From e35766aa4d3476da172b12c16729d779d0338589 Mon Sep 17 00:00:00 2001
From: Andrii Skyba <andrii.h.skyba@cit.khpi.edu.ua>
Date: Mon, 2 Dec 2024 21:05:05 +0100
Subject: [PATCH] lab 2

---
 lab2.ipynb | 232 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 232 insertions(+)
 create mode 100644 lab2.ipynb

diff --git a/lab2.ipynb b/lab2.ipynb
new file mode 100644
index 0000000..794e581
--- /dev/null
+++ b/lab2.ipynb
@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0fe7dcd6-15d8-4398-8e86-48ac17df22ce",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  516.   706.   706. ... 19339. 19355. 19369.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import csv\n",
+    "import numpy as np\n",
+    "\n",
+    "def get_column_data(filename, column_name):\n",
+    "    with open(filename, newline='', encoding='utf-8') as csvfile:\n",
+    "        reader = csv.DictReader(csvfile)\n",
+    "        column_data = [row[column_name] for row in reader]\n",
+    "    return np.array(column_data, dtype=float)\n",
+    "\n",
+    "filename = \"C:/Users/skiba/python_khpi/russia_losses_equipment.csv\"\n",
+    "APC_losses = get_column_data(filename, \"APC\")\n",
+    "APC_losses = APC_losses[::-1]\n",
+    "print(APC_losses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1da9a636-a783-4ff7-b438-d8fb7b416162",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[190.   0. 110. ...  33.  16.  14.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "daily_losses = np.diff(APC_losses)\n",
+    "print(daily_losses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8098786e-a1ef-4d81-8118-ed4985b9bed1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 91.  96. 110. 120. 190.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "largest_losses = np.sort(daily_losses)[-5:]\n",
+    "print(largest_losses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a7b22a8f-5c67-4db3-b1d8-e8eda0d339ac",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1c0f28f0-08bb-462b-b3ed-4a024983fb4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Втрати БТР влітку 2023 року: -1126\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "def summer_losses_2023(filename, losses_column):\n",
+    "    \"\"\"\n",
+    "    Рахує втрати БТР за літо 2023 року.\n",
+    "    \n",
+    "    Parameters:\n",
+    "        filename (str): Назва файлу CSV.\n",
+    "        losses_column (str): Назва стовпця з втратами.\n",
+    "        \n",
+    "    Returns:\n",
+    "        int: Загальні втрати за літо 2023 року.\n",
+    "    \"\"\"\n",
+    "    data = pd.read_csv(filename, parse_dates=['date'])\n",
+    "    summer_data = data[(data['date'] >= \"2023-06-01\") & (data['date'] <= \"2023-08-31\")]\n",
+    "    return summer_data[losses_column].diff().fillna(0).sum()\n",
+    "\n",
+    "summer_losses = summer_losses_2023(filename, \"APC\")\n",
+    "print(\"Втрати БТР влітку 2023 року:\", int(summer_losses))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "aa44e38e-6131-4792-b484-8432150bbb27",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Середнє значення втрат БТР за 100-500 днів: 5823.032418952618\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Обчислюємо середнє значення втрат за 100-500 днів\n",
+    "def average_losses_in_range(losses, start_day, end_day):\n",
+    "    \"\"\"\n",
+    "    Обчислює середнє значення втрат у заданому діапазоні днів.\n",
+    "    \n",
+    "    Parameters:\n",
+    "        losses (np.ndarray): Массив з даними про втрати.\n",
+    "        start_day (int): Початковий день (включно).\n",
+    "        end_day (int): Кінцевий день (включно).\n",
+    "        \n",
+    "    Returns:\n",
+    "        float: Середнє значення втрат.\n",
+    "    \"\"\"\n",
+    "    return np.mean(losses[start_day:end_day])\n",
+    "\n",
+    "# Викликаємо функцію для діапазону 100-500 днів\n",
+    "average_loss = average_losses_in_range(APC_losses, 100, 501)  # від 100 до 500 включно\n",
+    "print(f\"Середнє значення втрат БТР за 100-500 днів: {average_loss}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b65deb73-b780-446e-ac5b-691eec6d7535",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot_last_200_days(losses, title=\"Втрати БТР за останні 200 днів\"):\n",
+    "    \"\"\"\n",
+    "    Створює графік втрат БТР за останні 200 днів.\n",
+    "    \n",
+    "    Parameters:\n",
+    "        losses (np.ndarray): Массив з даними про втрати.\n",
+    "        title (str): Назва графіку.\n",
+    "    \"\"\"\n",
+    "    # Останні 200 днів\n",
+    "    last_200_losses = losses[-200:]\n",
+    "    days = np.arange(1, 201)  # Створюємо масив з чисел від 1 до 200 для осі X\n",
+    "\n",
+    "    # Створення графіку\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    plt.plot(days, last_200_losses, label=\"Втрати БТР\", linestyle='-', color='b')  # Лінія графіку\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(\"Дні\")\n",
+    "    plt.ylabel(\"Кількість знищених БТР\")\n",
+    "    plt.grid(True, linestyle='--', color='gray', linewidth=0.5)\n",
+    "    plt.legend()\n",
+    "    \n",
+    "    # Збереження графіку\n",
+    "    plt.savefig(\"APC_losses_last_200_days.png\", dpi=100)\n",
+    "    plt.show()\n",
+    "\n",
+    "# Створюємо графік для останніх 200 днів\n",
+    "plot_last_200_days(APC_losses)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1fc49a86-96f0-4724-a37d-0699d2a1998e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
-- 
GitLab