",
       "      <td>202992</td>\n",
       "      <td>4.000</td>\n",
       "      <td>DTXSID7020182 202992</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.561497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>DTXSID7020182</td>\n",
       "      <td>202992</td>\n",
       "      <td>8.000</td>\n",
       "      <td>DTXSID7020182 202992</td>\n",
       "      <td>0.0542</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.561497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>DTXSID7020182</td>\n",
       "      <td>202992</td>\n",
       "      <td>20.000</td>\n",
       "      <td>DTXSID7020182 202992</td>\n",
       "      <td>0.0697</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.561497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>DTXSID7020182</td>\n",
       "      <td>202992</td>\n",
       "      <td>40.000</td>\n",
       "      <td>DTXSID7020182 202992</td>\n",
       "      <td>0.7752</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.561497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>DTXSID7020182</td>\n",
       "      <td>202992</td>\n",
       "      <td>90.000</td>\n",
       "      <td>DTXSID7020182 202992</td>\n",
       "      <td>2.9322</td>\n",
       "      <td>3</td>\n",
       "      <td>0.207660</td>\n",
       "      <td>0.938508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Chemical_ID  End_Point    Dose     bmdrc.Endpoint.ID  num.affected  \\\n",
       "0       Chemical2  Endpoint1   0.000   Chemical2 Endpoint1        0.0200   \n",
       "1       Chemical2  Endpoint1   2.000   Chemical2 Endpoint1        0.1200   \n",
       "2       Chemical2  Endpoint1   8.000   Chemical2 Endpoint1        0.6300   \n",
       "3       Chemical2  Endpoint1  10.000   Chemical2 Endpoint1        1.4300   \n",
       "4       Chemical2  Endpoint1  20.000   Chemical2 Endpoint1        3.5300   \n",
       "5       Chemical2  Endpoint1  40.000   Chemical2 Endpoint1        4.6100   \n",
       "6       Chemical2  Endpoint2   0.000   Chemical2 Endpoint2        0.0000   \n",
       "7       Chemical2  Endpoint2   2.000   Chemical2 Endpoint2        0.0000   \n",
       "8       Chemical2  Endpoint2   8.000   Chemical2 Endpoint2        0.0800   \n",
       "9       Chemical2  Endpoint2  10.000   Chemical2 Endpoint2        0.0600   \n",
       "10      Chemical2  Endpoint2  20.000   Chemical2 Endpoint2        0.1000   \n",
       "11      Chemical2  Endpoint2  40.000   Chemical2 Endpoint2        0.1500   \n",
       "12      Chemical3  Endpoint2   0.000   Chemical3 Endpoint2        0.0000   \n",
       "13      Chemical3  Endpoint2   1.000   Chemical3 Endpoint2        0.1000   \n",
       "14  DTXSID7020182     202992   0.000  DTXSID7020182 202992        0.0348   \n",
       "15  DTXSID7020182     202992   0.003  DTXSID7020182 202992        0.0157   \n",
       "16  DTXSID7020182     202992   0.007  DTXSID7020182 202992        0.0823   \n",
       "17  DTXSID7020182     202992   0.010  DTXSID7020182 202992        0.0840   \n",
       "18  DTXSID7020182     202992   0.030  DTXSID7020182 202992        0.1212   \n",
       "19  DTXSID7020182     202992   0.070  DTXSID7020182 202992        0.0097   \n",
       "20  DTXSID7020182     202992   0.100  DTXSID7020182 202992        0.0670   \n",
       "21  DTXSID7020182     202992   0.300  DTXSID7020182 202992        0.0158   \n",
       "22  DTXSID7020182     202992   0.700  DTXSID7020182 202992        0.0165   \n",
       "23  DTXSID7020182     202992   2.000  DTXSID7020182 202992        0.1098   \n",
       "24  DTXSID7020182     202992   4.000  DTXSID7020182 202992        0.0000   \n",
       "25  DTXSID7020182     202992   8.000  DTXSID7020182 202992        0.0542   \n",
       "26  DTXSID7020182     202992  20.000  DTXSID7020182 202992        0.0697   \n",
       "27  DTXSID7020182     202992  40.000  DTXSID7020182 202992        0.7752   \n",
       "28  DTXSID7020182     202992  90.000  DTXSID7020182 202992        2.9322   \n",
       "\n",
       "    num.nonna     CI_Lo     CI_Hi  \n",
       "0           5  0.000000  0.434482  \n",
       "1           5  0.000000  0.434482  \n",
       "2           5  0.000000  0.434482  \n",
       "3           5  0.036224  0.624465  \n",
       "4           5  0.230724  0.882379  \n",
       "5           5  0.375535  0.963776  \n",
       "6           1  0.000000  0.793451  \n",
       "7           1  0.000000  0.793451  \n",
       "8           1  0.000000  0.793451  \n",
       "9           1  0.000000  0.793451  \n",
       "10          1  0.000000  0.793451  \n",
       "11          1  0.000000  0.793451  \n",
       "12          1  0.000000  0.793451  \n",
       "13          1  0.000000  0.793451  \n",
       "14          3  0.000000  0.561497  \n",
       "15          3  0.000000  0.561497  \n",
       "16          3  0.000000  0.561497  \n",
       "17          3  0.000000  0.561497  \n",
       "18          3  0.000000  0.561497  \n",
       "19          2  0.000000  0.657620  \n",
       "20          3  0.000000  0.561497  \n",
       "21          3  0.000000  0.561497  \n",
       "22          3  0.000000  0.561497  \n",
       "23          3  0.000000  0.561497  \n",
       "24          3  0.000000  0.561497  \n",
       "25          3  0.000000  0.561497  \n",
       "26          3  0.000000  0.561497  \n",
       "27          3  0.000000  0.561497  \n",
       "28          3  0.207660  0.938508  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_only.output_dose_table()\n",
    "res_only.output_res_dose_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>X_vals</th>\n",
       "      <th>Y_vals</th>\n",
       "      <th>bmdrc.Endpoint.ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DTXSID7020182</td>\n",
       "      <td>202992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DTXSID7020182 202992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chemical2 Endpoint1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>0.2222</td>\n",
       "      <td>0.001161</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>0.4444</td>\n",
       "      <td>0.002310</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.003459</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>0.8889</td>\n",
       "      <td>0.004605</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>1.1111</td>\n",
       "      <td>0.005751</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>1.3333</td>\n",
       "      <td>0.006895</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>1.5556</td>\n",
       "      <td>0.008038</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>1.7778</td>\n",
       "      <td>0.009180</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.010320</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>2.6667</td>\n",
       "      <td>0.013733</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>3.3333</td>\n",
       "      <td>0.017134</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>0.020524</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>4.6667</td>\n",
       "      <td>0.023902</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>5.3333</td>\n",
       "      <td>0.027267</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>0.030622</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>6.6667</td>\n",
       "      <td>0.033965</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>7.3333</td>\n",
       "      <td>0.037296</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.040616</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>8.2222</td>\n",
       "      <td>0.041720</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>8.4444</td>\n",
       "      <td>0.042823</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>8.6667</td>\n",
       "      <td>0.043925</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>8.8889</td>\n",
       "      <td>0.045025</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>9.1111</td>\n",
       "      <td>0.046124</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>9.3333</td>\n",
       "      <td>0.047222</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>9.5556</td>\n",
       "      <td>0.048319</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>9.7778</td>\n",
       "      <td>0.049414</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>0.050508</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>11.1111</td>\n",
       "      <td>0.055959</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>12.2222</td>\n",
       "      <td>0.061378</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>13.3333</td>\n",
       "      <td>0.066767</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>14.4444</td>\n",
       "      <td>0.072125</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>15.5556</td>\n",
       "      <td>0.077452</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>16.6667</td>\n",
       "      <td>0.082748</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>17.7778</td>\n",
       "      <td>0.088014</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>18.8889</td>\n",
       "      <td>0.093250</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>0.098455</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>22.2222</td>\n",
       "      <td>0.108777</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>24.4444</td>\n",
       "      <td>0.118981</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>26.6667</td>\n",
       "      <td>0.129068</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>28.8889</td>\n",
       "      <td>0.139039</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>31.1111</td>\n",
       "      <td>0.148896</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>33.3333</td>\n",
       "      <td>0.158640</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>35.5556</td>\n",
       "      <td>0.168274</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>37.7778</td>\n",
       "      <td>0.177796</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Chemical2</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>0.187209</td>\n",
       "      <td>Chemical2 Endpoint2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chemical3</td>\n",
       "      <td>Endpoint2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chemical3 Endpoint2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Chemical_ID  End_Point   X_vals    Y_vals     bmdrc.Endpoint.ID\n",
       "0   DTXSID7020182     202992      NaN       NaN  DTXSID7020182 202992\n",
       "0       Chemical2  Endpoint1      NaN       NaN   Chemical2 Endpoint1\n",
       "0       Chemical2  Endpoint2   0.0000  0.000010   Chemical2 Endpoint2\n",
       "1       Chemical2  Endpoint2   0.2222  0.001161   Chemical2 Endpoint2\n",
       "2       Chemical2  Endpoint2   0.4444  0.002310   Chemical2 Endpoint2\n",
       "3       Chemical2  Endpoint2   0.6667  0.003459   Chemical2 Endpoint2\n",
       "4       Chemical2  Endpoint2   0.8889  0.004605   Chemical2 Endpoint2\n",
       "5       Chemical2  Endpoint2   1.1111  0.005751   Chemical2 Endpoint2\n",
       "6       Chemical2  Endpoint2   1.3333  0.006895   Chemical2 Endpoint2\n",
       "7       Chemical2  Endpoint2   1.5556  0.008038   Chemical2 Endpoint2\n",
       "8       Chemical2  Endpoint2   1.7778  0.009180   Chemical2 Endpoint2\n",
       "9       Chemical2  Endpoint2   2.0000  0.010320   Chemical2 Endpoint2\n",
       "10      Chemical2  Endpoint2   2.6667  0.013733   Chemical2 Endpoint2\n",
       "11      Chemical2  Endpoint2   3.3333  0.017134   Chemical2 Endpoint2\n",
       "12      Chemical2  Endpoint2   4.0000  0.020524   Chemical2 Endpoint2\n",
       "13      Chemical2  Endpoint2   4.6667  0.023902   Chemical2 Endpoint2\n",
       "14      Chemical2  Endpoint2   5.3333  0.027267   Chemical2 Endpoint2\n",
       "15      Chemical2  Endpoint2   6.0000  0.030622   Chemical2 Endpoint2\n",
       "16      Chemical2  Endpoint2   6.6667  0.033965   Chemical2 Endpoint2\n",
       "17      Chemical2  Endpoint2   7.3333  0.037296   Chemical2 Endpoint2\n",
       "18      Chemical2  Endpoint2   8.0000  0.040616   Chemical2 Endpoint2\n",
       "19      Chemical2  Endpoint2   8.2222  0.041720   Chemical2 Endpoint2\n",
       "20      Chemical2  Endpoint2   8.4444  0.042823   Chemical2 Endpoint2\n",
       "21      Chemical2  Endpoint2   8.6667  0.043925   Chemical2 Endpoint2\n",
       "22      Chemical2  Endpoint2   8.8889  0.045025   Chemical2 Endpoint2\n",
       "23      Chemical2  Endpoint2   9.1111  0.046124   Chemical2 Endpoint2\n",
       "24      Chemical2  Endpoint2   9.3333  0.047222   Chemical2 Endpoint2\n",
       "25      Chemical2  Endpoint2   9.5556  0.048319   Chemical2 Endpoint2\n",
       "26      Chemical2  Endpoint2   9.7778  0.049414   Chemical2 Endpoint2\n",
       "27      Chemical2  Endpoint2  10.0000  0.050508   Chemical2 Endpoint2\n",
       "28      Chemical2  Endpoint2  11.1111  0.055959   Chemical2 Endpoint2\n",
       "29      Chemical2  Endpoint2  12.2222  0.061378   Chemical2 Endpoint2\n",
       "30      Chemical2  Endpoint2  13.3333  0.066767   Chemical2 Endpoint2\n",
       "31      Chemical2  Endpoint2  14.4444  0.072125   Chemical2 Endpoint2\n",
       "32      Chemical2  Endpoint2  15.5556  0.077452   Chemical2 Endpoint2\n",
       "33      Chemical2  Endpoint2  16.6667  0.082748   Chemical2 Endpoint2\n",
       "34      Chemical2  Endpoint2  17.7778  0.088014   Chemical2 Endpoint2\n",
       "35      Chemical2  Endpoint2  18.8889  0.093250   Chemical2 Endpoint2\n",
       "36      Chemical2  Endpoint2  20.0000  0.098455   Chemical2 Endpoint2\n",
       "37      Chemical2  Endpoint2  22.2222  0.108777   Chemical2 Endpoint2\n",
       "38      Chemical2  Endpoint2  24.4444  0.118981   Chemical2 Endpoint2\n",
       "39      Chemical2  Endpoint2  26.6667  0.129068   Chemical2 Endpoint2\n",
       "40      Chemical2  Endpoint2  28.8889  0.139039   Chemical2 Endpoint2\n",
       "41      Chemical2  Endpoint2  31.1111  0.148896   Chemical2 Endpoint2\n",
       "42      Chemical2  Endpoint2  33.3333  0.158640   Chemical2 Endpoint2\n",
       "43      Chemical2  Endpoint2  35.5556  0.168274   Chemical2 Endpoint2\n",
       "44      Chemical2  Endpoint2  37.7778  0.177796   Chemical2 Endpoint2\n",
       "45      Chemical2  Endpoint2  40.0000  0.187209   Chemical2 Endpoint2\n",
       "0       Chemical3  Endpoint2      NaN       NaN   Chemical3 Endpoint2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_only.output_fits_table()\n",
    "res_only.output_res_fits_table"
   ]
  }
 ],
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