vignettes/LPR 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 light photomotor response (LPR - continuous convereted to 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 locally installed 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 light photomotor response data, use the `LPRClass` object which can be imported using `from bmdrc.LPRClass import LPRClass`. Next, read the data as a pandas dataframe. \n",
    "\n",
    "Note that the following columns must exist and be specified in the following format: chemical identifier (chemical.id), concentration (conc), plate ID (plate.id), well name (well), time (variable, listed as \"t0\" for the first measurement, \"t1\" for the second, etc), and value (movement in mm). The data must be provided in long format, as wide format is not an acceptable format option for LPR data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
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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>chemical.id</th>\n",
       "      <th>conc</th>\n",
       "      <th>plate.id</th>\n",
       "      <th>well</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20544</td>\n",
       "      <td>H01</td>\n",
       "      <td>t0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20544</td>\n",
       "      <td>H02</td>\n",
       "      <td>t0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20544</td>\n",
       "      <td>H03</td>\n",
       "      <td>t0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20544</td>\n",
       "      <td>H04</td>\n",
       "      <td>t0</td>\n",
       "      <td>1.083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20544</td>\n",
       "      <td>H05</td>\n",
       "      <td>t0</td>\n",
       "      <td>1.608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   chemical.id  conc  plate.id well variable  value\n",
       "0            2   0.0     20544  H01       t0  0.000\n",
       "1            2   0.0     20544  H02       t0    NaN\n",
       "2            2   0.0     20544  H03       t0    NaN\n",
       "3            2   0.0     20544  H04       t0  1.083\n",
       "4            2   0.0     20544  H05       t0  1.608"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"../data/LPR_Long.csv\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following information must be provided to calculate LPR cycles: the cycle_length, the cycle_cooldown, and the starting_cycle value. In this specific instance, cycles are measured in 6 second intervals, so a length of 20 represents 120 second or 2 minutes. Between 2 minute measurement times, there are 1 minute cooldowns. Here, we calculate the difference in the area under the curve (AUC) between dark and light cycles, so specifying whether the first cycle - the starting cycle - is a \"light\" or \"dark\" cycle is important. The other endpoint that is calculated for this data, besides AUC, is MOV, which is the difference in movement between transtion periods. Here we have an example where the cycle_length is 120 seconds (20 sets of 6 second intervals), cycel_cooldown is 60 seconds (10 sets of 6 second intervals), and the first measurement is a \"light\" cycle.\n",
    "\n",
    "![LPR_Image](./LPR_Example.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...defining cycles\n",
      "...calculating AUC values\n",
      "...calculating MOV values\n"
     ]
    }
   ],
   "source": [
    "from bmdrc.LPRClass import LPRClass\n",
    "\n",
    "# Convert the continuous data to dichotomous \n",
    "LPR = LPRClass(\n",
    "    df = pd.read_csv(\"../data/LPR_Long.csv\"),\n",
    "    chemical = \"chemical.id\",\n",
    "    plate = \"plate.id\",\n",
    "    well = \"well\",\n",
    "    concentration = \"conc\",\n",
    "    time = \"variable\",\n",
    "    value = \"value\",\n",
    "    cycle_length = 20.0,\n",
    "    cycle_cooldown = 10.0, \n",
    "    starting_cycle = \"light\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>conc</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>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20544</td>\n",
       "      <td>H01</td>\n",
       "      <td>AUC1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20544</td>\n",
       "      <td>H02</td>\n",
       "      <td>AUC1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20544</td>\n",
       "      <td>H03</td>\n",
       "      <td>AUC1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20544</td>\n",
       "      <td>H04</td>\n",
       "      <td>AUC1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20544</td>\n",
       "      <td>H05</td>\n",
       "      <td>AUC1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   chemical.id  conc  plate.id well endpoint  value\n",
       "0            2   0.0     20544  H01     AUC1    0.0\n",
       "1            2   0.0     20544  H02     AUC1    0.0\n",
       "2            2   0.0     20544  H03     AUC1    0.0\n",
       "3            2   0.0     20544  H04     AUC1    0.0\n",
       "4            2   0.0     20544  H05     AUC1    1.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we can see the dichotomous data, which follows the same format as we've seen for the morphological example in the \"Binary Class Example\" jupyter notebook\n",
    "LPR.df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Module 2: Pre-Processing Modules\n",
    "\n",
    "For a full example of all functions is available in the \"Binary Class Example\" notebook. Here we will cover a few functions that are relevant for LPR data.\n",
    "\n",
    "### LPR Class: Combine Endpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "LPR.combine_and_create_new_endpoints({\"ANY_MOV\": [\"MOV1\", \"MOV2\", \"MOV3\", \"MOV4\"],\n",
    "                                      \"ANY_AUC\": [\"AUC1\", \"AUC2\", \"AUC3\", \"AUC4\"]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Module 3: Filtering Modules\n",
    "\n",
    "For a full example of all functions is available in the \"Binary Class Example\" notebook. Here we will cover a few functions that are relevant for LPR data.\n",
    "\n",
    "### Negative Control Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LPR.filter_negative_control(percentage = 50, apply = True, diagnostic_plot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Minimum Concentration Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LPR.filter_min_concentration(count = 3, apply = True, diagnostic_plot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Score Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "LPR.filter_correlation_score(score = 0.2, diagnostic_plot = True, apply = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Review what is kept\n",
    "\n",
    "Following EPA recommendations of filtering, only two endpoint and chemical combinations are considered for model fitting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ANY_AUC', 'ANY_MOV', 'AUC1', 'AUC2', 'AUC3', 'AUC4', 'MOV1',\n",
       "       'MOV2', 'MOV3', 'MOV4'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See summary statistics as well as benchmark dose calculations \n",
    "LPR.plate_groups[LPR.plate_groups[\"bmdrc.filter\"] == \"Keep\"][\"endpoint\"].unique()"
   ]
  },
  {
   "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": 10,
   "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",
    "LPR.fit_models(gof_threshold = 0.1, aic_threshold = 2, model_selection = \"lowest BMDL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the best fitting curve\n",
    "LPR.response_curve(chemical_name = 2, endpoint_name = \"AUC4\", model = \"log probit\")\n",
    "LPR._2_AUC4_log_probit_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": 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>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>2</td>\n",
       "      <td>ANY_AUC</td>\n",
       "      <td>Quantal Linear</td>\n",
       "      <td>43.035314</td>\n",
       "      <td>21.746308</td>\n",
       "      <td>283.121302</td>\n",
       "      <td>30.662500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.306625</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2 ANY_AUC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>ANY_MOV</td>\n",
       "      <td>Quantal Linear</td>\n",
       "      <td>43.614513</td>\n",
       "      <td>21.947944</td>\n",
       "      <td>286.931749</td>\n",
       "      <td>43.535833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.435358</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2 ANY_MOV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>AUC1</td>\n",
       "      <td>Quantal Linear</td>\n",
       "      <td>31.786734</td>\n",
       "      <td>20.011926</td>\n",
       "      <td>209.118995</td>\n",
       "      <td>26.160000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.261600</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2 AUC1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>AUC2</td>\n",
       "      <td>Gamma</td>\n",
       "      <td>58.931251</td>\n",
       "      <td>6.038799</td>\n",
       "      <td>1637.334617</td>\n",
       "      <td>11.751667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.117517</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2 AUC2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>AUC3</td>\n",
       "      <td>Quantal Linear</td>\n",
       "      <td>97.080887</td>\n",
       "      <td>39.822480</td>\n",
       "      <td>638.677048</td>\n",
       "      <td>11.433333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.114333</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2 AUC3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>AUC4</td>\n",
       "      <td>Log Probit</td>\n",
       "      <td>7.735567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17014.593969</td>\n",
       "      <td>11.158333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.111583</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2 AUC4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>MOV2</td>\n",
       "      <td>Gamma</td>\n",
       "      <td>93.249075</td>\n",
       "      <td>22.171698</td>\n",
       "      <td>145.787871</td>\n",
       "      <td>9.245000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.092450</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2 MOV2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>MOV4</td>\n",
       "      <td>Gamma</td>\n",
       "      <td>1.182246</td>\n",
       "      <td>0.217522</td>\n",
       "      <td>2502.230056</td>\n",
       "      <td>32.092500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.320925</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2 MOV4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>MOV1</td>\n",
       "      <td>Quantal Linear</td>\n",
       "      <td>188.646274</td>\n",
       "      <td>60.947346</td>\n",
       "      <td>1241.068652</td>\n",
       "      <td>11.957500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.119575</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2 MOV1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>MOV3</td>\n",
       "      <td>Quantal Linear</td>\n",
       "      <td>141.421860</td>\n",
       "      <td>63.039286</td>\n",
       "      <td>930.388040</td>\n",
       "      <td>9.411667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.094117</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2 MOV3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Chemical_ID End_Point           Model       BMD10       BMDL         BMD50  \\\n",
       "0           2   ANY_AUC  Quantal Linear   43.035314  21.746308    283.121302   \n",
       "1           2   ANY_MOV  Quantal Linear   43.614513  21.947944    286.931749   \n",
       "2           2      AUC1  Quantal Linear   31.786734  20.011926    209.118995   \n",
       "3           2      AUC2           Gamma   58.931251   6.038799   1637.334617   \n",
       "4           2      AUC3  Quantal Linear   97.080887  39.822480    638.677048   \n",
       "5           2      AUC4      Log Probit    7.735567        NaN  17014.593969   \n",
       "7           2      MOV2           Gamma   93.249075  22.171698    145.787871   \n",
       "9           2      MOV4           Gamma    1.182246   0.217522   2502.230056   \n",
       "6           2      MOV1  Quantal Linear  188.646274  60.947346   1241.068652   \n",
       "8           2      MOV3  Quantal Linear  141.421860  63.039286    930.388040   \n",
       "\n",
       "         AUC  Min_Dose  Max_Dose  AUC_Norm  DataQC_Flag  BMD_Analysis_Flag  \\\n",
       "0  30.662500       0.0     100.0  0.306625            2                  1   \n",
       "1  43.535833       0.0     100.0  0.435358            2                  1   \n",
       "2  26.160000       0.0     100.0  0.261600            2                  1   \n",
       "3  11.751667       0.0     100.0  0.117517            2                  1   \n",
       "4  11.433333       0.0     100.0  0.114333            2                  1   \n",
       "5  11.158333       0.0     100.0  0.111583            2                  1   \n",
       "7   9.245000       0.0     100.0  0.092450            2                  1   \n",
       "9  32.092500       0.0     100.0  0.320925            2                  1   \n",
       "6  11.957500       0.0     100.0  0.119575            2                  0   \n",
       "8   9.411667       0.0     100.0  0.094117            4                  0   \n",
       "\n",
       "   BMD10_Flag  BMD50_Flag bmdrc.Endpoint.ID  \n",
       "0           1           0         2 ANY_AUC  \n",
       "1           1           0         2 ANY_MOV  \n",
       "2           1           0            2 AUC1  \n",
       "3           1           0            2 AUC2  \n",
       "4           1           0            2 AUC3  \n",
       "5           1           0            2 AUC4  \n",
       "7           1           0            2 MOV2  \n",
       "9           1           0            2 MOV4  \n",
       "6           0           0            2 MOV1  \n",
       "8           0           0            2 MOV3  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LPR.output_benchmark_dose()\n",
    "LPR.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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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>2</td>\n",
       "      <td>ANY_AUC</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2 ANY_AUC</td>\n",
       "      <td>11.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.180045</td>\n",
       "      <td>0.468563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>ANY_AUC</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2 ANY_AUC</td>\n",
       "      <td>10.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.158483</td>\n",
       "      <td>0.439925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>ANY_AUC</td>\n",
       "      <td>6.80</td>\n",
       "      <td>2 ANY_AUC</td>\n",
       "      <td>15.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.271406</td>\n",
       "      <td>0.577997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>ANY_AUC</td>\n",
       "      <td>14.70</td>\n",
       "      <td>2 ANY_AUC</td>\n",
       "      <td>15.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.271406</td>\n",
       "      <td>0.577997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>ANY_AUC</td>\n",
       "      <td>31.60</td>\n",
       "      <td>2 ANY_AUC</td>\n",
       "      <td>13.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.224756</td>\n",
       "      <td>0.524249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2</td>\n",
       "      <td>MOV4</td>\n",
       "      <td>14.70</td>\n",
       "      <td>2 MOV4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.117163</td>\n",
       "      <td>0.380847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2</td>\n",
       "      <td>MOV4</td>\n",
       "      <td>31.60</td>\n",
       "      <td>2 MOV4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.180045</td>\n",
       "      <td>0.468563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2</td>\n",
       "      <td>MOV4</td>\n",
       "      <td>56.20</td>\n",
       "      <td>2 MOV4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.180045</td>\n",
       "      <td>0.468563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2</td>\n",
       "      <td>MOV4</td>\n",
       "      <td>75.00</td>\n",
       "      <td>2 MOV4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.137505</td>\n",
       "      <td>0.410705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2</td>\n",
       "      <td>MOV4</td>\n",
       "      <td>100.00</td>\n",
       "      <td>2 MOV4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.180045</td>\n",
       "      <td>0.468563</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Chemical_ID End_Point    Dose bmdrc.Endpoint.ID  num.affected  num.nonna  \\\n",
       "0             2   ANY_AUC    0.00         2 ANY_AUC          11.0         36   \n",
       "1             2   ANY_AUC    2.16         2 ANY_AUC          10.0         36   \n",
       "2             2   ANY_AUC    6.80         2 ANY_AUC          15.0         36   \n",
       "3             2   ANY_AUC   14.70         2 ANY_AUC          15.0         36   \n",
       "4             2   ANY_AUC   31.60         2 ANY_AUC          13.0         36   \n",
       "..          ...       ...     ...               ...           ...        ...   \n",
       "75            2      MOV4   14.70            2 MOV4           8.0         36   \n",
       "76            2      MOV4   31.60            2 MOV4          11.0         36   \n",
       "77            2      MOV4   56.20            2 MOV4          11.0         36   \n",
       "78            2      MOV4   75.00            2 MOV4           9.0         36   \n",
       "79            2      MOV4  100.00            2 MOV4          11.0         36   \n",
       "\n",
       "       CI_Lo     CI_Hi  \n",
       "0   0.180045  0.468563  \n",
       "1   0.158483  0.439925  \n",
       "2   0.271406  0.577997  \n",
       "3   0.271406  0.577997  \n",
       "4   0.224756  0.524249  \n",
       "..       ...       ...  \n",
       "75  0.117163  0.380847  \n",
       "76  0.180045  0.468563  \n",
       "77  0.180045  0.468563  \n",
       "78  0.137505  0.410705  \n",
       "79  0.180045  0.468563  \n",
       "\n",
       "[80 rows x 8 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LPR.output_dose_table()\n",
    "LPR.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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>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>2</td>\n",
       "      <td>ANY_AUC</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.327847</td>\n",
       "      <td>2 ANY_AUC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>ANY_AUC</td>\n",
       "      <td>0.2400</td>\n",
       "      <td>0.328242</td>\n",
       "      <td>2 ANY_AUC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>ANY_AUC</td>\n",
       "      <td>0.4800</td>\n",
       "      <td>0.328637</td>\n",
       "      <td>2 ANY_AUC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>ANY_AUC</td>\n",
       "      <td>0.7200</td>\n",
       "      <td>0.329031</td>\n",
       "      <td>2 ANY_AUC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>ANY_AUC</td>\n",
       "      <td>0.9600</td>\n",
       "      <td>0.329425</td>\n",
       "      <td>2 ANY_AUC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2</td>\n",
       "      <td>MOV4</td>\n",
       "      <td>88.8889</td>\n",
       "      <td>0.313157</td>\n",
       "      <td>2 MOV4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2</td>\n",
       "      <td>MOV4</td>\n",
       "      <td>91.6667</td>\n",
       "      <td>0.314634</td>\n",
       "      <td>2 MOV4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2</td>\n",
       "      <td>MOV4</td>\n",
       "      <td>94.4444</td>\n",
       "      <td>0.316075</td>\n",
       "      <td>2 MOV4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2</td>\n",
       "      <td>MOV4</td>\n",
       "      <td>97.2222</td>\n",
       "      <td>0.317484</td>\n",
       "      <td>2 MOV4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2</td>\n",
       "      <td>MOV4</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>0.318861</td>\n",
       "      <td>2 MOV4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>640 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Chemical_ID End_Point    X_vals    Y_vals bmdrc.Endpoint.ID\n",
       "0            2   ANY_AUC    0.0000  0.327847         2 ANY_AUC\n",
       "1            2   ANY_AUC    0.2400  0.328242         2 ANY_AUC\n",
       "2            2   ANY_AUC    0.4800  0.328637         2 ANY_AUC\n",
       "3            2   ANY_AUC    0.7200  0.329031         2 ANY_AUC\n",
       "4            2   ANY_AUC    0.9600  0.329425         2 ANY_AUC\n",
       "..         ...       ...       ...       ...               ...\n",
       "59           2      MOV4   88.8889  0.313157            2 MOV4\n",
       "60           2      MOV4   91.6667  0.314634            2 MOV4\n",
       "61           2      MOV4   94.4444  0.316075            2 MOV4\n",
       "62           2      MOV4   97.2222  0.317484            2 MOV4\n",
       "63           2      MOV4  100.0000  0.318861            2 MOV4\n",
       "\n",
       "[640 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LPR.output_fits_table()\n",
    "LPR.output_res_fits_table"
   ]
  }
 ],
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