From cd2776e3d8a3ff18cedda4f483069fb839072b5f Mon Sep 17 00:00:00 2001
From: arielsaffer <61440835+arielsaffer@users.noreply.github.com>
Date: Tue, 12 Apr 2022 17:32:19 -0400
Subject: [PATCH] Add basic Excel-oriented import-export example (#188)

- Basic example using Python.
- Uses Excel (XLSX) configuration file.
- Notebook is computed but the saved file is not included.
---
 examples/notebooks/data/small_config.xlsx | Bin 0 -> 15844 bytes
 examples/notebooks/run_from_xlsx.ipynb    | 323 ++++++++++++++++++++++
 2 files changed, 323 insertions(+)
 create mode 100644 examples/notebooks/data/small_config.xlsx
 create mode 100644 examples/notebooks/run_from_xlsx.ipynb

diff --git a/examples/notebooks/data/small_config.xlsx b/examples/notebooks/data/small_config.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..48b2a503afaf9fb4d475b76d4b192a7a40b6a01b
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diff --git a/examples/notebooks/run_from_xlsx.ipynb b/examples/notebooks/run_from_xlsx.ipynb
new file mode 100644
index 00000000..54a99950
--- /dev/null
+++ b/examples/notebooks/run_from_xlsx.ipynb
@@ -0,0 +1,323 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PoPS Border simulation using an XLSX configuration file\n",
+    "\n",
+    "## Test example \n",
+    "\n",
+    "First, we complete the Excel template `data/small_config.xlsx` with the minimal configuration parameter required to run the simulation.\n",
+    "\n",
+    "The Excel configuration should include all parameters related to:\n",
+    "1. [consignments](../../docs/consignments.md) (what is imported, from where, in what amounts), \n",
+    "2. [contaminants](../../docs/contamination.md) (proportion and distribution of contaminated consignments, boxes, and items), and \n",
+    "3. [inspection](../../docs/inspections.md) (sampling strategies and rates)\n",
+    "\n",
+    "There is a help file (linked above) for each of these sections that goes into greater detail about how to define the parameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set up the workspace\n",
+    "\n",
+    "First, we load the needed `popsborder` functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load required functions and packages\n",
+    "\n",
+    "from popsborder.simulation import run_simulation\n",
+    "from popsborder.inputs import load_configuration\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We provide the location of our Excel configuration file. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xlsx_loc = \"data/small_config.xlsx\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use the `load_configuration` function to import the simulation settings from the file. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_config = load_configuration(xlsx_loc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run the simulation \n",
+    "\n",
+    "We use the `run_simulation` function to run the simulation. We assign the output of this function to a variable, so that we can examine the results later.\n",
+    "\n",
+    "We can include the `pretty` and `verbose` parameters below to visualize the contamination and output directly in the notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "━━ Consignment ━━ Boxes: 3 ━━ Items: 60 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
+      "🐛 ✿ ✿ 🐛 ✿ 🐛 🐛 🐛 🐛 🐛 ✿ ✿ 🐛 🐛 ✿ ✿ 🐛 🐛 ✿ ✿ | ✿ ✿ ✿ ✿ ✿ 🐛 ✿ ✿ ✿ ✿ 🐛 ✿ ✿ 🐛 🐛 ✿ ✿ ✿ ✿ ✿ | 🐛 🐛 🐛 ✿ ✿ 🐛 ✿ ✿ ✿ ✿ ✿ 🐛 ✿ ✿ 🐛 ✿ ✿ ✿ ✿ ✿\n",
+      "Inspection worked, found contaminant [TP]\n",
+      "━━ Consignment ━━ Boxes: 2 ━━ Items: 40 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
+      "🐛 🐛 🐛 ✿ 🐛 🐛 🐛 ✿ 🐛 ✿ 🐛 🐛 ✿ ✿ ✿ ✿ 🐛 ✿ 🐛 🐛 | ✿ 🐛 ✿ 🐛 ✿ ✿ 🐛 🐛 ✿ ✿ ✿ ✿ 🐛 ✿ 🐛 ✿ 🐛 🐛 🐛 🐛\n",
+      "Inspection worked, found contaminant [TP]\n",
+      "━━ Consignment ━━ Boxes: 5 ━━ Items: 100 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
+      "✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ 🐛 ✿ ✿ ✿ ✿ ✿ 🐛 ✿ 🐛 🐛 ✿ | 🐛 ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ 🐛 ✿ ✿ | ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ 🐛 🐛 ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ 🐛 | ✿ ✿ ✿ ✿ ✿ 🐛 ✿ ✿ 🐛 ✿ ✿ 🐛 🐛 🐛 ✿ ✿ ✿ ✿ ✿ ✿ | ✿ ✿ 🐛 ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ 🐛 ✿ ✿ ✿ 🐛 ✿ ✿\n",
+      "Inspection worked, found contaminant [TP]\n",
+      "━━ Consignment ━━ Boxes: 4 ━━ Items: 80 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
+      "✿ ✿ ✿ 🐛 ✿ 🐛 ✿ 🐛 ✿ 🐛 ✿ ✿ 🐛 🐛 🐛 ✿ ✿ ✿ ✿ ✿ | 🐛 ✿ ✿ ✿ ✿ ✿ 🐛 ✿ 🐛 ✿ ✿ ✿ ✿ 🐛 ✿ ✿ 🐛 ✿ ✿ ✿ | ✿ ✿ ✿ 🐛 🐛 🐛 🐛 🐛 ✿ 🐛 ✿ ✿ 🐛 🐛 ✿ ✿ ✿ 🐛 ✿ ✿ | ✿ ✿ ✿ ✿ ✿ 🐛 ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ ✿ 🐛 🐛 🐛 ✿\n",
+      "Inspection worked, found contaminant [TP]\n",
+      "Missing {missing:.0f}% of contaminated consignments.\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = run_simulation(\n",
+    "    config=base_config,\n",
+    "    seed=42,\n",
+    "    num_simulations=1,\n",
+    "    num_consignments=4,\n",
+    "    pretty=\"boxes\",\n",
+    "    verbose=True\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Export simulation results\n",
+    "\n",
+    "The summary of results can also be converted to a tabular format with the `pandas` library and exported to Excel or used directly for further analyses. \n",
+    "\n",
+    "First, we import an additional needed `popsborder` function and the `pandas` library."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from popsborder.outputs import save_simulation_result_to_pandas \n",
+    "import pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use `save_simulation_result_to_pandas` to convert our simulation results into a dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_pd = save_simulation_result_to_pandas(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The resulting dataframe can be used directly here for further analyses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>missing</th>\n",
+       "      <th>false_neg</th>\n",
+       "      <th>missed_within_tolerance</th>\n",
+       "      <th>intercepted</th>\n",
+       "      <th>num_inspections</th>\n",
+       "      <th>num_boxes</th>\n",
+       "      <th>num_items</th>\n",
+       "      <th>avg_boxes_opened_completion</th>\n",
+       "      <th>avg_boxes_opened_detection</th>\n",
+       "      <th>pct_boxes_opened_completion</th>\n",
+       "      <th>...</th>\n",
+       "      <th>pct_contaminant_unreported_if_detection</th>\n",
+       "      <th>true_contamination_rate</th>\n",
+       "      <th>max_missed_contamination_rate</th>\n",
+       "      <th>avg_missed_contamination_rate</th>\n",
+       "      <th>max_intercepted_contamination_rate</th>\n",
+       "      <th>avg_intercepted_contamination_rate</th>\n",
+       "      <th>false_negative_present</th>\n",
+       "      <th>true_positive_present</th>\n",
+       "      <th>total_intercepted_contaminants</th>\n",
+       "      <th>total_missed_contaminants</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.25</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>53.333333</td>\n",
+       "      <td>0.345625</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>0.345625</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   missing  false_neg  missed_within_tolerance  intercepted  num_inspections  \\\n",
+       "0      0.0        0.0                      0.0          4.0              4.0   \n",
+       "\n",
+       "   num_boxes  num_items  avg_boxes_opened_completion  \\\n",
+       "0       14.0      280.0                          3.5   \n",
+       "\n",
+       "   avg_boxes_opened_detection  pct_boxes_opened_completion  ...  \\\n",
+       "0                        1.25                        100.0  ...   \n",
+       "\n",
+       "   pct_contaminant_unreported_if_detection  true_contamination_rate  \\\n",
+       "0                                53.333333                 0.345625   \n",
+       "\n",
+       "   max_missed_contamination_rate  avg_missed_contamination_rate  \\\n",
+       "0                           None                           None   \n",
+       "\n",
+       "   max_intercepted_contamination_rate  avg_intercepted_contamination_rate  \\\n",
+       "0                                0.55                            0.345625   \n",
+       "\n",
+       "   false_negative_present true_positive_present  \\\n",
+       "0                       0                     1   \n",
+       "\n",
+       "  total_intercepted_contaminants  total_missed_contaminants  \n",
+       "0                           85.0                        0.0  \n",
+       "\n",
+       "[1 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dataframe can also be exported to XLSX (or other formats, like CSV) to use later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_pd.to_excel('data/small_config_results.xlsx')"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "35a97950b3485d686f65df065bbd4f3ea2db4d126aaaac47be7ba4d8431a0071"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.7 ('pops_border')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}