rnelsonchem/demo.ipynb

## demo.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b0f301fd-4387-4c5f-88ab-254d23c320eb",
   "metadata": {},
   "source": [
    "# tl;dr\n",
    "\n",
    "This code snippet is a minimal, self-contained example of running a cost model and saving the results as a dynamic Excel file called 'demo output.xlsx'. In this case, the reactions and materials are defined in a single file called 'demo costing.xlsx', which can be downloaded from the [costcalc website](https://costcalc.rnelsonchem.com/). This file needs to reside in the same directory as this notebook or a Python file containing the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "87143c10-92ea-455f-bec0-4ec0ebfbd293",
   "metadata": {},
   "outputs": [],
   "source": [
    "from costcalc import ExcelCost\n",
    "\n",
    "model = ExcelCost(materials_file='demo costing.xlsx', materials_sheet='Materials',\n",
    "                      rxn_file='demo costing.xlsx', rxn_sheet='Bromine Route',\n",
    "                      final_prod='Product')\n",
    "\n",
    "model.calc_cost()\n",
    "\n",
    "model.excel('demo output.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de2fc60a-d7d1-4518-a116-dbd6710b83c9",
   "metadata": {},
   "source": [
    "# Installation\n",
    "\n",
    "The `costcalc2` package can be installed using either `pip`\n",
    "\n",
    "    $ pip install costcalc2\n",
    "    \n",
    "or `conda`\n",
    "\n",
    "    $ conda install -c rnelsonchem costcalc2\n"
   ]
  },
  {
   "attachments": {
    "7c319c55-f66e-4027-b52b-692715ea8407.png": {
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    }
   },
   "cell_type": "markdown",
   "id": "2b78b7ee-3589-4c9b-9568-34fceffcab63",
   "metadata": {},
   "source": [
    "# High-level Design\n",
    "\n",
    "![image.png](attachment:7c319c55-f66e-4027-b52b-692715ea8407.png)\n",
    "\n",
    "A high-level representation of the `costcalc` internals is shown above. The central portion of this figure is a high-level representation of the core operation of the `costcalc` module. There is an internal costing algorithm that takes two tables as inputs and exports two types of costing tables. All the tables are Pandas DataFrames, and they require a minimum number of columns with the names given in the `constants.py` module, which can be found in the costcalc2 `src` directory. The details of how these tables need to be populated is not provided in this document. You can get a sense for how this is done by looking at the demo Excel file available from the costcalc web app and also at the test files provided in the `tests/data` costcalc2 directory. Below is a general description of the information in these tables.\n",
    "\n",
    "* Reactions table: The reaction information for the synthetic route, such as compound names, equivalents, solvent utilization, etc. \n",
    "* Materials table: Information about the materials used in the route, such as MW, density, and cost (as $/kg). At a minimum, this table must contain all of the materials in the Reactions table; however, it can contain any number of additional compounds as well.\n",
    "* Numerical display table: The combined reaction and material information as well as the raw material (RM) costing information as *numbers* (i.e. as floating point values). This is most useful to display purposes, for example.\n",
    "* Excel formula table: This table provides the same information as the numerical display table; however, the costing information is provided as Excel-formated *equation strings*. This table can be saved to disk, and when this file is opened with Excel, it will be fully dynamic (i.e. changing values will cause the entire cost model to be recalculated). \n",
    "\n",
    "Execution of the tasks above is handled by the `CoreCost` object class, which is defined in the `core.py` costcalc module. However, using this class on its own requires a system that can provide the necessary input tables (DataFrames), so as shown in the figure above, \"converters\" are needed to take reaction/materials information from other sources and convert them into Pandas DataFrames. Two converter classes for common use cases are provided in the `frontends.py` costcalc module:\n",
    "\n",
    "* `ExcelCost` : This class reads reaction and materials information from one or more Excel/csv files.\n",
    "* `ColabCost` : This class reads reaction and materials information from one or more Google sheets. This is meant to be used in a Google Colaboratory notebook running in Google Drive.\n",
    "\n",
    "Both of these are subclasses of the `CoreCost` class, so they provide all of the methods/attributes from this base class. \n",
    "\n",
    "# Load Route Information From Excel File\n",
    "\n",
    "In this code block, the an `ExcelCost` instance is being created from a single Excel file called 'demo costing.xlsx'. The `*_sheet` arguments are defining the sheet names in the Excel file that conain the necessary information. And `final_prod` is simply the name of the final product of the route. Note: The warning message is not important. It is simply saying that the input Excel file contains data validation, which is not supported by the Python Excel processing module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "318912b7-9d57-4023-a849-73fde9f2cc8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from costcalc import ExcelCost\n",
    "\n",
    "model = ExcelCost(materials_file='demo costing.xlsx', materials_sheet='Materials',\n",
    "                      rxn_file='demo costing.xlsx', rxn_sheet='Bromine Route',\n",
    "                      final_prod='Product')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e0fa255-71d9-4826-88fa-3436d5b44710",
   "metadata": {},
   "source": [
    "# Input Tables and Full Route Table\n",
    "\n",
    "\n",
    "The \"converter\" used in our model creation in the previous cell uses an Excel file reader to extract the necessary route and materials information. This information is converted into the Pandas DataFrames named `rxns` and `materials`, which are displayed below. The `rxns` DataFrame has two new columns: \"Cost step\" and \"OPEX\". The former is a reaction connectivity matrix that determines how the costing will be run. The latter is an operational expenses column, which is empty. (This is an optional column in the input file.) A third table - `fulldata` - is automatically created by combining the `rxns` and `materials` tables using a SQL-style joining operation, which matches the correct materials information with the corresponding compounds in the reactions table. (Unused materials are ignored.) Several costing related columns have also been added, but are not immediately populated with data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "35229e4d-a43c-4723-aa58-4efad890141a",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>Intermediate A</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>Reagent C</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>Tetrahydrofuran (THF)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>Product</td>\n",
       "      <td>0.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Step               Compound  Equiv  Volumes  Recycle  Notes Cost step  OPEX\n",
       "0    1      Starting Material   1.00      NaN      NaN    NaN       NaN   NaN\n",
       "1    1                Bromine   2.50      NaN      NaN    NaN       NaN   NaN\n",
       "2    1  Dichloromethane (DCM)    NaN     10.0     0.75    NaN       NaN   NaN\n",
       "3    1         Intermediate A   0.75      NaN      NaN    NaN         1   NaN\n",
       "5    2         Intermediate A   1.00      NaN      NaN    NaN         1   NaN\n",
       "6    2              Reagent C   1.00      NaN      NaN    NaN       NaN   NaN\n",
       "7    2  Tetrahydrofuran (THF)    NaN      5.0     0.75    NaN       NaN   NaN\n",
       "8    2                Product   0.92      NaN      NaN    NaN         2   NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.rxns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bdf71ed4-8256-4cc7-bad8-8a71b209b15c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound</th>\n",
       "      <th>MW</th>\n",
       "      <th>Density</th>\n",
       "      <th>$/kg</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Starting Material</td>\n",
       "      <td>151.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dichloromethane (DCM)</td>\n",
       "      <td>84.93</td>\n",
       "      <td>1.325</td>\n",
       "      <td>1.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tetrahydrofuran (THF)</td>\n",
       "      <td>72.11</td>\n",
       "      <td>0.889</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bromine</td>\n",
       "      <td>159.81</td>\n",
       "      <td>3.119</td>\n",
       "      <td>5.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Reagent C</td>\n",
       "      <td>222.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Same reagent transforms both Cl and Br interme...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sulfuryl Chloride</td>\n",
       "      <td>134.97</td>\n",
       "      <td>1.665</td>\n",
       "      <td>2.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Acetonitrile (MeCN)</td>\n",
       "      <td>41.05</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.75</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Intermediate A</td>\n",
       "      <td>230.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.00</td>\n",
       "      <td>SM + 1 bromine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Product</td>\n",
       "      <td>183.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SM + 2 oxygen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Intermediate B</td>\n",
       "      <td>186.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SM + 1 chlorine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Compound      MW  Density    $/kg   \n",
       "0       Starting Material  151.00      NaN   15.00  \\\n",
       "1   Dichloromethane (DCM)   84.93    1.325    1.50   \n",
       "2   Tetrahydrofuran (THF)   72.11    0.889    1.00   \n",
       "3                 Bromine  159.81    3.119    5.00   \n",
       "4               Reagent C  222.00      NaN   10.00   \n",
       "5       Sulfuryl Chloride  134.97    1.665    2.00   \n",
       "6     Acetonitrile (MeCN)   41.05    0.786    0.75   \n",
       "8          Intermediate A  230.90      NaN  100.00   \n",
       "9                 Product  183.00      NaN     NaN   \n",
       "10         Intermediate B  186.45      NaN     NaN   \n",
       "\n",
       "                                                Notes  \n",
       "0                                                 NaN  \n",
       "1                                                 NaN  \n",
       "2                                                 NaN  \n",
       "3                                                 NaN  \n",
       "4   Same reagent transforms both Cl and Br interme...  \n",
       "5                                                 NaN  \n",
       "6                                                 NaN  \n",
       "8                                      SM + 1 bromine  \n",
       "9                                       SM + 2 oxygen  \n",
       "10                                    SM + 1 chlorine  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
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    "model.materials"
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   "execution_count": 5,
   "id": "ab2bff3a-5076-4dbb-b8c6-ec06cbe646f2",
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   "outputs": [
    {
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       "      <th>MW</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
       "      <th>Starting Material</th>\n",
       "      <td>151.00</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bromine</th>\n",
       "      <td>159.81</td>\n",
       "      <td>3.119</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dichloromethane (DCM)</th>\n",
       "      <td>84.93</td>\n",
       "      <td>1.325</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intermediate A</th>\n",
       "      <td>230.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SM + 1 bromine</td>\n",
       "      <td>0.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2</th>\n",
       "      <th>Intermediate A</th>\n",
       "      <td>230.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SM + 1 bromine</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reagent C</th>\n",
       "      <td>222.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Same reagent transforms both Cl and Br interme...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tetrahydrofuran (THF)</th>\n",
       "      <td>72.11</td>\n",
       "      <td>0.889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <td>183.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SM + 2 oxygen</td>\n",
       "      <td>0.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                MW  Density  $/kg   \n",
       "Step Compound                                       \n",
       "1    Starting Material      151.00      NaN  15.0  \\\n",
       "     Bromine                159.81    3.119   5.0   \n",
       "     Dichloromethane (DCM)   84.93    1.325   1.5   \n",
       "     Intermediate A         230.90      NaN   NaN   \n",
       "2    Intermediate A         230.90      NaN   NaN   \n",
       "     Reagent C              222.00      NaN  10.0   \n",
       "     Tetrahydrofuran (THF)   72.11    0.889   1.0   \n",
       "     Product                183.00      NaN   NaN   \n",
       "\n",
       "                                                               Material Notes   \n",
       "Step Compound                                                                   \n",
       "1    Starting Material                                                    NaN  \\\n",
       "     Bromine                                                              NaN   \n",
       "     Dichloromethane (DCM)                                                NaN   \n",
       "     Intermediate A                                            SM + 1 bromine   \n",
       "2    Intermediate A                                            SM + 1 bromine   \n",
       "     Reagent C              Same reagent transforms both Cl and Br interme...   \n",
       "     Tetrahydrofuran (THF)                                                NaN   \n",
       "     Product                                                    SM + 2 oxygen   \n",
       "\n",
       "                            Equiv  Volumes  Recycle Cost step  OPEX   \n",
       "Step Compound                                                         \n",
       "1    Starting Material       1.00      NaN      NaN       NaN   NaN  \\\n",
       "     Bromine                 2.50      NaN      NaN       NaN   NaN   \n",
       "     Dichloromethane (DCM)    NaN     10.0     0.75       NaN   NaN   \n",
       "     Intermediate A          0.75      NaN      NaN         1   NaN   \n",
       "2    Intermediate A          1.00      NaN      NaN         1   NaN   \n",
       "     Reagent C               1.00      NaN      NaN       NaN   NaN   \n",
       "     Tetrahydrofuran (THF)    NaN      5.0     0.75       NaN   NaN   \n",
       "     Product                 0.92      NaN      NaN         2   NaN   \n",
       "\n",
       "                            Reaction Notes  kg/kg rxn  RM cost/kg rxn   \n",
       "Step Compound                                                           \n",
       "1    Starting Material                 NaN        NaN             NaN  \\\n",
       "     Bromine                           NaN        NaN             NaN   \n",
       "     Dichloromethane (DCM)             NaN        NaN             NaN   \n",
       "     Intermediate A                    NaN        NaN             NaN   \n",
       "2    Intermediate A                    NaN        NaN             NaN   \n",
       "     Reagent C                         NaN        NaN             NaN   \n",
       "     Tetrahydrofuran (THF)             NaN        NaN             NaN   \n",
       "     Product                           NaN        NaN             NaN   \n",
       "\n",
       "                            % RM cost/kg rxn  kg/kg prod  RM cost/kg prod   \n",
       "Step Compound                                                               \n",
       "1    Starting Material                   NaN         NaN              NaN  \\\n",
       "     Bromine                             NaN         NaN              NaN   \n",
       "     Dichloromethane (DCM)               NaN         NaN              NaN   \n",
       "     Intermediate A                      NaN         NaN              NaN   \n",
       "2    Intermediate A                      NaN         NaN              NaN   \n",
       "     Reagent C                           NaN         NaN              NaN   \n",
       "     Tetrahydrofuran (THF)               NaN         NaN              NaN   \n",
       "     Product                             NaN         NaN              NaN   \n",
       "\n",
       "                            % RM cost/kg prod  \n",
       "Step Compound                                  \n",
       "1    Starting Material                    NaN  \n",
       "     Bromine                              NaN  \n",
       "     Dichloromethane (DCM)                NaN  \n",
       "     Intermediate A                       NaN  \n",
       "2    Intermediate A                       NaN  \n",
       "     Reagent C                            NaN  \n",
       "     Tetrahydrofuran (THF)                NaN  \n",
       "     Product                              NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
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  },
  {
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   "source": [
    "# Running the Costing\n",
    "\n",
    "The costing is executed by calling the `calc_cost` method. This populates the `fulldata` table with the resulting values. A `pmi` table is also created for assessing route waste, but this table is not shown here."
   ]
  },
  {
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   "execution_count": 6,
   "id": "1d849bf2-d6de-4216-8774-ce6a321fb398",
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   "outputs": [
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       "      <th>Step</th>\n",
       "      <th>Compound</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
       "      <th>Starting Material</th>\n",
       "      <td>151.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.871950</td>\n",
       "      <td>13.079255</td>\n",
       "      <td>45.183370</td>\n",
       "      <td>1.195850</td>\n",
       "      <td>17.937752</td>\n",
       "      <td>32.967767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bromine</th>\n",
       "      <td>159.81</td>\n",
       "      <td>3.119</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.307059</td>\n",
       "      <td>11.535297</td>\n",
       "      <td>39.849638</td>\n",
       "      <td>3.164053</td>\n",
       "      <td>15.820266</td>\n",
       "      <td>29.076042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dichloromethane (DCM)</th>\n",
       "      <td>84.93</td>\n",
       "      <td>1.325</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.888335</td>\n",
       "      <td>4.332503</td>\n",
       "      <td>14.966991</td>\n",
       "      <td>3.961254</td>\n",
       "      <td>5.941880</td>\n",
       "      <td>10.920573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intermediate A</th>\n",
       "      <td>230.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.947055</td>\n",
       "      <td>SM + 1 bromine</td>\n",
       "      <td>0.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.947055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2</th>\n",
       "      <th>Intermediate A</th>\n",
       "      <td>230.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.947055</td>\n",
       "      <td>SM + 1 bromine</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.371466</td>\n",
       "      <td>39.699899</td>\n",
       "      <td>72.964383</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reagent C</th>\n",
       "      <td>222.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>Same reagent transforms both Cl and Br interme...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.318603</td>\n",
       "      <td>13.186030</td>\n",
       "      <td>24.234584</td>\n",
       "      <td>1.318603</td>\n",
       "      <td>13.186030</td>\n",
       "      <td>24.234584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tetrahydrofuran (THF)</th>\n",
       "      <td>72.11</td>\n",
       "      <td>0.889</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.524041</td>\n",
       "      <td>1.524041</td>\n",
       "      <td>2.801033</td>\n",
       "      <td>1.524041</td>\n",
       "      <td>1.524041</td>\n",
       "      <td>2.801033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <td>183.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.409970</td>\n",
       "      <td>SM + 2 oxygen</td>\n",
       "      <td>0.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.409970</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                MW  Density       $/kg   \n",
       "Step Compound                                            \n",
       "1    Starting Material      151.00      NaN  15.000000  \\\n",
       "     Bromine                159.81    3.119   5.000000   \n",
       "     Dichloromethane (DCM)   84.93    1.325   1.500000   \n",
       "     Intermediate A         230.90      NaN  28.947055   \n",
       "2    Intermediate A         230.90      NaN  28.947055   \n",
       "     Reagent C              222.00      NaN  10.000000   \n",
       "     Tetrahydrofuran (THF)   72.11    0.889   1.000000   \n",
       "     Product                183.00      NaN  54.409970   \n",
       "\n",
       "                                                               Material Notes   \n",
       "Step Compound                                                                   \n",
       "1    Starting Material                                                    NaN  \\\n",
       "     Bromine                                                              NaN   \n",
       "     Dichloromethane (DCM)                                                NaN   \n",
       "     Intermediate A                                            SM + 1 bromine   \n",
       "2    Intermediate A                                            SM + 1 bromine   \n",
       "     Reagent C              Same reagent transforms both Cl and Br interme...   \n",
       "     Tetrahydrofuran (THF)                                                NaN   \n",
       "     Product                                                    SM + 2 oxygen   \n",
       "\n",
       "                            Equiv  Volumes  Recycle Cost step  OPEX   \n",
       "Step Compound                                                         \n",
       "1    Starting Material       1.00      NaN      NaN       NaN   NaN  \\\n",
       "     Bromine                 2.50      NaN      NaN       NaN   NaN   \n",
       "     Dichloromethane (DCM)    NaN     10.0     0.75       NaN   NaN   \n",
       "     Intermediate A          0.75      NaN      NaN         1   NaN   \n",
       "2    Intermediate A          1.00      NaN      NaN         1   NaN   \n",
       "     Reagent C               1.00      NaN      NaN       NaN   NaN   \n",
       "     Tetrahydrofuran (THF)    NaN      5.0     0.75       NaN   NaN   \n",
       "     Product                 0.92      NaN      NaN         2   NaN   \n",
       "\n",
       "                            Reaction Notes  kg/kg rxn  RM cost/kg rxn   \n",
       "Step Compound                                                           \n",
       "1    Starting Material                 NaN   0.871950       13.079255  \\\n",
       "     Bromine                           NaN   2.307059       11.535297   \n",
       "     Dichloromethane (DCM)             NaN   2.888335        4.332503   \n",
       "     Intermediate A                    NaN        NaN       28.947055   \n",
       "2    Intermediate A                    NaN   1.371466       39.699899   \n",
       "     Reagent C                         NaN   1.318603       13.186030   \n",
       "     Tetrahydrofuran (THF)             NaN   1.524041        1.524041   \n",
       "     Product                           NaN        NaN       54.409970   \n",
       "\n",
       "                            % RM cost/kg rxn  kg/kg prod  RM cost/kg prod   \n",
       "Step Compound                                                               \n",
       "1    Starting Material             45.183370    1.195850        17.937752  \\\n",
       "     Bromine                       39.849638    3.164053        15.820266   \n",
       "     Dichloromethane (DCM)         14.966991    3.961254         5.941880   \n",
       "     Intermediate A                      NaN         NaN              NaN   \n",
       "2    Intermediate A                72.964383         NaN              NaN   \n",
       "     Reagent C                     24.234584    1.318603        13.186030   \n",
       "     Tetrahydrofuran (THF)          2.801033    1.524041         1.524041   \n",
       "     Product                             NaN         NaN              NaN   \n",
       "\n",
       "                            % RM cost/kg prod  \n",
       "Step Compound                                  \n",
       "1    Starting Material              32.967767  \n",
       "     Bromine                        29.076042  \n",
       "     Dichloromethane (DCM)          10.920573  \n",
       "     Intermediate A                       NaN  \n",
       "2    Intermediate A                       NaN  \n",
       "     Reagent C                      24.234584  \n",
       "     Tetrahydrofuran (THF)           2.801033  \n",
       "     Product                              NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.calc_cost()\n",
    "model.fulldata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e210bfd6-b9bc-42a4-bfa0-9a8f318b7222",
   "metadata": {},
   "source": [
    "# Formatted Display w PMI and Excel Output\n",
    "\n",
    "The `fulldata` table is not well formated for printing to the screen, so a `results` method has been defined to provide a more user-friendly display of the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a18ad2c4-cff6-4ebf-b08f-a86659b6dfc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As of 2023-05-03 21:54 UTC --\n",
      "The final cost of Product is $54.41/kg.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>$/kg</th>\n",
       "      <th>Equiv</th>\n",
       "      <th>Volumes</th>\n",
       "      <th>Recycle</th>\n",
       "      <th>kg/kg rxn</th>\n",
       "      <th>RM cost/kg rxn</th>\n",
       "      <th>% RM cost/kg rxn</th>\n",
       "      <th>kg/kg prod</th>\n",
       "      <th>RM cost/kg prod</th>\n",
       "      <th>% RM cost/kg prod</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Step</th>\n",
       "      <th>Compound</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>Starting Material</th>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.87</td>\n",
       "      <td>13.08</td>\n",
       "      <td>45.18</td>\n",
       "      <td>1.2</td>\n",
       "      <td>17.94</td>\n",
       "      <td>32.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bromine</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>2.31</td>\n",
       "      <td>11.54</td>\n",
       "      <td>39.85</td>\n",
       "      <td>3.16</td>\n",
       "      <td>15.82</td>\n",
       "      <td>29.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dichloromethane (DCM)</th>\n",
       "      <td>1.5</td>\n",
       "      <td>-</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4.33</td>\n",
       "      <td>14.97</td>\n",
       "      <td>3.96</td>\n",
       "      <td>5.94</td>\n",
       "      <td>10.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intermediate A</th>\n",
       "      <td>28.95</td>\n",
       "      <td>0.75</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>28.95</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>*Step 1 PMI</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>6.07</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">2</th>\n",
       "      <th>Intermediate A</th>\n",
       "      <td>28.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1.37</td>\n",
       "      <td>39.7</td>\n",
       "      <td>72.96</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reagent C</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1.32</td>\n",
       "      <td>13.19</td>\n",
       "      <td>24.23</td>\n",
       "      <td>1.32</td>\n",
       "      <td>13.19</td>\n",
       "      <td>24.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tetrahydrofuran (THF)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.52</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.52</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <td>54.41</td>\n",
       "      <td>0.92</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>54.41</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>*Step 2 PMI</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>4.21</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>**Full Route PMI</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>11.16</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             $/kg Equiv Volumes Recycle kg/kg rxn   \n",
       "Step Compound                                                       \n",
       "1    Starting Material       15.0   1.0       -       -      0.87  \\\n",
       "     Bromine                  5.0   2.5       -       -      2.31   \n",
       "     Dichloromethane (DCM)    1.5     -    10.0    0.75      2.89   \n",
       "     Intermediate A         28.95  0.75       -       -         -   \n",
       "     *Step 1 PMI                -     -       -       -      6.07   \n",
       "2    Intermediate A         28.95   1.0       -       -      1.37   \n",
       "     Reagent C               10.0   1.0       -       -      1.32   \n",
       "     Tetrahydrofuran (THF)    1.0     -     5.0    0.75      1.52   \n",
       "     Product                54.41  0.92       -       -         -   \n",
       "     *Step 2 PMI                -     -       -       -      4.21   \n",
       "     **Full Route PMI           -     -       -       -         -   \n",
       "\n",
       "                           RM cost/kg rxn % RM cost/kg rxn kg/kg prod   \n",
       "Step Compound                                                           \n",
       "1    Starting Material              13.08            45.18        1.2  \\\n",
       "     Bromine                        11.54            39.85       3.16   \n",
       "     Dichloromethane (DCM)           4.33            14.97       3.96   \n",
       "     Intermediate A                 28.95                -          -   \n",
       "     *Step 1 PMI                        -                -          -   \n",
       "2    Intermediate A                  39.7            72.96          -   \n",
       "     Reagent C                      13.19            24.23       1.32   \n",
       "     Tetrahydrofuran (THF)           1.52              2.8       1.52   \n",
       "     Product                        54.41                -          -   \n",
       "     *Step 2 PMI                        -                -          -   \n",
       "     **Full Route PMI                   -                -      11.16   \n",
       "\n",
       "                           RM cost/kg prod % RM cost/kg prod  \n",
       "Step Compound                                                 \n",
       "1    Starting Material               17.94             32.97  \n",
       "     Bromine                         15.82             29.08  \n",
       "     Dichloromethane (DCM)            5.94             10.92  \n",
       "     Intermediate A                      -                 -  \n",
       "     *Step 1 PMI                         -                 -  \n",
       "2    Intermediate A                      -                 -  \n",
       "     Reagent C                       13.19             24.23  \n",
       "     Tetrahydrofuran (THF)            1.52               2.8  \n",
       "     Product                             -                 -  \n",
       "     *Step 2 PMI                         -                 -  \n",
       "     **Full Route PMI                    -                 -  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.results()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcc505ae-7134-4d66-b108-73f97b7b6cb0",
   "metadata": {},
   "source": [
    "To save these results to disk as an Excel file, the `excel` method can be used. This method requires a string defining the desired name of the outupt file. In this case, it will be 'demo output.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d0a3807d-5f46-465c-837d-8711d1f11ec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.excel('demo output.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ffe5a8e-3dfc-4d7e-a3f9-80e8740522a2",
   "metadata": {},
   "source": [
    "# Costing Alternative Scenarios\n",
    "\n",
    "The costing classes also have a `value_mod` method, which can be used to change specific values to create alternative costing scenarios. This method can be called multiple times to set several different values. The `calc_cost` method must be used after any `value_mod` call(s) to cost the alternative model. Value modifications occur in the `fulldata` table; the original model, as defined in the `rxns` and `materials` tables, is retained. (See below for a method for restoring the system back to the starting values.) Note: if multiple instances of the same compound appear in the same reaction, then setting a value for that compound will affect all of the compounds equally. There is no way to self-select just one of many.\n",
    "\n",
    "In the code below, the price of the compound named \"Starting Material\" is being set to \\\\$5/kg. It was originally \\\\$15/kg. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fbde6839-36b6-403c-9fd7-0694d07f06a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As of 2023-05-03 21:54 UTC --\n",
      "The final cost of Product is $42.45/kg.\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>$/kg</th>\n",
       "      <th>Equiv</th>\n",
       "      <th>Volumes</th>\n",
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       "      <th>RM cost/kg prod</th>\n",
       "      <th>% RM cost/kg prod</th>\n",
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       "    <tr>\n",
       "      <th>Step</th>\n",
       "      <th>Compound</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>Starting Material</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.87</td>\n",
       "      <td>4.36</td>\n",
       "      <td>21.55</td>\n",
       "      <td>1.2</td>\n",
       "      <td>5.98</td>\n",
       "      <td>14.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bromine</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>2.31</td>\n",
       "      <td>11.54</td>\n",
       "      <td>57.03</td>\n",
       "      <td>3.16</td>\n",
       "      <td>15.82</td>\n",
       "      <td>37.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dichloromethane (DCM)</th>\n",
       "      <td>1.5</td>\n",
       "      <td>-</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4.33</td>\n",
       "      <td>21.42</td>\n",
       "      <td>3.96</td>\n",
       "      <td>5.94</td>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
       "      <th>Intermediate A</th>\n",
       "      <td>20.23</td>\n",
       "      <td>0.75</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>20.23</td>\n",
       "      <td>-</td>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>*Step 1 PMI</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>6.07</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">2</th>\n",
       "      <th>Intermediate A</th>\n",
       "      <td>20.23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1.37</td>\n",
       "      <td>27.74</td>\n",
       "      <td>65.35</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reagent C</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1.32</td>\n",
       "      <td>13.19</td>\n",
       "      <td>31.06</td>\n",
       "      <td>1.32</td>\n",
       "      <td>13.19</td>\n",
       "      <td>31.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tetrahydrofuran (THF)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.52</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.52</td>\n",
       "      <td>3.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <td>42.45</td>\n",
       "      <td>0.92</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>42.45</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>*Step 2 PMI</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>4.21</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>**Full Route PMI</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>11.16</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "                             $/kg Equiv Volumes Recycle kg/kg rxn   \n",
       "Step Compound                                                       \n",
       "1    Starting Material        5.0   1.0       -       -      0.87  \\\n",
       "     Bromine                  5.0   2.5       -       -      2.31   \n",
       "     Dichloromethane (DCM)    1.5     -    10.0    0.75      2.89   \n",
       "     Intermediate A         20.23  0.75       -       -         -   \n",
       "     *Step 1 PMI                -     -       -       -      6.07   \n",
       "2    Intermediate A         20.23   1.0       -       -      1.37   \n",
       "     Reagent C               10.0   1.0       -       -      1.32   \n",
       "     Tetrahydrofuran (THF)    1.0     -     5.0    0.75      1.52   \n",
       "     Product                42.45  0.92       -       -         -   \n",
       "     *Step 2 PMI                -     -       -       -      4.21   \n",
       "     **Full Route PMI           -     -       -       -         -   \n",
       "\n",
       "                           RM cost/kg rxn % RM cost/kg rxn kg/kg prod   \n",
       "Step Compound                                                           \n",
       "1    Starting Material               4.36            21.55        1.2  \\\n",
       "     Bromine                        11.54            57.03       3.16   \n",
       "     Dichloromethane (DCM)           4.33            21.42       3.96   \n",
       "     Intermediate A                 20.23                -          -   \n",
       "     *Step 1 PMI                        -                -          -   \n",
       "2    Intermediate A                 27.74            65.35          -   \n",
       "     Reagent C                      13.19            31.06       1.32   \n",
       "     Tetrahydrofuran (THF)           1.52             3.59       1.52   \n",
       "     Product                        42.45                -          -   \n",
       "     *Step 2 PMI                        -                -          -   \n",
       "     **Full Route PMI                   -                -      11.16   \n",
       "\n",
       "                           RM cost/kg prod % RM cost/kg prod  \n",
       "Step Compound                                                 \n",
       "1    Starting Material                5.98             14.08  \n",
       "     Bromine                         15.82             37.27  \n",
       "     Dichloromethane (DCM)            5.94              14.0  \n",
       "     Intermediate A                      -                 -  \n",
       "     *Step 1 PMI                         -                 -  \n",
       "2    Intermediate A                      -                 -  \n",
       "     Reagent C                       13.19             31.06  \n",
       "     Tetrahydrofuran (THF)            1.52              3.59  \n",
       "     Product                             -                 -  \n",
       "     *Step 2 PMI                         -                 -  \n",
       "     **Full Route PMI                    -                 -  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.value_mod('Starting Material', 5., val_type='$/kg')\n",
    "model.calc_cost()\n",
    "model.results()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec7270ce-5619-40c0-abb4-3df420cb2eb9",
   "metadata": {},
   "source": [
    "The system can be restored to the original state using the `rxn_data_setup` method. This simply recombines the original `rxns` and `materials` DataFrames. Here the original model is re-costed to validate that things worked as expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f9307ae2-8fdc-4e02-b99d-6627d47a7fdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As of 2023-05-03 21:54 UTC --\n",
      "The final cost of Product is $54.41/kg.\n"
     ]
    },
    {
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       "      <th>Starting Material</th>\n",
       "      <td>15.0</td>\n",
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       "      <td>45.18</td>\n",
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       "      <td>17.94</td>\n",
       "      <td>32.97</td>\n",
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       "      <th>Bromine</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>-</td>\n",
       "      <td>2.31</td>\n",
       "      <td>11.54</td>\n",
       "      <td>39.85</td>\n",
       "      <td>3.16</td>\n",
       "      <td>15.82</td>\n",
       "      <td>29.08</td>\n",
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       "    <tr>\n",
       "      <th>Dichloromethane (DCM)</th>\n",
       "      <td>1.5</td>\n",
       "      <td>-</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4.33</td>\n",
       "      <td>14.97</td>\n",
       "      <td>3.96</td>\n",
       "      <td>5.94</td>\n",
       "      <td>10.92</td>\n",
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       "      <th>Intermediate A</th>\n",
       "      <td>28.95</td>\n",
       "      <td>0.75</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <th>*Step 1 PMI</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>6.07</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <th rowspan=\"6\" valign=\"top\">2</th>\n",
       "      <th>Intermediate A</th>\n",
       "      <td>28.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1.37</td>\n",
       "      <td>39.7</td>\n",
       "      <td>72.96</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <th>Reagent C</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1.32</td>\n",
       "      <td>13.19</td>\n",
       "      <td>24.23</td>\n",
       "      <td>1.32</td>\n",
       "      <td>13.19</td>\n",
       "      <td>24.23</td>\n",
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       "      <th>Tetrahydrofuran (THF)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.52</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.52</td>\n",
       "      <td>2.8</td>\n",
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       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <td>54.41</td>\n",
       "      <td>0.92</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>54.41</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>4.21</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>11.16</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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      ],
      "text/plain": [
       "                             $/kg Equiv Volumes Recycle kg/kg rxn   \n",
       "Step Compound                                                       \n",
       "1    Starting Material       15.0   1.0       -       -      0.87  \\\n",
       "     Bromine                  5.0   2.5       -       -      2.31   \n",
       "     Dichloromethane (DCM)    1.5     -    10.0    0.75      2.89   \n",
       "     Intermediate A         28.95  0.75       -       -         -   \n",
       "     *Step 1 PMI                -     -       -       -      6.07   \n",
       "2    Intermediate A         28.95   1.0       -       -      1.37   \n",
       "     Reagent C               10.0   1.0       -       -      1.32   \n",
       "     Tetrahydrofuran (THF)    1.0     -     5.0    0.75      1.52   \n",
       "     Product                54.41  0.92       -       -         -   \n",
       "     *Step 2 PMI                -     -       -       -      4.21   \n",
       "     **Full Route PMI           -     -       -       -         -   \n",
       "\n",
       "                           RM cost/kg rxn % RM cost/kg rxn kg/kg prod   \n",
       "Step Compound                                                           \n",
       "1    Starting Material              13.08            45.18        1.2  \\\n",
       "     Bromine                        11.54            39.85       3.16   \n",
       "     Dichloromethane (DCM)           4.33            14.97       3.96   \n",
       "     Intermediate A                 28.95                -          -   \n",
       "     *Step 1 PMI                        -                -          -   \n",
       "2    Intermediate A                  39.7            72.96          -   \n",
       "     Reagent C                      13.19            24.23       1.32   \n",
       "     Tetrahydrofuran (THF)           1.52              2.8       1.52   \n",
       "     Product                        54.41                -          -   \n",
       "     *Step 2 PMI                        -                -          -   \n",
       "     **Full Route PMI                   -                -      11.16   \n",
       "\n",
       "                           RM cost/kg prod % RM cost/kg prod  \n",
       "Step Compound                                                 \n",
       "1    Starting Material               17.94             32.97  \n",
       "     Bromine                         15.82             29.08  \n",
       "     Dichloromethane (DCM)            5.94             10.92  \n",
       "     Intermediate A                      -                 -  \n",
       "     *Step 1 PMI                         -                 -  \n",
       "2    Intermediate A                      -                 -  \n",
       "     Reagent C                       13.19             24.23  \n",
       "     Tetrahydrofuran (THF)            1.52               2.8  \n",
       "     Product                             -                 -  \n",
       "     *Step 2 PMI                         -                 -  \n",
       "     **Full Route PMI                    -                 -  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.rxn_data_setup()\n",
    "model.calc_cost()\n",
    "model.results()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f28b6ead-82ed-4630-ac4c-644f497230b7",
   "metadata": {},
   "source": [
    "Setting the price of a reaction product \"short circuits\" the reaction where the RMC was being calculated. This can be useful if the price of the compound is known, but it will be compared with RMCs for an alternative preparation method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "20893502-1410-48b9-a7b9-48a2cb20739b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As of 2023-05-03 21:54 UTC --\n",
      "The final cost of Product is $151.86/kg.\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>$/kg</th>\n",
       "      <th>Equiv</th>\n",
       "      <th>Volumes</th>\n",
       "      <th>Recycle</th>\n",
       "      <th>kg/kg rxn</th>\n",
       "      <th>RM cost/kg rxn</th>\n",
       "      <th>% RM cost/kg rxn</th>\n",
       "      <th>kg/kg prod</th>\n",
       "      <th>RM cost/kg prod</th>\n",
       "      <th>% RM cost/kg prod</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Step</th>\n",
       "      <th>Compound</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>Starting Material</th>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bromine</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dichloromethane (DCM)</th>\n",
       "      <td>1.5</td>\n",
       "      <td>-</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.75</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>Intermediate A</th>\n",
       "      <td>100.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>*Step 1 PMI</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">2</th>\n",
       "      <th>Intermediate A</th>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1.37</td>\n",
       "      <td>137.15</td>\n",
       "      <td>90.31</td>\n",
       "      <td>1.37</td>\n",
       "      <td>137.15</td>\n",
       "      <td>90.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reagent C</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1.32</td>\n",
       "      <td>13.19</td>\n",
       "      <td>8.68</td>\n",
       "      <td>1.32</td>\n",
       "      <td>13.19</td>\n",
       "      <td>8.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tetrahydrofuran (THF)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <td>151.86</td>\n",
       "      <td>0.92</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>151.86</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>*Step 2 PMI</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>4.21</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>**Full Route PMI</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>4.21</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              $/kg Equiv Volumes Recycle kg/kg rxn   \n",
       "Step Compound                                                        \n",
       "1    Starting Material        15.0   1.0       -       -         -  \\\n",
       "     Bromine                   5.0   2.5       -       -         -   \n",
       "     Dichloromethane (DCM)     1.5     -    10.0    0.75         -   \n",
       "     Intermediate A          100.0  0.75       -       -         -   \n",
       "     *Step 1 PMI                 -     -       -       -       0.0   \n",
       "2    Intermediate A          100.0   1.0       -       -      1.37   \n",
       "     Reagent C                10.0   1.0       -       -      1.32   \n",
       "     Tetrahydrofuran (THF)     1.0     -     5.0    0.75      1.52   \n",
       "     Product                151.86  0.92       -       -         -   \n",
       "     *Step 2 PMI                 -     -       -       -      4.21   \n",
       "     **Full Route PMI            -     -       -       -         -   \n",
       "\n",
       "                           RM cost/kg rxn % RM cost/kg rxn kg/kg prod   \n",
       "Step Compound                                                           \n",
       "1    Starting Material                  -                -          -  \\\n",
       "     Bromine                            -                -          -   \n",
       "     Dichloromethane (DCM)              -                -          -   \n",
       "     Intermediate A                     -                -          -   \n",
       "     *Step 1 PMI                        -                -          -   \n",
       "2    Intermediate A                137.15            90.31       1.37   \n",
       "     Reagent C                      13.19             8.68       1.32   \n",
       "     Tetrahydrofuran (THF)           1.52              1.0       1.52   \n",
       "     Product                       151.86                -          -   \n",
       "     *Step 2 PMI                        -                -          -   \n",
       "     **Full Route PMI                   -                -       4.21   \n",
       "\n",
       "                           RM cost/kg prod % RM cost/kg prod  \n",
       "Step Compound                                                 \n",
       "1    Starting Material                   -                 -  \n",
       "     Bromine                             -                 -  \n",
       "     Dichloromethane (DCM)               -                 -  \n",
       "     Intermediate A                      -                 -  \n",
       "     *Step 1 PMI                         -                 -  \n",
       "2    Intermediate A                 137.15             90.31  \n",
       "     Reagent C                       13.19              8.68  \n",
       "     Tetrahydrofuran (THF)            1.52               1.0  \n",
       "     Product                             -                 -  \n",
       "     *Step 2 PMI                         -                 -  \n",
       "     **Full Route PMI                    -                 -  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.value_mod('Intermediate A', 100.)\n",
    "model.calc_cost()\n",
    "model.results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "67602c04-75b3-4d7a-993f-5b4a0b6ade2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.rxn_data_setup() # Reset the system"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a14898-dc85-486d-a1be-e360f8dfbc8f",
   "metadata": {},
   "source": [
    "# Helper Functions for Automation and Plotting\n",
    "\n",
    "There are some additional helper functions for automation and plotting that are defined in the `helper` module, which can be loaded as shown below. These functions are not meant to be \"perfect,\" but are meant to demonstrate how these types of operations could be coded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "55401dab-11f6-4aaa-9012-16706863817c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import costcalc.helper as helper\n",
    "\n",
    "# This is simply loading the plotting library Matplotlib\n",
    "# and setting a couple of display parameters for the plots\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "plt.rc('figure', dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75bca75b-b31f-4ba3-b9a1-72625c739b93",
   "metadata": {},
   "source": [
    "## Sensitivity analysis\n",
    "\n",
    "The `sensitivity` function creates a sensititivity analysis tables. This is done by running through every value of a particular type, raising and lowering it by a small amount (default 10%), and recalculating the RM costs. The results are returned as a DataFrame. The first column is the original value. The \"Val low\" and \"Cost low\" are the lowered value and the resulting RM cost. The \"% low\" column shows how much the cost has be changed in % compared to the original value. The \"high\" columns are the corresponding values for the increased value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3f846221-ee96-4132-8f77-6017a15982f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>$/kg</th>\n",
       "      <th>Val low</th>\n",
       "      <th>Val high</th>\n",
       "      <th>Cost low</th>\n",
       "      <th>Cost high</th>\n",
       "      <th>% low</th>\n",
       "      <th>% high</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Step</th>\n",
       "      <th>Compound</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
       "      <th>Starting Material</th>\n",
       "      <td>15.0</td>\n",
       "      <td>13.50</td>\n",
       "      <td>16.50</td>\n",
       "      <td>52.62</td>\n",
       "      <td>56.20</td>\n",
       "      <td>-3.30</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bromine</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>5.50</td>\n",
       "      <td>52.83</td>\n",
       "      <td>55.99</td>\n",
       "      <td>-2.91</td>\n",
       "      <td>2.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dichloromethane (DCM)</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.65</td>\n",
       "      <td>53.82</td>\n",
       "      <td>55.00</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>Reagent C</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>53.09</td>\n",
       "      <td>55.73</td>\n",
       "      <td>-2.42</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tetrahydrofuran (THF)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.10</td>\n",
       "      <td>54.26</td>\n",
       "      <td>54.56</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            $/kg  Val low  Val high  Cost low  Cost high   \n",
       "Step Compound                                                              \n",
       "1    Starting Material      15.0    13.50     16.50     52.62      56.20  \\\n",
       "     Bromine                 5.0     4.50      5.50     52.83      55.99   \n",
       "     Dichloromethane (DCM)   1.5     1.35      1.65     53.82      55.00   \n",
       "2    Reagent C              10.0     9.00     11.00     53.09      55.73   \n",
       "     Tetrahydrofuran (THF)   1.0     0.90      1.10     54.26      54.56   \n",
       "\n",
       "                            % low  % high  \n",
       "Step Compound                              \n",
       "1    Starting Material      -3.30    3.30  \n",
       "     Bromine                -2.91    2.91  \n",
       "     Dichloromethane (DCM)  -1.09    1.09  \n",
       "2    Reagent C              -2.42    2.42  \n",
       "     Tetrahydrofuran (THF)  -0.28    0.28  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helper.sensitivity(model, col='$/kg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63836e52-25bb-4fcd-9f96-0cd064085418",
   "metadata": {},
   "source": [
    "## Scanning through an array of values\n",
    "\n",
    "The `value_scan` function can be used to create a table of costs for an array of values. In the example below, the price of the compound \"Starting Material\" is being scanned from \\\\$1./kg to \\\\$20./kg in 10 equally-spaced increments. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "21fa2c78-2efb-4aa8-b527-db8fe9b12bb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Values</th>\n",
       "      <th>$/kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>37.668068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.111111</td>\n",
       "      <td>40.192641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.222222</td>\n",
       "      <td>42.717213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.333333</td>\n",
       "      <td>45.241786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.444444</td>\n",
       "      <td>47.766358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11.555556</td>\n",
       "      <td>50.290931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13.666667</td>\n",
       "      <td>52.815504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15.777778</td>\n",
       "      <td>55.340076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17.888889</td>\n",
       "      <td>57.864649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>60.389221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Values       $/kg\n",
       "0   1.000000  37.668068\n",
       "1   3.111111  40.192641\n",
       "2   5.222222  42.717213\n",
       "3   7.333333  45.241786\n",
       "4   9.444444  47.766358\n",
       "5  11.555556  50.290931\n",
       "6  13.666667  52.815504\n",
       "7  15.777778  55.340076\n",
       "8  17.888889  57.864649\n",
       "9  20.000000  60.389221"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helper.value_scan(model, 'Starting Material', 1., 20., 10, val_type='$/kg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7d323ec-3150-46df-9e24-f032f8a3c4c7",
   "metadata": {},
   "source": [
    "## Plotting RM costs for a range of values\n",
    "\n",
    "The `plot_scan` function is similar to the `value_scan` function, except that the values will be plotted using the Matplotlib library. The final figure is not created using the `plot_scan` function, so multiple calls to this function will be plotted to the same plot. In the code block below, the overall RM costs (y axis) are being plotted for several different prices (default value type) for the Starting Material and Reagent C. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b61ba7e6-f09b-4d73-81ce-22ffd0aea978",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "helper.plot_scan(model, 'Starting Material', 1., 20., 10, label=True)\n",
    "helper.plot_scan(model, 'Reagent C', 1., 20., 10, label=True)\n",
    "\n",
    "plt.title('Changes in total RM costs with reagent prices')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9788b4c3-ce15-40b0-843b-29dac264fcfd",
   "metadata": {},
   "source": [
    "The `val_type` keyword argument can be used to scan different values. In the example below, the yield for the first reaction is being scanned from 50% to 90%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "118b6e8a-22d0-418e-ad53-94c22db0ddaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "helper.plot_scan(model, 'Intermediate A', 0.50, 0.95, 10, step=1, val_type='Equiv')\n",
    "\n",
    "plt.title('Changes in total RM costs with Step 1 yield')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac44de0a-24d7-4b71-aa7f-95d9cfde6f53",
   "metadata": {},
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