{ "cells": [ { "metadata": {}, "cell_type": "markdown", "source": "# Generuj zestaw zadań", "id": "e6685f821bf3b135" }, { "metadata": {}, "cell_type": "markdown", "source": [ "```{admonition} Wskazówka\n", ":class: tip\n", "\n", "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/DyonOylloug/generator_zadan/HEAD?labpath=docs%2Fgeneruj_zestaw.ipynb) <- interaktywna sesja notebooka\n", "\n", "```" ], "id": "ca8a7b6043e294a0" }, { "metadata": { "ExecuteTime": { "end_time": "2025-04-22T18:00:08.703346Z", "start_time": "2025-04-22T18:00:08.700382Z" } }, "cell_type": "code", "source": [ "import os\n", "import sys\n", "from random import choice\n", "\n", "import ipywidgets as widgets\n", "from IPython.display import display, Image, Markdown, Latex\n", "from pdf2image import convert_from_path\n", "\n", "import generator_zadan.generatory as gz\n", "\n", "print(gz.__version__)" ], "id": "e09fa3157fc76075", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.2.10\n" ] } ], "execution_count": 2 }, { "metadata": {}, "cell_type": "markdown", "source": [ "---\n", "\n", "## Wybierz zadania\n" ], "id": "c7b6887d61c3d313" }, { "metadata": {}, "cell_type": "markdown", "source": [ "```{admonition} Wskazówka\n", ":class: tip\n", "> - Można komórki uruchamiać wielokrotnie. Aż się trafi ciekawszy przykład.\n", "> - Można zmieniać parametry funkcji generujących.\n", "> - Zmień __`zadanie_nr`__ na odpowiednio rozróżnialne __`zadanie_1`__, __`zadanie_2`__ itp. Tylko te będą potem przekazywane do funkcji generującej zestaw. Domyślnie pięć zadań. Można to zmienić niżej przy wywołaniu funkcji ***generuj_LaTeX()***.\n", "> - Na potrzeby dokumentacji na początku numery ustawione ma pierwszych pięć przykładów. \n", "\n", "```" ], "id": "92a5e20e31055a8b" }, { "metadata": {}, "cell_type": "markdown", "source": "### Liczby zespolone", "id": "200f7a6af031672a" }, { "metadata": {}, "cell_type": "markdown", "source": "#### Równanie liniowe", "id": "6d953d959dd85432" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2025-01-18T22:00:47.303506Z", "start_time": "2025-01-18T22:00:47.063747Z" } }, "cell_type": "code", "source": [ "zadanie = gz.rownanie_liniowe()\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n')[0].replace('\\\\', '')))\n", "display(Latex('$$' + zadanie[0].split('\\n')[2] + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex('$' + zadanie[1] + '$'))\n", "zadanie_1 = zadanie" ], "id": "5449b3729c720f05", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Rozwiązać równanie w zbiorze liczb zespolonych. Sprawdzić rozwiązanie." }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\t\t3 + 9i+\\left(7 + 7i\\right)z = \\left(6 + 9i\\right)z$$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$z=3 - 3i.$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 65 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Równanie kwadratowe", "id": "a2a3c96cb612544a" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2025-01-18T22:01:07.202546Z", "start_time": "2025-01-18T22:01:07.133904Z" } }, "cell_type": "code", "source": [ "zadanie = gz.rownanie_kwadratowe()\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n')[0].replace('\\\\', '')))\n", "display(Latex('$$' + zadanie[0].split('\\n')[2] + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex('$' + zadanie[1] + '$'))\n", "zadanie_2 = zadanie" ], "id": "63ea204b35f34245", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Rozwiązać równanie w zbiorze liczb zespolonych. Sprawdzić jedno z rozwiązań." }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\t\t\\left(1 - i\\right)z^2 + \\left(-7 + 13 i\\right) z + \\left(6 - 38 i\\right)=0$$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\Delta = 8-6i, \\quad \\sqrt{\\Delta}=\\pm( 3 - i), \\quad z_{1}=4 - 2 i, \\quad z_{2}=6 - i$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 69 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Pierwiastek zespolony", "id": "33923be2cec66dd9" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-11-26T20:59:26.215822Z", "start_time": "2024-11-26T20:59:23.297300Z" } }, "cell_type": "code", "source": [ "zadanie = gz.pierwiastek_zespolony(stopien=choice([3, 4]), nr_zadania=1234)\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n')[0].replace('\\\\', '')))\n", "display(Latex('$$' + zadanie[0].split('\\[')[1].split('\\]')[0] + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "temp = zadanie[1].split('\\n\\t')[2] + zadanie[1].split('\\n\\t')[5][4:]\n", "display(Latex(f'$${temp}$$'))\n", "display(Markdown(f'![](./pics//rozklad_pierwiastkow1234.png)'))\n", "print(\"\\033[32m*\\033[0m\" * 95)\n", "zadanie_3 = zadanie" ], "id": "9474837fa71f3d66", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Wyznaczyć wszystkie zadane pierwiastki zespolone i zaznaczyć je na płaszczyźnie zespolonej" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\n\t\t\\sqrt[\\leftroot{2}\\uproot{-4} \\displaystyle ^{3}]{\\left(-1 + \\sqrt{3} i \\right) \\left(\\frac{27 \\sqrt{3}}{4} - \\frac{27 i}{4} \\right)}\n\t$$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$\t\\sqrt[\\leftroot{2}\\uproot{-4} \\displaystyle ^{3}]{27 i}=\\left\\{ - 3 i, \\ - \\frac{3 \\sqrt{3}}{2} + \\frac{3 i}{2}, \\ \\frac{3 \\sqrt{3}}{2} + \\frac{3 i}{2}\\right\\}.$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "![](./pics//rozklad_pierwiastkow1234.png)" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\n" ] } ], "execution_count": 27 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Równanie zespolone ze sprzężeniem", "id": "fafbf5e0aaf89dd3" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-11-27T10:45:53.402262Z", "start_time": "2024-11-27T10:45:53.134039Z" } }, "cell_type": "code", "source": [ "zadanie = gz.rownanie_ze_sprzezeniem(calkowite=choice([False, True]), kwadratowe=choice([False, True]))\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n')[0].replace('\\\\', '')))\n", "display(Latex('$$' + zadanie[0].split('\\n')[2] + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "rozwiazanie = zadanie[1].replace('$ \\\\\\\\ \\n$', ' $ ').replace('$\\\\\\\\\\n$', ' $ ').replace('$ \\\\\\\\\\n$', ' $ ').split('$')\n", "for i in range(1, len(rozwiazanie) - 1):\n", " display(Latex('$$' + rozwiazanie[i] + '$$'))\n", "zadanie_2 = zadanie" ], "id": "fed64507b29eaad3", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Rozwiązać równanie w zbiorze liczb zespolonych" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\t\tz \\left(1 - 4 i\\right) + \\left(-1 - 5 i\\right) \\overline{z} + 2 + 5 i = 0$$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\left(-1 - 5 i\\right) \\left(x - i y\\right) + \\left(1 - 4 i\\right) \\left(x + i y\\right) + 2 + 5 i = 0, $$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$ - i \\left(9 x + y \\left(-2 - i\\right) - 5 + 2 i\\right) = 0, $$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$ \\left\\{\n\t\\begin{array}{c}\n\t\t2 - y = 0\\\\\n\t\t- 9 x + 2 y + 5 = 0\n\t\\end{array}\n\\right. $$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$ z = \\left\\{ x : 1, \\ y : 2\\right\\}.$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 7 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Obszar zespolony", "id": "ca38ed1076be1123" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-09-27T17:21:57.399281Z", "start_time": "2024-09-27T17:21:54.113823Z" } }, "cell_type": "code", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Zaznaczyć na płaszczyźnie zespolonej obszar spełniający warunek" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\n\t\t\\left|z-2 - 2 i\\right| \\geq 3\n\t$$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\begin{gather*}\n\t\t\\left(x - 2\\right)^{2} + \\left(y - 2\\right)^{2}\\geq9\n\t\\end{gather*}$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "![](./pics//obszar0.png)" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 2, "source": [ "zadanie = gz.obszar_zespolony(typ=choice([1, 2, 3, 4, 5]), nr_zadania=1234)\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n')[0].replace('\\\\', '')))\n", "display(Latex('$$' + zadanie[0].split('\\[')[1].split('\\]')[0] + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex('$$\\\\begin{gather*}' + zadanie[1].split('{gather*}')[1] + '{gather*}$$'))\n", "display(Markdown(f'![](./pics//obszar1234.png)'))\n", "zadanie_4 = zadanie" ], "id": "ce45879c406a855" }, { "metadata": {}, "cell_type": "markdown", "source": "#### Działania w różnych postaciach", "id": "113481fd48b1f3d8" }, { "metadata": { "ExecuteTime": { "end_time": "2024-10-27T18:06:09.365363Z", "start_time": "2024-10-27T18:05:22.728712Z" } }, "cell_type": "code", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Obliczyć" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\t\\frac{\\left(2 - 2 i\\right)\\left(3 + 3 i\\right)}{4 - \\frac{4 \\sqrt{3} i}{3}} + \\frac{2\\left(\\cos\\left(\\frac{5 \\pi}{3}\\right) + i \\, \\sin\\left(\\frac{5 \\pi}{3}\\right)\\right)\\cdot 3\\left(\\cos\\left(- \\frac{14 \\pi}{3}\\right) + i \\, \\sin\\left(- \\frac{14 \\pi}{3}\\right)\\right)}{4\\left(\\cos\\left(- \\frac{5 \\pi}{3}\\right) + i \\, \\sin\\left(- \\frac{5 \\pi}{3}\\right)\\right)} - \\frac{4\\,e^{- 3 \\pi\\, i}\\cdot 5\\,e^{2 \\pi\\, i}}{5\\,e^{- \\frac{13 \\pi}{3}\\, i}}. $$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Wynik zapisać w postaci algebraicznej, trygonometrycznej i wykładniczej." }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\n", "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\frac{7}{2} + \\frac{7 \\sqrt{3} i}{2}, \\qquad 7 \\left(\\cos\\left(\\frac{\\pi}{3}\\right) + i\\,\\sin\\left(\\frac{\\pi}{3}\\right)\\right), \\qquad 7 e^{\\frac{\\pi}{3}\\,i}$$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\u001B[32m*\u001B[0m\n" ] } ], "execution_count": 4, "source": [ "zadanie = gz.dzialania_zespolone()\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n\\t')[0]))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[2]) + '$$'))\n", "display(Markdown(zadanie[0].split('\\n\\t')[4]))\n", "print(\"\\033[34m*\\033[0m\" * 95)\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex('$$' + zadanie[1].split('$')[1] + '$$'))\n", "print(\"\\033[32m*\\033[0m\" * 95)\n", "zadanie_5 = zadanie" ], "id": "d5abe39b137160cc" }, { "metadata": {}, "cell_type": "markdown", "source": "### Macierze", "id": "6e3dd4a5c695230a" }, { "metadata": {}, "cell_type": "markdown", "source": "#### Wyznacznik", "id": "356c77f73d4e271a" }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-07T19:40:24.365123Z", "start_time": "2024-12-07T19:40:24.228209Z" } }, "cell_type": "code", "source": [ "zadanie = gz.wyznacznik(wymiar=choice([4,5,6]))\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0][:30].replace('A', '*A*')))\n", "display(Latex(zadanie[0][30:]))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex(zadanie[1].replace(\"$\", \"$$\")))\n", "zadanie_nr = zadanie" ], "id": "8fff78eee3310db7", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Obliczyć wyznacznik macierzy *A*" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "\n\t\\[\n\t\t\\textnormal{A=} \\left[\\begin{matrix}-2 & 1 & 2 & -2 & 0 & -1\\\\-1 & 0 & -2 & 3 & -2 & 1\\\\-1 & -1 & 1 & -1 & 2 & -2\\\\3 & -2 & 0 & -1 & 1 & 2\\\\2 & 1 & 1 & -2 & -2 & 1\\\\-1 & 3 & -2 & 3 & 3 & 3\\end{matrix}\\right]\n\t\\]" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\det(A)= 0$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 3 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Równanie macierzowe", "id": "32759ff628403709" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2025-01-18T22:01:26.327028Z", "start_time": "2025-01-18T22:01:26.287766Z" } }, "cell_type": "code", "source": [ "print(\"\\033[34m** Zadanie **\" + '*' * 81 + '\\033[0m')\n", "zadanie = gz.rownanie_macierzowe()\n", "display(Markdown(zadanie[0].split(':')[0]))\n", "display(Latex(zadanie[0].split(':')[1]))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex(zadanie[1].replace('$ \\\\\\\\ \\n\\t$', ',\\\\quad ').replace('$', '$$')))\n", "zadanie_3 = zadanie" ], "id": "b1c732f6ba6212e9", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ***********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Rozwiązać równanie" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "\n\t\\[\n\t\t\\left[\\begin{matrix}0 & 0 & 1 & 1 & 1\\\\1 & 0 & 0 & 2 & 2\\end{matrix}\\right]\\cdot \n\t\t\\left[\\begin{matrix}-1 & 0 & 0 & 1 & 0\\\\0 & 1 & -1 & -2 & -1\\end{matrix}\\right]^T + \n\t\t2X=\n\t\tX \\cdot \\left[\\begin{matrix}-1 & 2\\\\0 & 0\\\\2 & 0\\\\0 & 2\\\\2 & 2\\end{matrix}\\right]^T \\cdot\n\t\t\\left[\\begin{matrix}0 & 1\\\\0 & -1\\\\-2 & -2\\\\-2 & -2\\\\0 & 0\\end{matrix}\\right] \n\t\\]" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$ \\left[\\begin{matrix}1 & -4\\\\1 & -6\\end{matrix}\\right] + \n\t2X=\n\tX \\cdot \\left[\\begin{matrix}-4 & -5\\\\-4 & -2\\end{matrix}\\right] , \\quad \n\t\\left[\\begin{matrix}1 & -4\\\\1 & -6\\end{matrix}\\right] = \n\tX \\cdot \\left[\\begin{matrix}-6 & -5\\\\-4 & -4\\end{matrix}\\right] ,\\quad X=\\frac{1}{4}\\left[\\begin{matrix}-20 & 29\\\\-28 & 41\\end{matrix}\\right].$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 70 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Wyznacznik z parametrem", "id": "ae175fbb12e39851" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-11-27T19:42:16.136231Z", "start_time": "2024-11-27T19:42:16.095930Z" } }, "cell_type": "code", "source": [ "zadanie = gz.wyznacznik_parametr(wymiar=choice([2, 3, 4]), gotowiec=True)\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0][:91].replace('\\\\ ', ' ').replace('$', '*')))\n", "display(Latex(zadanie[0][91:]))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex(zadanie[1].replace('$ \\\\\\\\ \\n\\t$', ',\\\\quad ').replace('$', '$$')))\n", "zadanie_4 = zadanie" ], "id": "d31f4b2d63b140d9", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Dla jakich rzeczywistych wartości parametru *x* wyznacznik macierzy *A* jest różny od zera?" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "\n\t\\[\n\t\t\\textnormal{A=}\n\t\t\\left[\\begin{matrix}-3 & x - 2 & 1\\\\1 & 2 x + 1 & x + 1\\\\3 & 3 & 3\\end{matrix}\\right]\n\t\\]" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\det A=3 x^{2} - 21 x \\neq 0, \\quad x\\neq 0, \\ x\\neq 7, \\ $$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 11 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Macierz odwrotna z parametrem", "id": "1b8029e290f37927" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-11-27T19:42:40.254609Z", "start_time": "2024-11-27T19:42:36.847473Z" } }, "cell_type": "code", "source": [ "zadanie = gz.macierz_odwrotna_parametr(wymiar=choice([2, 3, 4]), gotowiec=True)\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0][:79].replace('$', '*')))\n", "display(Latex(zadanie[0][79:].split('W')[0]))\n", "display(Markdown('W' + zadanie[0][79:].split('W')[1].replace('$', '*')))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "rozwiazanie = zadanie[1].replace('$\\\\\\\\\\n\\t$', ' $ ').split('$')\n", "for i in range(1, len(rozwiazanie) - 1):\n", " display(Latex('$$' + rozwiazanie[i] + '$$'))\n", "zadanie_5 = zadanie" ], "id": "e71af03aa056326b", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Dla jakich rzeczywistych wartości parametru *x* macierz *A* posiada odwrotność?" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "\n\t\\[\n\t\t\\textnormal{A=}\\left[\\begin{matrix}x - 2 & x + 1\\\\2 & 2 x + 2\\end{matrix}\\right]\n\t\\]\n" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Wyznaczyć macierz odwrotną dla *x=2.* " }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\det A=2 x^{2} - 4 x - 6\\neq 0, \\quad \n\tx\\neq -1\\ \\textnormal{oraz} \\ x\\neq 3, $$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$ A(2)= \\left[\\begin{matrix}0 & 3\\\\2 & 6\\end{matrix}\\right],\\ \n\t\\det A(2)=-6,\\ \n\tA^{-1}=- \\frac{1}{6}\\left[\\begin{matrix}6 & -3\\\\-2 & 0\\end{matrix}\\right].$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 14 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Wektor własne", "id": "3010127a4d59e647" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-09-25T07:25:07.970031Z", "start_time": "2024-09-25T07:25:07.912834Z" } }, "cell_type": "code", "source": [ "zadanie = gz.wartosci_wlasne(wymiar=choice([2, 3, 4]), zespolone=choice([False, True]))\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0][:51]))\n", "display(Latex(zadanie[0][51:]))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex(zadanie[1].split(', \\quad')[0].replace(\"$\", \"$$\")))\n", "display(Markdown(\"Wartości własne\"))\n", "display(Latex(\"$$\" + (zadanie[1].split(', \\quad')[1]).split(\"$\")[1] + \"$$\"))\n", "display(Markdown(\"Wektory własne\"))\n", "display(Latex(\"$$\" + (zadanie[1].split(', \\quad')[1]).split(\"$\")[3] + \"$$\"))\n", "zadanie_nr = zadanie" ], "id": "b56c24c55e327704", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Wyznaczyć wartości własne i wektory własne macierzy" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": " \n\t\\[\n\t\t\\textnormal{A=} \\left[\\begin{matrix}1 & -1 & -1 & 3\\\\-2 & -1 & -1 & 0\\\\2 & 2 & 2 & 1\\\\0 & 3 & 3 & -1\\end{matrix}\\right]\n\t\\]" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\det\\left(\\lambda \\mathbb{I} - A \\right) = \\lambda^{4} - \\lambda^{3} - 4 \\lambda^{2} + 4 \\lambda$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Wartości własne" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$ \\left\\{ -2 : 1, \\ 0 : 1, \\ 1 : 1, \\ 2 : 1\\right\\}, $$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Wektory własne" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\left[ \\left( -2, \\ 1, \\ \\left[ \\left[\\begin{matrix}- \\frac{10}{9}\\\\- \\frac{23}{18}\\\\\\frac{17}{18}\\\\1\\end{matrix}\\right]\\right]\\right), \\ \\left( 0, \\ 1, \\ \\left[ \\left[\\begin{matrix}0\\\\-1\\\\1\\\\0\\end{matrix}\\right]\\right]\\right), \\ \\left( 1, \\ 1, \\ \\left[ \\left[\\begin{matrix}\\frac{1}{2}\\\\-1\\\\1\\\\0\\end{matrix}\\right]\\right]\\right), \\ \\left( 2, \\ 1, \\ \\left[ \\left[\\begin{matrix}2\\\\- \\frac{5}{2}\\\\\\frac{7}{2}\\\\1\\end{matrix}\\right]\\right]\\right)\\right]$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 10 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Rząd macierzy", "id": "9a2a41aa10ace55f" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-09-27T17:22:07.955317Z", "start_time": "2024-09-27T17:22:07.932760Z" } }, "cell_type": "code", "source": [ "zadanie = gz.rzad_macierzy()\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0][:25].replace('A', '*A*')))\n", "display(Latex(zadanie[0][25:]))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex(zadanie[1].replace(\"$\", \"$$\")))\n", "zadanie_nr = zadanie" ], "id": "e28efaa57ef234ba", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Wyznaczyć rząd macierzy *A*" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "\n\t\\[\n\t\t\\textnormal{A=} \\left[\\begin{matrix}2 & -1 & 3 & 1 & -1 & 2\\\\0 & 1 & 3 & 0 & 3 & -2\\\\3 & 1 & -1 & 1 & 0 & -2\\\\-2 & -1 & 0 & 2 & -2 & -1\\end{matrix}\\right]\n\t\\]" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$R(A)= 4$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 3 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Diagonalizacja macierzy", "id": "3cbe73968108312b" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-09-25T07:25:08.123294Z", "start_time": "2024-09-25T07:25:08.066168Z" } }, "cell_type": "code", "source": [ "zadanie = gz.diagonalizacja_macierzy(choice([2, 3, 4]))\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0][:53]))\n", "display(Latex(zadanie[0][53:]))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex(zadanie[1].split(', \\quad')[0].replace(\"$\", \"$$\")))\n", "display(Markdown(\"Wartości własne\"))\n", "display(Latex(\"$$\" + (zadanie[1].split(', \\quad')[1]).split(\"$\")[1] + \"$$\"))\n", "display(Markdown(\"Wektory własne\"))\n", "display(Latex(\"$$\" + (zadanie[1].split(', \\quad')[1]).split(\"$\")[3] + \"$$\"))\n", "display(Markdown(\"Diagonalizacja:\"))\n", "try:\n", " display(Latex(\"$$\" + (zadanie[1].split(', \\quad')[1]).split(\"$\")[5]\n", " + '\\quad ' + (zadanie[1].split(', \\quad')[1]).split(\"$\")[7] + \"$$\"))\n", "except Exception:\n", " display(Markdown(\"Macierz nie jest diagonalizowalna\"))\n", "zadanie_nr = zadanie" ], "id": "6131594766ee9356", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Przeprowadzić diagonalizację macierzy (jeśli możliwa)" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": " \n\t\\[\n\t\t\\textnormal{A=} \\left[\\begin{matrix}1 & 2 & 2\\\\3 & -1 & -1\\\\-2 & 1 & 1\\end{matrix}\\right]\n\t\\]" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\det\\left(\\lambda \\mathbb{I} - A \\right) = \\lambda^{3} - \\lambda^{2} - 2 \\lambda$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Wartości własne" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$ \\left\\{ -1 : 1, \\ 0 : 1, \\ 2 : 1\\right\\}, $$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Wektory własne" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\left[ \\left( -1, \\ 1, \\ \\left[ \\left[\\begin{matrix}\\frac{1}{3}\\\\- \\frac{4}{3}\\\\1\\end{matrix}\\right]\\right]\\right), \\ \\left( 0, \\ 1, \\ \\left[ \\left[\\begin{matrix}0\\\\-1\\\\1\\end{matrix}\\right]\\right]\\right), \\ \\left( 2, \\ 1, \\ \\left[ \\left[\\begin{matrix}- \\frac{4}{3}\\\\- \\frac{5}{3}\\\\1\\end{matrix}\\right]\\right]\\right)\\right]$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Diagonalizacja:" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$A = P\\,D\\,P^{-1}: \\quad P=\\left[\\begin{matrix}1 & 0 & -4\\\\-4 & -1 & -5\\\\3 & 1 & 3\\end{matrix}\\right],\\quad D=\\left[\\begin{matrix}-1 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 2\\end{matrix}\\right]$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 12 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Diagonalizacja z wielokrotnymi wartościami własnymi ", "id": "1131aa71c7f9c14" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-09-25T07:25:12.520478Z", "start_time": "2024-09-25T07:25:08.179473Z" } }, "cell_type": "code", "source": [ "zadanie = gz.diagonalizacja_macierzy_z_wielokrotnym_wartosciami_wlasnymi(choice([2, 3, 4]))\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0][:39]))\n", "display(Latex(zadanie[0][39:]))\n", "print(\"\\033[34m*\\033[0m\" * 95)\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex(zadanie[1].split(', \\quad')[0].replace(\"$\", \"$$\")))\n", "display(Markdown(\"Wartości własne\"))\n", "display(Latex(\"$$\" + (zadanie[1].split(', \\quad')[1]).split(\"$\")[1] + \"$$\"))\n", "display(Markdown(\"Wektory własne\"))\n", "display(Latex(\"$$\" + (zadanie[1].split(', \\quad')[1]).split(\"$\")[3] + \"$$\"))\n", "display(Markdown(\"Diagonalizacja:\"))\n", "try:\n", " display(Latex(\"$$\" + (zadanie[1].split(', \\quad')[1]).split(\"$\")[5]\n", " + '\\quad ' + (zadanie[1].split(', \\quad')[1]).split(\"$\")[7] + \"$$\"))\n", "except Exception:\n", " display(Markdown(\"Macierz nie jest diagonalizowalna\"))\n", "zadanie_nr = zadanie" ], "id": "7a582c07dd4eb72", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Przeprowadzić diagonalizację macierzy\n\t" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "\\[\n\t\t\\textnormal{A=} \\left[\\begin{matrix}-2 & 2 & 0\\\\0 & 3 & 0\\\\0 & 0 & -2\\end{matrix}\\right]\n\t\\]" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\u001B[34m*\u001B[0m\n", "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\det\\left(\\lambda \\mathbb{I} - A \\right) = \\lambda^{3} + \\lambda^{2} - 8 \\lambda - 12$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Wartości własne" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$ \\left\\{ -2 : 2, \\ 3 : 1\\right\\}, $$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Wektory własne" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\left[ \\left( -2, \\ 2, \\ \\left[ \\left[\\begin{matrix}1\\\\0\\\\0\\end{matrix}\\right], \\ \\left[\\begin{matrix}0\\\\0\\\\1\\end{matrix}\\right]\\right]\\right), \\ \\left( 3, \\ 1, \\ \\left[ \\left[\\begin{matrix}\\frac{2}{5}\\\\1\\\\0\\end{matrix}\\right]\\right]\\right)\\right]$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Diagonalizacja:" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$A = P\\,D\\,P^{-1}: \\quad P=\\left[\\begin{matrix}1 & 0 & 2\\\\0 & 0 & 5\\\\0 & 1 & 0\\end{matrix}\\right],\\quad D=\\left[\\begin{matrix}-2 & 0 & 0\\\\0 & -2 & 0\\\\0 & 0 & 3\\end{matrix}\\right]$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 13 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Regresja", "id": "ce81fcf42d5351c1" }, { "metadata": { "ExecuteTime": { "end_time": "2024-11-27T10:46:27.918077Z", "start_time": "2024-11-27T10:46:27.181939Z" } }, "cell_type": "code", "source": [ "zadanie = gz.regresja(stopien=1, nr_zadania=1234)\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n')[0].replace('\\\\', '')))\n", "display(Latex('$$' + zadanie[0].split('\\[')[1].split('\\]')[0] + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "temp = zadanie[1].split('_L')[0].split('[\\n\\t\\t')[1] + '_L'\n", "display(Latex(f'$${temp}$$'))\n", "display(Latex(f\"$${zadanie[1].split('_L')[1][12:].split('raisebox')[0][:-11]}$$\"))\n", "display(Markdown(f'![](./pics//regresja1234.png)'))\n", "zadanie_5 = zadanie" ], "id": "a523f49d5f83434c", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Wyznaczyć prostą regresji dla punktów " }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\n\t\t(-5,0),\\ (2,3),\\ (1,1),\\ (-4,-2),\\ (-2,-1).\n\t$$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\left[\\begin{matrix}-5 & 1\\\\2 & 1\\\\1 & 1\\\\-4 & 1\\\\-2 & 1\\end{matrix}\\right]\n\t\t\\cdot \\left[\\begin{matrix} a \\\\ b \\end{matrix}\\right]\n\t\t\\simeq \\left[\\begin{matrix}0\\\\3\\\\1\\\\-2\\\\-1\\end{matrix}\\right]\n\t\t\\quad \\Biggm/ \\cdot \\left(\\left(A^T A \\right)^{-1} A^T \\right)_L$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\left[\\begin{matrix} a \\\\ b \\end{matrix}\\right] = \n\t\t \\left[\\begin{matrix} \\frac{1}{2} \\\\ 1 \\end{matrix}\\right]$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "![](./pics//regresja1234.png)" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 10 }, { "metadata": {}, "cell_type": "markdown", "source": "### Układy równań liniowych", "id": "ed041be275eb03d8" }, { "metadata": {}, "cell_type": "markdown", "source": "#### Układ Cramera", "id": "49a1746cae7c7adf" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2025-01-29T22:13:33.235508Z", "start_time": "2025-01-29T22:13:33.225034Z" } }, "cell_type": "code", "source": [ "zadanie = gz.uklad_Cramera(choice([3]))\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n')[0].split('$')[0] +\n", " '*' + zadanie[0].split('$')[1].split('$')[0] + '*'))\n", "display(Latex('$$' + zadanie[0].split('\\[')[1].split('\\]')[0] + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex(zadanie[1].replace('$ \\\\\\\\ \\n\\t$', ',\\\\quad ').replace('$', '$$')))\n", "zadanie_1 = zadanie" ], "id": "fb9db48fec4f734a", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Z układu równań wyznaczyć niewiadomą *x*" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\n\t\t\\left\\{\n\t\t\t\\begin{matrix}\n\t\t\t\t4 x - 3 y - z = -3 \\\\ \n\t\t\t\t- x - 3 y + 3 z = -1 \\\\ \n\t\t\t\t- x - 2 y + 2 z = 0 \\\\ \n\t\t\t\\end{matrix}\n\t\t\\right.\n\t$$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\det(A) = 4,\\ \\det(A_x)=-8,\\ x = -2$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 53 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Układ nieoznaczony", "id": "9342255bdf531150" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2025-01-29T22:13:37.056335Z", "start_time": "2025-01-29T22:13:37.008032Z" } }, "cell_type": "code", "source": [ "zadanie = gz.uklad_rownan_nieoznaczony()\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n')[0].replace('\\\\ ', ' ')))\n", "display(Latex('$$' + zadanie[0].split('\\[')[1].split('\\]')[0] + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex(zadanie[1].replace('$ \\\\\\\\ \\n\\t$', ',\\\\quad ').replace('$', '$$')))\n", "zadanie_2 = zadanie" ], "id": "1edae10760f2a91c", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Rozwiązać układ równań. Jeśli możliwe podać trzy przykładowe rozwiązania. Jedno rozwiązanie sprawdzić. " }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\n\t\t\\left\\{\n\t\t\t\\begin{matrix}\n\t\t\t\t- 9 t - 4 x + 3 y - 3 z = 4 \\\\ \n\t\t\t\t- 6 t - 2 x + 2 y = 6 \\\\ \n\t\t\t\t- 5 t - 2 x + y - z = 2 \\\\ \n\t\t\t\t4 t + 2 x - y + 2 z = 0 \\\\ \n\t\t\t\t7 t + 3 x - 2 y + 2 z = -3 \\\\ \n\t\t\t\\end{matrix}\n\t\t\\right.\n\t$$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\left\\{ t = z - 2, \\ x = 5 - 3 z, \\ y = 2\\right\\}$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 54 }, { "metadata": {}, "cell_type": "markdown", "source": "### Geometria analityczna", "id": "475145fc6380a639" }, { "metadata": {}, "cell_type": "markdown", "source": "#### Równanie prostej", "id": "7bfeb0581947fc1f" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2025-01-21T22:43:22.531832Z", "start_time": "2025-01-21T22:43:22.496427Z" } }, "cell_type": "code", "source": [ "zadanie = gz.rownanie_prostej()\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n\\t')[0]))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[2]) + '$$'))\n", "display(Markdown(zadanie[0].split('\\n\\t')[4]))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[6]) + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex('$' + zadanie[1] + '$'))\n", "zadanie_3 = zadanie" ], "id": "7e85d20973ad3dfb", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Wyznaczyć równanie prostej przechodzącej przez punkty" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\tP_1 = (-1, -1, 2), \\quad P_2 = (-3, 1, 5).$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Obliczyć odległość wyznaczonej prostej od punktu" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\tP_3 = (-1, -2, -3).$$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$l\\colon \\frac{x + 1}{-2}= \\frac{y + 1}{2}= \\frac{z - 2}{3}; \\qquad d(P_3,l) = 3$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 12 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Równanie płaszczyzny", "id": "986d67807361e2e0" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-09-25T07:25:13.847310Z", "start_time": "2024-09-25T07:25:13.695031Z" } }, "cell_type": "code", "source": [ "zadanie = gz.rownanie_plaszczyzny()\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n\\t')[0]))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[2]) + '$$'))\n", "display(Markdown(zadanie[0].split('\\n\\t')[4]))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[6]) + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex('$' + zadanie[1] + '$'))\n", "zadanie_nr = zadanie" ], "id": "afabcb0490f9eaa1", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Wyznaczyć równanie płaszczyzny przechodzącej przez punkty" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\tP_1 = (-3, -2, 1), \\quad P_2 = (3, -3, -3), \\quad P_3 = (0, -2, -3).$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Obliczyć odległość wyznaczonej płaszczyzny od punktu" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\tP_4 = (3, 2, 3).$$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\pi\\colon 4 x + 12 y + 3 z + 33=0; \\qquad d(P_4,\\pi) = 6$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 17 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Punkty symetryczny do płaszczyzny", "id": "9e2498526fc2871d" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-09-25T07:25:13.909133Z", "start_time": "2024-09-25T07:25:13.860254Z" } }, "cell_type": "code", "source": [ "zadanie = gz.punkt_symetryczny_do_plaszczyzny()\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n\\t')[0]))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[2]) + '$$'))\n", "display(Markdown(zadanie[0].split('\\n\\t')[4]))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[6]) + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Markdown(zadanie[1].split('$')[0]))\n", "display(Latex('$$' + zadanie[1].split('$')[1] + '$$'))\n", "display(Markdown(zadanie[1].split('$')[4][2:]))\n", "display(Latex('$$' + zadanie[1].split('$')[5] + ' \\quad ' + zadanie[1].split('$')[3] + '$$'))\n", "display(Markdown(zadanie[1].split('$')[6][6:]))\n", "display(Latex('$$' + zadanie[1].split('$')[7] + '$$'))\n", "zadanie_nr = zadanie" ], "id": "96c88e82c933041d", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Wyznaczyć punkt symetryczny do punktu" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\tP = (-2, -1, -1)$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "względem płaszczyzny" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\t\\pi\\colon - x - y - z + 5 = 0.$$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Prosta prostopadła: " }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$ \\frac{x + 2}{-1}= \\frac{y + 1}{-1}= \\frac{z + 1}{-1}= t,$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "\\\nPunkt przecięcia to: " }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$P_p =(1,2,2), \\quad t_p=-3$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": " \nPunkt symetryczny to: " }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$P_s = (4,5,5)$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 18 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Punkty symetryczny do prostej", "id": "6e45a9ef1e15047b" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-09-25T07:25:13.962105Z", "start_time": "2024-09-25T07:25:13.928204Z" } }, "cell_type": "code", "source": [ "zadanie = gz.punkt_symetryczny_do_prostej()\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n\\t')[0]))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[2]) + '$$'))\n", "display(Markdown(zadanie[0].split('\\n\\t')[4]))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[6]) + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Markdown(zadanie[1].split('$')[0]))\n", "display(Latex('$$' + zadanie[1].split('$')[1].split('\\\\quad')[0] + '$$'))\n", "display(Markdown(zadanie[1].split('$')[2][8:]))\n", "display(Latex('$$' + zadanie[1].split('$')[3] + ' \\quad ' + zadanie[1].split('$')[1].split('\\quad')[1] + '$$'))\n", "display(Markdown(zadanie[1].split('$')[4][12:]))\n", "display(Latex('$$' + zadanie[1].split('$')[5] + '$$'))\n", "zadanie_nr = zadanie" ], "id": "3e0283d4a703b0ac", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Wyznaczyć punkt symetryczny do punktu" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\tP = (5, 5, 4)$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "względem prostej" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\tl\\colon \\frac{x + 3}{1}= \\frac{y + 3}{1}= \\frac{z - 5}{-2}.$$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Płaszczyzna prostopadła: " }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\pi\\colon x + y - 2 z - 2 = 0, $$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Punkt przecięcia to: " }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$P_p =(0,0,-1), \\quad t_p=3$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Punkt symetryczny to: " }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$P_s = (-5,-5,-6)$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 19 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Proste skośne", "id": "eed5a4a5decccb25" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-09-25T07:25:25.595640Z", "start_time": "2024-09-25T07:25:13.973965Z" } }, "cell_type": "code", "source": [ "zadanie = gz.odleglosc_prostych_skosnych()\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n\\t')[0]))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[2]) + '$$'))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[3]) + '$$'))\n", "display(Markdown(zadanie[0].split('\\n\\t')[4][3:]))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Markdown('Płaszczyzna zawierająca *l2* i równoległa do *l1* to'))\n", "display(Latex('$$' + (zadanie[1].split('$')[5]) + '$$'))\n", "display(Markdown('Odległość prostych skośnych to'))\n", "display(Latex('$$' + (zadanie[1].split('$')[7]) + '$$'))\n", "display(Markdown('Punkty realizujące minimalną odległość to'))\n", "display(Latex('$$' + (zadanie[1].split('$')[9]).replace('\\\\ ', '\\qquad ') + '$$'))\n", "zadanie_nr = zadanie" ], "id": "6d4fa2126c85917f", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Obliczyć odległość prostych skośnych" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\tl_1\\colon \\frac{x - 3}{4}=\\frac{y - 4}{1}=\\frac{z + 1}{3}, \\quad $$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\tl_2\\colon \\frac{x - 1}{4}=\\frac{y - 1}{3}=\\frac{z - 4}{5}.$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Wyznaczyć punkty realizujące minimalną odległość." }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Płaszczyzna zawierająca *l2* i równoległa do *l1* to" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\pi\\colon - 4 x - 8 y + 8 z - 20=0$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Odległość prostych skośnych to" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$d(l_1,l_2)=6;$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Punkty realizujące minimalną odległość to" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$ P_3=(1,\\frac{7}{2},- \\frac{5}{2}),\\qquad P_4=(-1,- \\frac{1}{2},\\frac{3}{2})$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 20 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Kąty w trójkącie", "id": "d9811786cfcf0e97" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2025-01-21T22:44:17.526899Z", "start_time": "2025-01-21T22:44:15.100445Z" } }, "cell_type": "code", "source": [ "zadanie = gz.katy_w_trojkacie(prosty=choice([False, True]), calkowite=choice([False, True]))\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown('Wyznaczyć miary kątów wewnętrznych trójkąta *ABC*, gdzie'))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[2]) + '$$'))\n", "display(Markdown('Sprawdzić, czy sumują się do 180\\u00b0'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex('$' + zadanie[1] + '$'))\n", "zadanie_4 = zadanie" ], "id": "139210171c77c0d", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Wyznaczyć miary kątów wewnętrznych trójkąta *ABC*, gdzie" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\tA = (2, -1, 4),\\ B = (2, 1, 4),\\ C = (3, 0, 4)$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Sprawdzić, czy sumują się do 180°" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\alpha = 45.00^{\\circ},\\ \\beta = 45.00^{\\circ},\\ \\gamma = 90.00^{\\circ}.$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 24 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Iloczyn wektorowy", "id": "e5263dc758813ba5" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-12-18T22:50:05.242701Z", "start_time": "2024-12-18T22:50:04.995545Z" } }, "cell_type": "code", "source": [ "zadanie = gz.iloczyn_wektorowy(ladne=choice([False, True]))\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('$')[0]))\n", "display(Latex('$$' + (zadanie[0].split('$')[1]) + ', \\quad ' + (zadanie[0].split('$')[3]) + '$$'))\n", "display(Markdown('(a) Obliczyć ich iloczyn wektorowy i jego długość.'))\n", "display(Markdown('(b) Sprawdzić, czy wyznaczony iloczyn jest prostopadły do zadanych wektorów.'))\n", "display(Markdown('(c) Wyznaczyć kąt między zadanymi wektorami.'))\n", "display(Markdown('(d) Używając kąta między wektorami obliczyć długość iloczynu wektorowego i porównać z (a).'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex('$ \\quad ' + zadanie[1].split('\\\\newline')[0][1:] + '$'))\n", "display(Latex('$' + zadanie[1].split('\\\\newline')[1] + '$'))\n", "display(Latex('$' + zadanie[1].split('\\\\newline')[2] + '$'))\n", "display(Latex('$' + zadanie[1].split('\\\\newline')[3][:-1] + '$'))\n", "zadanie_nr = zadanie" ], "id": "8f4146950b4a2cc9", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Niech dane będą wektory " }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\vec{u}=[5, 4, 5], \\quad \\vec{v}=[-1, 5, -3].$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "(a) Obliczyć ich iloczyn wektorowy i jego długość." }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "(b) Sprawdzić, czy wyznaczony iloczyn jest prostopadły do zadanych wektorów." }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "(c) Wyznaczyć kąt między zadanymi wektorami." }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "(d) Używając kąta między wektorami obliczyć długość iloczynu wektorowego i porównać z (a)." }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$ \\quad \\textbf{(a)}\\ \\vec{u}\\times\\vec{v}=[-37, 10, 29], |\\vec{u}\\times\\vec{v}|=\\sqrt{2310}, \\quad \\textbf{(b)} \\ \\vec{u}\\circ (\\vec{u}\\times \\vec{v}) = 0, \\vec{v}\\circ (\\vec{u}\\times \\vec{v}) = 0,$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$ \\quad \\textbf{(c)} \\ \\angle (\\vec{u},\\vec{v})=\\arccos{\\frac{0}{\\sqrt{66}\\cdot \\sqrt{35}} }= 90.0^{\\circ} ,$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$ \\quad \\textbf{(d)} \\ |\\vec{u}\\times\\vec{v}|=\\sqrt{66}\\cdot\\sqrt{35}\\cdot\\sin{90.0^{\\circ}}=48.0624593627917,$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$ \\quad \\textbf{(d')}\\ |\\vec{u}\\times\\vec{v}|=\\sqrt{66}\\cdot\\sqrt{35}\\cdot \\sqrt{1-0}=\\sqrt{2310} $" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 3 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Pole trójkąta", "id": "c63a611fece77618" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2025-01-21T22:44:35.766456Z", "start_time": "2025-01-21T22:44:35.752695Z" } }, "cell_type": "code", "source": [ "zadanie = gz.pole_trojkata(calkowite=choice([False, True]))\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(\n", " f'Wyznaczyć pole trójkąta *ABC* oraz długość wysokości opuszczonej z wierzchołka *{zadanie[0].split(\"ka \")[1].split(\" dla\")[0]}*' + ' dla'))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[2]) + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex('$' + zadanie[1].replace('\\\\ \\\\', '\\qquad') + '$'))\n", "zadanie_5 = zadanie" ], "id": "3a4b4aec80644542", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Wyznaczyć pole trójkąta *ABC* oraz długość wysokości opuszczonej z wierzchołka *A* dla" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\tA = (3, 3, 3),\\ B = (3, 2, 2),\\ C = (-1, -1, -1)$$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$P=2 \\sqrt{2},\\qquad h_A=\\frac{4 \\sqrt{17}}{17}$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 27 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Płaszczyzna styczna", "id": "af87c3f75306266f" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-09-25T07:25:29.431755Z", "start_time": "2024-09-25T07:25:28.862790Z" } }, "cell_type": "code", "source": [ "zadanie = gz.plaszczyzna_styczna()\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(\n", " f'Wyznaczyć płaszczyznę styczną do powierzchni'))\n", "display(Latex('$$' + (zadanie[0].split('$$')[1]) + '$$'))\n", "display(Markdown('w punkcie'))\n", "display(Latex('$' + zadanie[0].split('punkcie ')[1] + '$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex('$' + zadanie[1].replace('\\\\ \\\\', '\\qquad') + '$'))\n", "zadanie_nr = zadanie" ], "id": "1c7fe7d5a193a1ed", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Wyznaczyć płaszczyznę styczną do powierzchni" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$f(x,y)=\\frac{\\sqrt{- 4 x^{2} + 2 y^{2}}}{- 4 x - 4 y}$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "w punkcie" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$P=(-1,-2,f(-1,-2)).$$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$z = \\frac{2 x}{9} - \\frac{y}{9} + \\frac{1}{6}$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 23 }, { "metadata": {}, "cell_type": "markdown", "source": "### Granice", "id": "623ec82fba5315c5" }, { "metadata": {}, "cell_type": "markdown", "source": "#### Granice ciągów", "id": "a0979c91daf7a633" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-12-04T07:20:57.842185Z", "start_time": "2024-12-04T07:20:57.087048Z" } }, "cell_type": "code", "source": [ "zadanie = gz.granica_ciagu(typ=choice([0, 1, 2, 3]))\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n\\t')[0]))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[2]) + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex('$' + zadanie[1] + '$'))\n", "zadanie_1 = zadanie" ], "id": "da99c8ad97058032", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Obliczyć granicę" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\t\\lim\\limits_{n \\rightarrow \\infty} \\left(\\sqrt{3 n^{2} - 2 n - 2} - \\sqrt{3 n^{2} + 2 n + 3} \\right) $$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$- \\frac{2 \\sqrt{3}}{3}$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 3 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Granice funkcji", "id": "ecf81fd5ff4d1990" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-12-07T13:29:39.553616Z", "start_time": "2024-12-07T13:29:39.409620Z" } }, "cell_type": "code", "source": [ "zadanie = gz.granica_funkcji(typ=2) # typ=choice(range(12))\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n\\t')[0]))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[2]) + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex('$' + zadanie[1] + '$'))\n", "zadanie_2 = zadanie" ], "id": "ae2c5d9bcdff6d80", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Obliczyć granicę" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\t\\lim\\limits_{{x \\rightarrow 1^{+}}} \\frac{\\ln{\\left(x \\right)}}{\\sqrt{x^{2} - 1}} $$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$0$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 2 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Asymptoty", "id": "8222c000d2e85b21" }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-29T22:14:13.951227Z", "start_time": "2025-01-29T22:14:13.922190Z" } }, "cell_type": "code", "source": [ "zadanie = zadanie = gz.asymptoty(typ=choice(range(1,11))) # typ=choice(range(1,11))\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n\\t')[0]))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[2]) + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex(zadanie[1].split('\\\\\\\\')[0]))\n", "for i in range(1,len(zadanie[1].split('\\\\\\\\'))):\n", " display(Markdown((zadanie[1].split('\\\\\\\\')[i].split('$')[0])))\n", " display(Latex('$' + zadanie[1].split('\\\\\\\\')[i].split('$')[1] + '$'))\n", "zadanie_3 = zadanie" ], "id": "7257539dd7991db4", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Wyznaczyć wszystkie asymptoty funkcji" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\tf(x)= \\frac{2 x^{2} + 2 x - 1}{- 2 x - 1}.$$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$D_f\\colon \\mathbb{R}\\setminus \\left\\{- \\frac{1}{2}\\right\\}.$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Asymptota pionowa dwustronna w " }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$x_0=- \\frac{1}{2}.$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Asymptota ukośna w plus i minus nieskończoności o równaniu " }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$y=- x - \\frac{1}{2}.$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 61 }, { "metadata": {}, "cell_type": "markdown", "source": "### Styczna i normalna", "id": "ec17e22b7ed5bfda" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2025-01-29T22:14:22.479766Z", "start_time": "2025-01-29T22:14:22.281261Z" } }, "cell_type": "code", "source": [ "zadanie = gz.styczna_normalna(typ=choice([1, 2]))\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n\\t')[0]))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[2]) + '$$'))\n", "display(Markdown('w punkcie o współrzędnej'))\n", "display(Latex('$ x_0' + zadanie[0].split('x_0')[1]))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Markdown('Prosta styczna: '))\n", "display(Latex('$$' + zadanie[1].split('$')[1][:-1] + '$$'))\n", "display(Markdown('Prosta normalna: '))\n", "display(Latex('$$' + zadanie[1].split('$')[3] + '$$'))\n", "zadanie_4 = zadanie" ], "id": "7bce9e2d52ee171a", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Wyznaczyć równanie stycznej i normalnej do wykresu funkcji" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\tf(x) = \\frac{- x^{2} + 2 x - 2}{4 - x}$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "w punkcie o współrzędnej" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$ x_0 = 2.$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Prosta styczna: " }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$y = 2 - \\frac{3 x}{2}$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Prosta normalna: " }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$y = \\frac{2 x}{3} - \\frac{7}{3}$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 62 }, { "metadata": {}, "cell_type": "markdown", "source": "### Monotonicznoźć", "id": "f53318a7df11ec88" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2025-01-29T22:14:59.316175Z", "start_time": "2025-01-29T22:14:59.179817Z" } }, "cell_type": "code", "source": [ "zadanie = gz.monotonicznosc(choice([1, 2, 3]))\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n\\t')[0]))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[2]) + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Markdown('$$' + zadanie[1].split('$')[1] + '$$'))\n", "display(Markdown('$$' + zadanie[1].split('$')[5].split('\\\\implies')[0] + '\\\\implies' + '$$'))\n", "display(Markdown('$$' + '\\\\implies' + zadanie[1].split('$')[5].split('\\\\implies')[1] + '$$'))\n", "display(Markdown('$$' + zadanie[1].split('$')[7].split('\\\\implies')[0] + '\\\\implies' + '$$'))\n", "display(Markdown('$$' + '\\\\implies' + zadanie[1].split('$')[7].split('\\\\implies')[1] + '$$'))\n", "display(Markdown('$$' + zadanie[1].split('$')[9].replace('\\\\left\\\\{', '\\\\left\\\\lbrace').replace('\\\\right\\\\}',\n", " '\\\\right\\\\rbrace') + '$$'))\n", "zadanie_5 = zadanie" ], "id": "967f8c2facdf9ef1", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Zbadać monotoniczność i ekstrema funkcji" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\tf(x)=\\frac{- 2 x^{2} - x - 1}{3 x + 3} $$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "$$D_f\\colon \\mathbb{R}\\setminus \\left\\{-1\\right\\}$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "$$f'(x) > 0 \\textnormal{ dla: }x > -2 \\wedge x < 0 \\wedge x \\neq -1 \\implies$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "$$\\implies f(x) \\textnormal{ jest rosnąca dla } x \\in \\left(-2,-1 \\right)\\textnormal{ oraz } x \\in \\left(-1,0 \\right) $$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "$$f'(x) < 0 \\textnormal{ dla: }0 < x \\vee x < -2 \\implies$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "$$\\implies f(x) \\textnormal{ jest malejąca dla } x \\in \\left( -\\infty, -2 \\right)\\textnormal{ oraz } x \\in \\left(0, \\infty \\right) $$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "$$f'(x) = 0 \\textnormal{ dla } x \\in \\left\\lbrace -2, \\ 0\\right\\rbrace \\implies f_{\\min}\\left(-2\\right) = \\frac{7}{3} \\textnormal{ oraz } f_{\\max}\\left(0\\right) = - \\frac{1}{3}$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 67 }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-04T07:23:12.457195Z", "start_time": "2024-12-04T07:23:12.262431Z" } }, "cell_type": "code", "source": [ "zadanie_5 = gz.asymptoty(typ=6)\n", "zadanie_5" ], "id": "505d0577283ad3fc", "outputs": [ { "data": { "text/plain": [ "('Wyznaczyć wszystkie asymptoty funkcji\\n\\t\\\\[\\n\\t\\tf(x)= \\\\frac{x + 2 e^{- x}}{x + 1}.\\n\\t\\\\]\\n',\n", " '$D_f\\\\colon \\\\mathbb{R}\\\\setminus \\\\left\\\\{-1\\\\right\\\\}.$\\\\\\\\Asymptota pionowa dwustronna w $x_0=-1.$ \\\\\\\\Asymptota pozioma w plus nieskończoności o równaniu $y=1.$ ')" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 23 }, { "metadata": {}, "cell_type": "markdown", "source": "### Całki nieoznaczone", "id": "22a53cf59d9f596c" }, { "metadata": {}, "cell_type": "markdown", "source": "#### Przez podstawianie i przez części", "id": "fd34a60e81d26975" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-09-25T07:25:31.794074Z", "start_time": "2024-09-25T07:25:31.449629Z" } }, "cell_type": "code", "source": [ "zadanie = gz.calka_nieoznaczona(typ=choice([1, 2, 3, 4]))\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n\\t')[0]))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[2]) + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex('$$' + zadanie[1].split('=')[1] + '$'))\n", "zadanie_nr = zadanie" ], "id": "2a9374610434009e", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Obliczyć całkę" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\t\\int \\left(- 2 x^{2} - 2 x\\right) \\cos{\\left(x \\right)} \\, dx $$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$ \\left(- 4 x - 2\\right) \\cos{\\left(x \\right)} + \\left(- 2 x^{2} - 2 x + 4\\right) \\sin{\\left(x \\right)} + C$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 28 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Wymierne", "id": "507adf6dc0c4691b" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-09-25T07:25:32.174775Z", "start_time": "2024-09-25T07:25:31.947726Z" } }, "cell_type": "code", "source": [ "zadanie = gz.calka_wymierna(wlasciwy=choice([False, True]))\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n\\t')[0]))\n", "display(Latex('$$' + (zadanie[0].split('\\n\\t')[2]) + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex('$' + zadanie[1] + '$'))\n", "zadanie_nr = zadanie" ], "id": "8a17fb33ba4e09e9", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Obliczyć całkę" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\t\\int \\frac{- 4 x^{3} + 3 x^{2} - 8 x + 4}{x^{4} - 16} \\, dx $$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$$\\int \\left(\\frac{1 - x}{x^{2} + 4} - \\frac{2}{x + 2} - \\frac{1}{x - 2}\\right)\\,dx = - \\ln{\\left(x - 2 \\right)} - 2 \\ln{\\left(x + 2 \\right)} - \\frac{\\ln{\\left(x^{2} + 4 \\right)}}{2} + \\frac{\\arctan{\\left(\\frac{x}{2} \\right)}}{2} + C$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 29 }, { "metadata": {}, "cell_type": "markdown", "source": "### Całki oznaczone", "id": "bdc13d50b2902e9e" }, { "metadata": {}, "cell_type": "markdown", "source": "#### Pole obszaru", "id": "51e21bece2f7f728" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-09-25T07:47:38.928220Z", "start_time": "2024-09-25T07:47:23.937683Z" } }, "cell_type": "code", "source": [ "zadanie = gz.pole_obszaru(typ=choice([1, 2, 3, 4, 5]))\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].replace('\\\\[', '$$').replace('\\\\]', '$$')))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Markdown(zadanie[1].replace('$', '$$').replace('\\,', '')))\n", "zadanie_nr = zadanie" ], "id": "4f82841256dee42", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie 1 **********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Obliczyć pole obszaru ograniczonego wykresami krzywych\n\t$$\n\t\t f(x) = \\frac{3}{x} \\quad \\textnormal{ oraz } \\quad g(x) = - 2 x^{2} + 5 x + 4 \n\t$$\n" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Pole obszaru to $$\\int\\limits_{1/2}^{3}\\left(- 2 x^{2} + 5 x + 4 - \\frac{3}{x}\\right)dx = - 3 \\ln{\\left(3 \\right)} - 3 \\ln{\\left(2 \\right)} + \\frac{335}{24}$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 38 }, { "metadata": {}, "cell_type": "markdown", "source": "### Całki podwójne", "id": "e4d748b56b9e4eca" }, { "metadata": {}, "cell_type": "markdown", "source": "#### Po trójkącie", "id": "f8e6ec693af5d42b" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-11-08T11:26:47.142749Z", "start_time": "2024-11-08T11:26:44.329018Z" } }, "cell_type": "code", "source": [ "zadanie = gz.calka_podwojna(typ=1, nr_zadania=1234)\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].replace('\\\\,', '').replace('$', '*', ).replace('**', '$$', 2)))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "img1 = open(f'pics/calka_podwojna1234x.png', 'rb').read()\n", "wi1 = widgets.Image(value=img1, format='jpg', width=300, height=400)\n", "img2 = open(f'pics/calka_podwojna1234y.png', 'rb').read()\n", "wi2 = widgets.Image(value=img2, format='jpg', width=300, height=400)\n", "a = [wi1, wi2]\n", "wid = widgets.HBox(a)\n", "display(wid)\n", "print(len(zadanie[1].split('$')))\n", "if len(zadanie[1].split('$')) == 5:\n", " display(Markdown(f'Względem *{zadanie[1].split(\"$\")[1]}*'))\n", " display(Markdown('$$' + zadanie[1].split('$')[3].split('$')[0] + '$$'))\n", "else:\n", " display(Markdown(f'Względem *{zadanie[1].split(\"$\")[1]}*'))\n", " display(Markdown('$$' + zadanie[1].split('$')[3].split('$')[0] + '$$'))\n", " display(Markdown(f'Względem *{zadanie[1].split(\"$\")[5]}*'))\n", " display(Markdown('$$' + zadanie[1].split('$')[7].split('$')[0] + '$$'))\n", "zadanie_nr = zadanie" ], "id": "b37b6ca382f549dd", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Obliczyć $$\\iint\\limits_D\\left(x - y + 1\\right)dxdy$$ gdzie *D* - trójkąt *ABC* dla *A=(-2,1), B=(4,-3), C=(4,-1)*" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "HBox(children=(Image(value=b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\x01\\xc2\\x00\\x00\\x01\\xc2\\x08\\x06\\x00\\x…" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "d0ac36ea25a7455f823afa25650c9c19" } }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "9\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Względem *Ox:*" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "$$\\int\\limits_{-2}^{4}\\left(\\int\\limits_{- \\frac{2 x}{3} - \\frac{1}{3}}^{\\frac{1}{3} - \\frac{x}{3}}\\left(x - y + 1\\right)dy\\right)dx\n\t=\\ldots=\\int\\limits_{-2}^{4}\\left(\\frac{x^{2}}{2} + \\frac{4 x}{3} + \\frac{2}{3}\\right)dx = \\ldots = 24$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "Względem *Oy:*" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "$$\\int\\limits_{-3}^{-1}\\left(\\int\\limits_{- \\frac{3 y}{2} - \\frac{1}{2}}^{4}\\left(x - y + 1\\right)dx\\right)dy + \\int\\limits_{-1}^{1}\\left(\\int\\limits_{- \\frac{3 y}{2} - \\frac{1}{2}}^{1 - 3 y}\\left(x - y + 1\\right)dx\\right)dy\n\t=\\ldots=\\int\\limits_{-3}^{-1}\\left( - \\frac{21 y^{2}}{8} - \\frac{15 y}{4} + \\frac{99}{8} \\right)dy + \\int\\limits_{-1}^{1}\\left( \\frac{39 y^{2}}{8} - \\frac{27 y}{4} + \\frac{15}{8} \\right)dy = \\ldots = 24$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 6 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Po obszarze ograniczonym krzywymi", "id": "e8b8625e9c805ff2" }, { "metadata": { "tags": [ "hide-input" ], "ExecuteTime": { "end_time": "2024-09-27T17:22:39.941811Z", "start_time": "2024-09-27T17:22:36.229980Z" } }, "cell_type": "code", "source": [ "zadanie = gz.calka_podwojna(typ=2, nr_zadania=1111) # nr by wykluczyć zduplikowanie wykresu\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(\n", " zadanie[0].split('krzywymi')[0].replace('\\\\,', '').replace('$', '*', ).replace('**', '$$', 2) + 'krzywymi'))\n", "display(Markdown('$$' + zadanie[0].split('$')[7] + '\\\\quad \\\\text{' + zadanie[0].split('$')[8][1:] + '} \\\\quad ' +\n", " zadanie[0].split('$')[9] + '$$'))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "img1 = open(f'pics/calka_podwojna1111x.png', 'rb').read()\n", "wi1 = widgets.Image(value=img1, format='jpg', width=300, height=400)\n", "img2 = open(f'pics/calka_podwojna1111y.png', 'rb').read()\n", "wi2 = widgets.Image(value=img2, format='jpg', width=300, height=400)\n", "a = [wi1, wi2]\n", "wid = widgets.HBox(a)\n", "display(wid)\n", "print(len(zadanie[1].split('$')))\n", "if len(zadanie[1].split('$')) == 5:\n", " display(Markdown(f'Względem *{zadanie[1].split(\"$\")[1]}*'))\n", " display(Markdown('$$' + zadanie[1].split('$')[3].split('$')[0] + '$$'))\n", "else:\n", " display(Markdown(f'Względem *{zadanie[1].split(\"$\")[1]}*'))\n", " display(Markdown('$$' + zadanie[1].split('$')[3].split('$')[0] + '$$'))\n", " display(Markdown(f'Względem *{zadanie[1].split(\"$\")[5]}*'))\n", " display(Markdown('$$' + zadanie[1].split('$')[7].split('$')[0] + '$$'))\n", "zadanie_nr = zadanie" ], "id": "3fd097f7da71ed9b", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Obliczyć $$\\iint\\limits_D\\left(2 y\\right)dxdy$$ gdzie *D* - obszar ograniczony krzywymi" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "$$y=\\frac{3 x^{2}}{2} - 3 x - 3\\quad \\text{ oraz } \\quad y=3 - 3 x$$" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "HBox(children=(Image(value=b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\x01\\xc2\\x00\\x00\\x01\\xc2\\x08\\x06\\x00\\x…" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "83e523224d4a4e4fb0dc5afa30d1e85e" } }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Względem *Ox:*" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "$$\\int\\limits_{-2}^{2}\\left(\\int\\limits_{\\frac{3 x^{2}}{2} - 3 x - 3}^{3 - 3 x}\\left(2 y\\right)dy\\right)dx\n\t=\\ldots=\\int\\limits_{-2}^{2}\\left(- \\frac{9 x^{4}}{4} + 9 x^{3} + 9 x^{2} - 36 x\\right)dx = \\ldots = \\frac{96}{5}$$" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 5 }, { "metadata": {}, "cell_type": "markdown", "source": "### Szeregi Fouriera", "id": "611a1603f6fd6dd2" }, { "metadata": { "ExecuteTime": { "end_time": "2024-09-27T17:22:54.674922Z", "start_time": "2024-09-27T17:22:42.091187Z" } }, "cell_type": "code", "source": [ "zadanie = gz.szereg_Fouriera(\n", " typ_l=choice([0, 1, 2, 3, 4]),\n", " typ_p=choice([0, 1, 2, 3, 4]),\n", " bez_wykresu=True,\n", " tylko_koncowy=True,\n", " nr_zadania=123)\n", "print(f\"\\033[34m** Zadanie **\" + '*' * 82 + '\\033[0m')\n", "display(Markdown(zadanie[0].split('\\n')[0]))\n", "display(Latex('$$' + zadanie[0].split('\\[')[1].split('\\]')[0] + '$$'))\n", "display(Markdown(zadanie[0].split('\\n')[7].replace('$', '$$')))\n", "print(\"\\033[32m** Rozwiązanie **\" + '*' * 78 + '\\033[0m')\n", "display(Latex('$' + zadanie[1].split('$')[1] + '$'))\n", "display(Latex('$' + zadanie[1].split('$')[3] + '$'))\n", "display(Markdown(f'![](./pics//szereg_Fouriera_{123}_funkcja.png)'))\n", "display(Markdown(f'![](./pics//szereg_Fouriera_{123}_inf.png)'))\n", "zadanie_5 = zadanie" ], "id": "6030e97bd991244e", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[34m** Zadanie ************************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/markdown": "Rozwinąć w szereg Fouriera funkcję " }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$$\n\t\tf(x)=\\left\\{\\begin{matrix}\n\t\t\tx + 1 & \\textnormal{ dla } & x\\in\\left(- \\frac{\\pi}{2},0\\right)\\\\\n\t\t\t-1 & \\textnormal{ dla } & x\\in\\left[0,\\frac{\\pi}{2}\\right]\n\t\t\\end{matrix}\\right.\n\t$$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "o okresie zasadniczym $$2T=\\pi.$$ Naszkicować wykres funkcji, do której zbieżny jest uzyskany szereg." }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m** Rozwiązanie ********************************************************************************\u001B[0m\n" ] }, { "data": { "text/plain": [ "" ], "text/latex": "$a_0=- \\frac{\\pi}{4},\\quad a_n=\\frac{1 - \\left(-1\\right)^{n}}{2 \\pi n^{2}},\\quad b_n=\\frac{2 \\left(- \\frac{\\left(-1\\right)^{n} \\pi}{4} + \\left(-1\\right)^{n} - 1\\right)}{\\pi n},$" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/latex": "$S(x) = \\frac{2 \\left(-2 + \\frac{\\pi}{4}\\right) \\sin{\\left(2 x \\right)}}{\\pi} - \\frac{\\sin{\\left(4 x \\right)}}{4} + \\frac{2 \\left(-2 + \\frac{\\pi}{4}\\right) \\sin{\\left(6 x \\right)}}{3 \\pi} + \\frac{\\cos{\\left(2 x \\right)}}{\\pi} + \\frac{\\cos{\\left(6 x \\right)}}{9 \\pi} - \\frac{\\pi}{8} + \\dots $" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "![](./pics//szereg_Fouriera_123_funkcja.png)" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ], "text/markdown": "![](./pics//szereg_Fouriera_123_inf.png)" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 6 }, { "metadata": {}, "cell_type": "markdown", "source": [ "---\n", "\n", "## Ustaw nazwy" ], "id": "5dc817ea97ab66bc" }, { "metadata": {}, "cell_type": "markdown", "source": [ "```{admonition} Wskazówka\n", ":class: tip\n", "\n", "> Nazwy mogą być puste, tzn. ''. Nie wszystkie, bo na ich podstawie tworzona jest nazwa pliku.\n", "> \n", "> 'kolor_odpowiedzi' jest ważny dla pliku tex:\n", "> - 'red' - odpowiedzi ukryte, ale dostępne po kliknięciu,\n", "> - 'white' - ukrywa, ale zostawia miejsce,\n", "> - 'brak' - ukrywa i chowa miejsce,\n", "> - dla innych kolorów odpowiedzi są pokazywane, ale można je ukryć po kliknięciu.\n", ">\n", "> Dlatego potrzebna jest dwukrotna kompilacja i potrzebny jest czytnik pdf z obsługą warst i JavaScript. Zalecam Adobe Acrobat Reader \n", "\n", "```\n" ], "id": "521cc21f07383cf6" }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-29T22:15:45.751882Z", "start_time": "2025-01-29T22:15:45.747929Z" } }, "cell_type": "code", "source": [ "# bez podkreśleń !!! bo są błędy w kompilacji z podkreśleniami\n", "kierunek = 'OC' # np. AiR \n", "grupa = '' # np. Grupa-11\n", "przedmiot = 'Matematyka 1' # np. Algebra\n", "semestr = 'Sem 1' # np. Semestr-1 \n", "kolokwium = 'Kolokwium nr 2' # np. Kolokwium nr 2\n", "data = '30 stycznia 2025' # tego już w nazwie pliku nie będzie\n", "kolor_odpowiedzi = 'blue'\n", "\n", "nazwa_pliku = f'{kierunek}-{grupa}-{przedmiot}-{semestr}-{kolokwium}'\n" ], "id": "d771398ed607d314", "outputs": [], "execution_count": 68 }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-29T22:15:46.939227Z", "start_time": "2025-01-29T22:15:46.935535Z" } }, "cell_type": "code", "source": "nazwa_pliku", "id": "ad78cc4b08194d4a", "outputs": [ { "data": { "text/plain": [ "'OC--Matematyka 1-Sem 1-Kolokwium nr 2'" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 69 }, { "metadata": {}, "cell_type": "markdown", "source": [ "---\n", "\n", "## Generuj plik LaTeX z wybranych zadań" ], "id": "a0b7605ed4e08dfc" }, { "metadata": {}, "cell_type": "markdown", "source": [ "```{admonition} Wskazówka\n", ":class: tip\n", "\n", "> Wynik jest gotową zawartością pliku LaTeX.\n", "> \n", "> W mybinder (interaktywna sesja) można skopiować.\n", "> \n", "> Kompilować dwukrotnie pdflatex.\n", "> \n", "> W przypadku zadań z obrazkami trzeba też je pobierać w odpowiednie miejsce.\n", "> \n", "> Lepiej po prostu wygenerować pdf poniżej.\n", "\n", "```" ], "id": "40a07fabed117e71" }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-29T22:15:48.797250Z", "start_time": "2025-01-29T22:15:48.793754Z" } }, "cell_type": "code", "source": [ "# Wynik jest gotową zawartością pliku LaTeX. Można skopiować i wkleić. \n", "# Kompilować dwukrotnie pdflatex.\n", "LaTeX_source = gz.generuj_LaTeX(kierunek,\n", " grupa,\n", " przedmiot,\n", " semestr,\n", " kolokwium,\n", " data,\n", " kolor_odpowiedzi,\n", " zadanie_1, zadanie_2, zadanie_3, zadanie_4, zadanie_5)\n", "print(LaTeX_source)" ], "id": "3e909f0406dbf9b9", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "% !TeX spellcheck = pl_PL-Polish\n", "\\documentclass[a4paper,12pt]{article}\n", "\\linespread{1.3} %odstepy miedzy liniami\n", "\\usepackage[a4paper, lmargin=2cm, rmargin=2cm, tmargin=2cm, bmargin=2cm]{geometry}\n", "\\usepackage{amsfonts}\n", "\\usepackage{amsmath}\n", "\\usepackage{color}\n", "\\usepackage{enumitem}\n", "\\usepackage{fancyhdr}\n", "\\usepackage{float}\n", "\\usepackage{graphicx}\n", "\\usepackage{ifthen}\n", "\\usepackage[utf8]{inputenc}\n", "\\usepackage{lmodern}\n", "\\usepackage{ocgx}\n", "\\usepackage{polski}\n", "\\usepackage{tcolorbox}\n", "\\tcbuselibrary{most}\n", "\\tcbuselibrary{skins}\n", "\\tcbuselibrary{raster}\n", "% brak - bez odpowiedzi i bez miejsca, white - bez odpowiedzi z miejscem, red = odpowiedzi ukryte ale dostepne\n", "\\newcommand{\\kolorodpowiedzi}{blue}\n", "\\renewcommand{\\footrulewidth}{0.4pt}% linia pozioma na końcu strony - default is 0pt\n", "\\DeclareFontShape{OMX}{cmex}{m}{n}\n", " {<-7.5> cmex7\n", " <7.5-8.5> cmex8\n", " <8.5-9.5> cmex9\n", " <9.5-> cmex10}{}\n", "\\DeclareSymbolFont{largesymbols}{OMX}{cmex}{m}{n}\n", "\n", "\n", "\\newcommand{\\ukryte}{1} % domyślnie odpowiedzi są do pokazywania po kliknięciu\n", "\\ifthenelse{\\equal{\\kolorodpowiedzi}{red}} % ukrywamy od pokazywania gdy kolor jest red\n", "\t{\\renewcommand{\\ukryte}{0}}{}\n", "\n", "\\newcommand{\\zOdpowiedziami}[3]{\n", "\t\\ifthenelse{\\equal{#1}{brak}}{}{\n", "\t\t\\ifthenelse{\\equal{#1}{white}}{\\vphantom{#3}}{\n", "\t\t\t\\switchocg{#2}{\\textcolor{\\kolorodpowiedzi}{\\\\Rozwiązanie: }}\n", "\t\t\t\t\\begin{ocg}{Warstwa odpowiedzi}{#2}{\\ukryte}\n", "\t\t\t\t\t\\textcolor{\\kolorodpowiedzi}{#3}\n", "\t\t\t\t\\end{ocg}}}}\n", "\n", "\\pdfinfo{/Author (https://generator-zadan.readthedocs.io)}\n", "\n", "\\begin{document}\n", " \\pagestyle{fancy}\n", " \\setlength{\\headheight}{27.29453pt}\n", " \\fancyhead{}\n", " \\fancyhead[L]{\\textbf{OC\\\\Matematyka 1 - }}\n", " \\fancyhead[R]{\\textbf{Kolokwium nr 2\\\\ 30 stycznia 2025}}\n", " \\fancyfoot{}\n", " \\fancyfoot[R]{\\tiny\\textbf{29 stycznia 2025, 23:15}}\n", "% \\hspace{1cm}\n", " \\begin{enumerate}[label= \\textbf{Zadanie \\arabic*. }, leftmargin=1cm, align=left, itemsep=0pt]\n", "\t\t\\item Z układu równań wyznaczyć niewiadomą $x$\n", "\t\t\t\\[\n", "\t\t\t\t\\left\\{\n", "\t\t\t\t\t\\begin{matrix}\n", "\t\t\t\t\t\t4 x - 3 y - z = -3 \\\\ \n", "\t\t\t\t\t\t- x - 3 y + 3 z = -1 \\\\ \n", "\t\t\t\t\t\t- x - 2 y + 2 z = 0 \\\\ \n", "\t\t\t\t\t\\end{matrix}\n", "\t\t\t\t\\right.\n", "\t\t\t\\]\n", "\t\t\t\\zOdpowiedziami{\\kolorodpowiedzi}{ocg0}\n", "\t\t\t\t{$\\det(A) = 4,\\ \\det(A_x)=-8,\\ x = -2$}\n", "\n", "\t\t\\item Rozwiązać układ równań.\\ Jeśli możliwe podać trzy przykładowe rozwiązania.\\ Jedno rozwiązanie sprawdzić. \n", "\t\t\t\\[\n", "\t\t\t\t\\left\\{\n", "\t\t\t\t\t\\begin{matrix}\n", "\t\t\t\t\t\t- 9 t - 4 x + 3 y - 3 z = 4 \\\\ \n", "\t\t\t\t\t\t- 6 t - 2 x + 2 y = 6 \\\\ \n", "\t\t\t\t\t\t- 5 t - 2 x + y - z = 2 \\\\ \n", "\t\t\t\t\t\t4 t + 2 x - y + 2 z = 0 \\\\ \n", "\t\t\t\t\t\t7 t + 3 x - 2 y + 2 z = -3 \\\\ \n", "\t\t\t\t\t\\end{matrix}\n", "\t\t\t\t\\right.\n", "\t\t\t\\]\n", "\t\t\t\\zOdpowiedziami{\\kolorodpowiedzi}{ocg1}\n", "\t\t\t\t{$\\left\\{ t = z - 2, \\ x = 5 - 3 z, \\ y = 2\\right\\}$}\n", "\n", "\t\t\\item Wyznaczyć wszystkie asymptoty funkcji\n", "\t\t\t\\[\n", "\t\t\t\tf(x)= \\frac{2 x^{2} + 2 x - 1}{- 2 x - 1}.\n", "\t\t\t\\]\n", "\n", "\t\t\t\\zOdpowiedziami{\\kolorodpowiedzi}{ocg2}\n", "\t\t\t\t{$D_f\\colon \\mathbb{R}\\setminus \\left\\{- \\frac{1}{2}\\right\\}.$\\\\Asymptota pionowa dwustronna w $x_0=- \\frac{1}{2}.$ \\\\Asymptota ukośna w plus i minus nieskończoności o równaniu $y=- x - \\frac{1}{2}.$ }\n", "\n", "\t\t\\item Wyznaczyć równanie stycznej i normalnej do wykresu funkcji\n", "\t\t\t\\[\n", "\t\t\t\tf(x) = \\frac{- x^{2} + 2 x - 2}{4 - x}\n", "\t\t\t\\]\n", "\t\t w punkcie $x_0 = 2.$\n", "\t\t\t\\zOdpowiedziami{\\kolorodpowiedzi}{ocg3}\n", "\t\t\t\t{Styczna: $y = 2 - \\frac{3 x}{2},$ \\quad\n", "\t\t\tNormalna: $y = \\frac{2 x}{3} - \\frac{7}{3}$}\n", "\n", "\t\t\\item Zbadać monotoniczność i ekstrema funkcji\n", "\t\t\t\\[\n", "\t\t\t\tf(x)=\\frac{- 2 x^{2} - x - 1}{3 x + 3} \n", "\t\t\t\\]\n", "\n", "\t\t\t\\zOdpowiedziami{\\kolorodpowiedzi}{ocg4}\n", "\t\t\t\t{$D_f\\colon \\mathbb{R}\\setminus \\left\\{-1\\right\\}$\\newline\n", "\t\t\t$f'(x)=- \\frac{2 x \\left(x + 2\\right)}{3 \\left(x + 1\\right)^{2}}$\\newline\n", "\t\t\t$f'(x) > 0 \\textnormal{ dla: }x > -2 \\wedge x < 0 \\wedge x \\neq -1 \\implies f(x) \\textnormal{ jest rosnąca dla } x \\in \\left(-2,-1 \\right)\\textnormal{ oraz } x \\in \\left(-1,0 \\right) $\\newline\n", "\t\t\t$f'(x) < 0 \\textnormal{ dla: }0 < x \\vee x < -2 \\implies f(x) \\textnormal{ jest malejąca dla } x \\in \\left( -\\infty, -2 \\right)\\textnormal{ oraz } x \\in \\left(0, \\infty \\right) $\\newline\n", "\t\t\t$f'(x) = 0 \\textnormal{ dla } x \\in \\left\\{ -2, \\ 0\\right\\} \\implies f_{\\min}\\left(-2\\right) = \\frac{7}{3} \\textnormal{ oraz } f_{\\max}\\left(0\\right) = - \\frac{1}{3}$}\n", "\n", " \\end{enumerate}\n", "\\end{document}\n" ] } ], "execution_count": 70 }, { "metadata": {}, "cell_type": "markdown", "source": [ "```{admonition} Wskazówka\n", ":class: tip\n", "\n", "Poniżej można zapisać gotowy plik na dysku. Można go edytować. \n", "W mybinder można go pobrać w menu po lewej stronie z katalogu wygenerowane. \n", "Kompilować dwukrotnie.\n", "\n", "```" ], "id": "90c5ffcb997a03dc" }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-29T22:15:49.926341Z", "start_time": "2025-01-29T22:15:49.922469Z" } }, "cell_type": "code", "source": [ "if not os.path.exists('wygenerowane'):\n", " os.makedirs('wygenerowane')\n", " print(\" ! Tworzę katalog wygenerowane \", file=sys.stderr)\n", "with open(f'./wygenerowane/{nazwa_pliku}.tex', 'w',\n", " encoding='utf-8') as LaTeX_file:\n", " print(LaTeX_source, file=LaTeX_file)" ], "id": "b0d3947f4938122d", "outputs": [], "execution_count": 71 }, { "metadata": {}, "cell_type": "markdown", "source": [ "---\n", "\n", "## Generuj plik Pdf z pliku Latex" ], "id": "5654250ece733965" }, { "metadata": {}, "cell_type": "markdown", "source": [ "\n", "```{admonition} Wskazówka\n", ":class: tip\n", "\n", "Można wygenerować gotowego pdf-a.\n", " \n", "W mybinder (interaktywna sesja) plik pdf jest w menu po lewej. Można pobrać. \n", "W mybinder wszystkie pakiety LaTeX-a są zainstalowane. U siebie pewnie trzeba będzie coś doinstalować. \n", "\n", "Kompilacja wykonuje się dwukrotnie.\n", "\n", "```\n", "\n", "```{admonition} Uwaga\n", ":class: attention\n", "Przy ponownej kompilacji online pliku z taką samą nazwą podgląd pdf-a w przeglądarce może pokazywać poprzednią wersję. \n", "\n", "Trzeba wygenerowanego pdf-a pobrać na dysk lokalny i tam go oglądać.\n", "Albo podejrzeć niżej pdf-a przetworzonego na jpg.\n", "```\n" ], "id": "b129b40b982120cc" }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-29T22:15:53.795056Z", "start_time": "2025-01-29T22:15:51.443369Z" } }, "cell_type": "code", "source": [ "import subprocess\n", "\n", "for i in range(2):\n", " result = subprocess.run(\n", " 'cd wygenerowane && pdflatex \"' + f'{nazwa_pliku}' + '\".tex',\n", " capture_output=True,\n", " shell=True)\n", "\n", "# w razie problemów poniżej można zobaczyć komunikaty\n", "# trzeba odkomentować \n", "\n", "# for i in str(result).split(', '):\n", "# print(i.replace('\\\\n', '\\n'))" ], "id": "86469940cc43c2f4", "outputs": [], "execution_count": 72 }, { "metadata": {}, "cell_type": "markdown", "source": [ "```{admonition} Uwaga\n", ":class: attention\n", "\n", "To co widać niżej to jest obraz stworzony z pdf-a.\n", "Kolor odpowiedzi jest 'blue' - czyli widoczne, do ukrycia po kliknięciu (w pliku pdf).\n", "Kolor można zmieniać w pliku tex. \n", "\n", "Widoczna jest tylko pierwsza strona. \n", "\n", "```" ], "id": "85028946c1b64a27" }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-29T22:15:54.698622Z", "start_time": "2025-01-29T22:15:53.811622Z" } }, "cell_type": "code", "source": [ "image = convert_from_path(f'wygenerowane/{nazwa_pliku}.pdf')\n", "image[0].save(f'wygenerowane/{nazwa_pliku}.png')\n", "display(Image(f'wygenerowane/{nazwa_pliku}.png', width=800))" ], "id": "763e92f1aeb5b4cb", "outputs": [ { "data": { "image/png": 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hw4eJ6MGDB02bNtV2dr4j9evXf/bs2YkTJwYPHqz2SlT/+tzd3bt162ZiYnL8+PEuXbqovUUAAAAAAIDs6Wk7AwBQGOno6JQsWZKI9PX1dXV1tZ2dvGdiYsI/FC9eXLs5+d6YmppqvhIVv77Q0NDhw4fXrFnz3Llz1tbWmm8XAAAAAAAgKwixAYByly9fPnLkSLdu3fT0imBBsW7dugYNGlSoUKFu3brazsv3JU/e1lTl60tLSxs8eHCjRo2OHTtWqlQpzTcKAAAAAACQDbwoCgAABaddu3ZeXl4aviiqigMHDvj6+jo5ORXJZpgAAAAAAFDYFMHGKQAAAGPHjh07dqy2cwEAAAAAAN8LjCgKAAAAAAAAAACgEbRig6Lg/Pnz4eHhUVFRkZGRTZo0GTVqFBH5+Ph4eXm9fPmycuXKbdq06dixYzZreP369YEDBx49eqSjo6Orq9utW7dRo0YJXaqr6ObNmyEhIZGRkZGRkba2thMnTkxNTb106dKtW7csLS1nzJihuMI7d+4cOXLkzZs3RGRmZtavX79Bgwbp6HwV+5ZKpefOnctmu7q6ut27dzcwMLh69apUKjUwMNDV1ZVKpRKJxNLS0tbW9uLFi/r6+vr6+kSUnp4ukUhat25dunRpIrp8+bJIJNLX19fR0ZFIJHXr1rW0tLx169abN28iIyOjoqKsrKx++uknxY3eu3fPzc0tLi4uOTm5evXq9vb2TZo0UZo9T0/P2NjYrDIvEomaN29evnx5IYUxduzYMQ8Pj7i4OKlUWrdu3cmTJ8vOIFDvew8ICPDz8+N7l5iYuGXLFqUZu3nz5uHDh6Ojo9PT08uXLz9q1KgOHTpktRdZSUhI2L17d0hISEZGhkgkMjc3nz179rlz5ywtLbt168a38vbt26ioqKioKGNj46VLlzLGLl++/PDhw7dv31paWvbq1atly5Zyq3327JmwC4wxR0dHIrp169bVq1czMjKmTJlSo0YN2flz/LKSkpI8PDz4SSISiaRSaVpaWsOGDXV1dX19fQ0MDPT09PgZlZGR0adPH75rt2/fNjAw0NfXZ4ylpaV17NhRbuTZd+/enTlzJigoiDHWoEGDESNGGBsbKz1Qnz598vb2zuZIli5dun379qTy15eYmOjq6nrt2rWYmBgzM7MGDRpMmzbNwsIim00AAAAAAABohAF8+4YOHdq4cWN+Ss+cOTM5OXn69OkbNmx48eLFu3fvli5dSkTjx4+XSqVKF9+1a1fNmjWPHDkSFxfHGAsJCXF0dKxTp46Pj0+usjFjxowWLVrwANn48ePj4uIGDx589uzZvXv3ikSiHj16yM6cmprq4ODQsmVLDw8PsVickZHh7+8/fvz4zp07h4WFyc4ZHR3Nd61v374bNmzYu3fvof/r3LkzEVlYWMTGxjLGfvzxx/+xd+dxMa5/H8C/076rpMVOhOzKduxkJ7KLY4lj3471EMd+bB0cJ0uUPVmzJUtZylakdBCVyhJaVCotM1NzP3/cnvmNe6ZUU82Uz/v1vJ7X7bru5Xs34/z4uBYbGxvx7+527drt37+fXfRdvJ2ivr5+nz592NSDYZiBAweKdzNo0aLFvXv3GIaZM2dO27Zt2WXpBw0aJP2m8+fPX758eWJiIsMwQqFw9+7durq6M2fOzM7Olj65efPmRNS6dev169e7u7sfO3aMLX727NlEpKKiwj6UlZGRMWjQIA8PD/ZWX758mTFjhoGBwb///it955J97gcOHOjevbu2tjYR6enpSd82Nzd3+vTplpaW165dY1uuXLlSpUoVZ2fn/Px86fMLcuvWrTZt2vj6+opbYmJiunTpoqKi4u7uzrbMmjWrY8eO7Edga2sbHh7er1+/Xbt2vXz5Mjo62sXFRVNTc+TIkew3U2zfvn09evTQ1dUlorp164pEogULFuzYsSM4ONjAwKBatWrp6enik4vyYX348MHBwaFu3brsD1NDQ6NXr14PHz58+vTp0KFDq1atyrbXrFnTwcGBvSQuLq5Vq1Zsu6qqateuXSWLzMvLW79+fdWqVXfs2BEXF/f58+fr16/b29v7+Ph07tyZiM6ePSv5RufOnSMiTU3N3377zdXV9ciRI+yX5OjRo9WrVyci8W+fH358DMOEhoY2aNCgU6dOoaGh+fn5SUlJvr6+AwcO9PT0LPrHBwAAAAAAUCyI2KDymDlzJhHNnj172rRpz58/l+xihzLdvn1b+qpJkyYZGxtHR0dz2i9evKiurn706NHilrF582Y22ZkxY0ZISAjDMM7OzkRUv3598Tnp6ektW7Zs165dTk4O5/KFCxeamJj8999/4pbXr19LRxIMwzx69IgdcyROglhDhgyRjCRYQqGQHbgk/UYnTpwgou3bt0tXIjNi27dv359//slp3L59OxGNHDlS6ufBsOPgOOFUWlpanTp1iMjZ2VmyfezYsf7+/pw72NvbE9Hu3bulb86U9HO/dOmSzIyGz+e3bdvWyMjo3bt3ku1ubm5EtHz5cpk1SMvIyKhRo8aFCxc47UlJSSYmJuKIjbVz5042Ku3Ro0dqaqpk15UrVzQ0NOrWrRsbG8u5VVBQEBuxeXh47N27l2EYPz8/IuLxeG/evGHPKdaHlZWVZWFhQUSbNm2SbP/w4YOWlhYRcX4mDMNMmzZNR0dHMiRlGCY9Pb1Dhw5VqlThhNR5eXnjxo2rVq2a9PfZ3d3dyMhI+rfhjh07iMjExOTDhw+S7QV9fAzDXLhwgc0l8/LyJNuTk5NtbGzCwsKkLwEAAAAAAJAfIjaoPNicwtraesuWLZyujRs3EtGUKVM47WfPniWibdu2ybzhwIEDDQwMOGPKfujChQtE1KlTp/nz57MtiYmJK1euvH//vvicOXPmENGjR4+kL8/KyjI1NW3fvr04k3r06FGnTp04p6Wnp9evX5+IFi9ezOm6evUqEZmamnIiBjYkmj17Nuf8Dx8+dOjQQboS9nzpiI2dL8m5T15eHjsH8OnTp5zzTU1N2UF2kkaOHElEHTp0EAqF4sacnBxNTU0ej3f69GnJkx89ekRE1apVkzxZrASfO8MwkZGRMjMadtLlhg0bOO0ikcja2lpXV5cdDvZD165dI6Jnz55Jd82cOZMTsbGBkZaWFidIYrERLSczZRjmy5cvRFSrVi3xZyQSibZu3Xrs2DHxOcX9sJYtW0ZEw4YN47SPHTuWiM6cOcNp9/T0/OOPPziN06ZNI6JDhw5Jv0tGRkaVKlWkI7atW7dywlaGYZ48eaKhoUFEly9f5nQV9PF9+fLF3NxcR0eHnUIrJhQK2Vmiq1evlq4KAAAAAABAftjuACoPdrZdRETEjBkzOF3swJk3b95INmZmZrKrjBW07eC4ceMyMjJkrkT2wzLu378/fvx4tsXU1HT9+vW//PIL+8vHjx/v2bPH2tq6bdu20pfr6OgMHTo0ODh47969bEtaWhq7CpWkmTNnxsbG2tjYsCmSJDs7u2rVqiUlJd28eVOy3dTUlIhOnz6dl5cn2X758mV2ziaHqqqqzBfMz88nopiYGM7JTZs2JaLnz59LtgsEAktLS0NDQ8nGAwcOnDlzxsDA4MSJE+JpqkQkFApVVFQYhomNjZU8v1mzZioqKsnJyYmJidL1FPdzL+TtPn78uGHDBiIaPXo0p4vH4/Xs2TMrK+v48ePSF0r7/PkzER05ckS6y8bGhvN0dnJx9erV2UmRHLNnz9bQ0Lh+/frp06cl29kXf//+vXiROB6Pt2TJEvEXj4r5YRGRo6MjEV25ciU9PV2ynf3ysAMeJfn4+LCjCMVu37594MABU1NTNpXj0NfXr1mzpnS79Jc8Kytr7NixAoFg3rx5gwYN4pxf0Jdz1apVCQkJI0aMEE9uZTEMw/4o9PT0ZF4IAAAAAAAgJ0RsUNnUrl1belcBdixMbm6uZOO5c+c+fvxYs2ZNNj6Qxq7zde7cuU+fPhW3DHV1dcll0STt3r1bJBIVtDmA+Lmurq7sL8UD1sQOHz584sQJPT09Ly8v9tUkqampjRo1iog8PT0l20+dOmVra5ucnOzv7y/ZfuHCBXZuaRHt379/06ZNe/bs4bTXqFGDiMQrx7HS0tIaNGgg2RIREbFgwQIicnNzq1evnmSXvr7+xYsXd+zYMWvWLMl2bW1tdnMGzs0lFf1zL8Tp06ezsrIMDAw4NbNsbW2JKCoqqii36tixo4qKiouLy8iRI2/fvs3n88Vdjo6OY8aMkb6Es82FmIWFBTvjVfpnLn5WQWUU68MiohYtWjRt2pTP53t7e4sbRSJRUFCQmZmZr68vO3SOlZWVlZqaWrt2bck7sDOO+/Xrx9n6QIxd448jMzNTvBIca/bs2VFRUS1atNi6dWtBb8chEAgOHjxIRNKRtLq6+oMHD86ePcsOIAUAAAAAACh12FEUKhvOXoqS2GEsYnfu3CEiNruRie1iGCYwMFB6WFPhqlevLjNKKPpzX716lZiYaGZmxs6pFIuKimJjgt27d4s3MeBwdHTcvXv3+fPn9+3bxy4M//Xr18TExOXLlw8fPtzT07Nfv37smfHx8YaGhgXt8yhTkyZNmjRpIr48LCwsNjY2LS3tv//+I6LMzEzJk83MzI4ePSr+ZW5u7pgxY7KzsydPniwzZurduze71aZIJIqKigoLC/v48WNaWlpWVpb0zSUV/XMvRHBwMBHp6+uzq+9zvHr1igoYEyetfv3669atW7ly5dmzZ8+ePauhodG8efOuXbtOmzatcePGRaxHrEGDBjdu3Hjw4IFIJJJO4ti8TKZifVgsR0dHZ2fnEydOTJ48mW0JCAjo3r17dnb2v//+e+7cuSlTprDtFy9elA5n2a+3zIyyEP/++6/kL0+cOHHkyBEdHZ2TJ08WFNVJCwsLY78nMr8MDRs2LOj3CwAAAAAAgPwwig0qm4JmkEn7+PEjEbHbMsoknlP24cOH4pZhYmJSFs8VCARjxozJyspydHQsaH4rEXXs2LFu3bqZmZk+Pj5sy8WLF4cOHTpgwAADA4MLFy5kZ2ez7adOnZIZdRUuJyfHxcWlWbNmdnZ2AQEBTZo0mTp1Kjv3sHCLFy9+9uxZw4YNOZGKpI8fP/7+++81atT47bffYmNj2VXtCvlxsYr+uRciKSmJiKpUqaIlS+fOnX18fP7+++8i3s3Z2fn69evstpsCgeDJkyc7duxo0aLFsmXLRCJRsQpjh/sJhUJ2/ilHIV82Kv6HxU7wvHXrVkJCAtty4sQJR0dHtl1yaOSZM2c4+W9GRsbXr1+JqFihLUdsbCw7+XTnzp3ifLAoxKNN2eXeAAAAAAAAyhNGscHPS11dnQqdRcgwDHsgPRnzhwoawsY+VygUluy5y5YtCwsLq1evnnilNiL6+vWrmpoau+ej+Oljx47dtGnTiRMn2BDk9OnT+/fv19LSGjZs2OHDhy9dusQma76+vuz2CEX39OnToUOHJicn79mzZ8KECeI3lVxVTaaLFy/u3r1bQ0Pj5MmT4shMKBRmZ2eLMxFPT8/p06ebmZmdP3++Q4cOkm9UrCJLhg03NTU1Bw4cWCo37NOnT58+fT5+/Hj37t3g4GBfX9/IyMitW7dWqVJlxYoVRb+PeLiZ5KcsVsgPpwQfVr169Tp27Pjw4cNTp07Nnz9fKBRGRka2aNGC7QoICPjw4UONGjVSUlIYhuGke+zvKSISCARFfztJQqFw7NixGRkZI0aM+O2338TtKSkpxsbGhX8N2AGb8jwdAAAAAACgxDCKDX5e7AJnnGXdJaWmpkqeWc7PVVVV5axy5evr+88//6ipqXl5eUmuO+bi4vL48WPOTcaNG8de8uXLl9TU1Pz8fHZHRcmxSJGRkfXq1StWgBgfH9+zZ8+3b996e3tPnDix6MlXfHy8k5MTEW3atElyHbr79+/v2rWLPfbx8Rk/fryGhsa9e/ck87Vyw04klDlSrLju3r0r/lCqV68+evTo7du3R0REsMP3XFxcOJtOFC46OpqITExMpNebK0SJPyz2y8NubnDt2rX+/fuz7WPGjBGJRCdPniSis2fPjhgxgnOhtrY2+zUT/94prpUrVz569Kh27dr79++XbGeXFyyceGk/djQiAAAAAABAeULEBj8vNjj48OFDQQPKIiMjiUhLS0u8Y2MpPpezyaP0c3/55RfJPOXTp0+TJk1iGGb9+vXt27eXPD82NlZ6x4amTZu2aNFCIBCwa4GJJ/T16tXL1NT0+vXrKSkpXl5e7A6SRbdx48a0tLTu3bv37duX08VZ2Etyl1KRSDR+/PjU1NR+/fr9/vvvBRW/ZMkSIpo/f76FhYXkOSKRSDyzlYiePn3q7u5erLKLaPDgwUQUHx9fSMomc1dTaREREdL7b6qoqMyZM2fSpElpaWlv374temHs1p/SG2sWrmQfFhGNHDlSTU3t0aNH0dHRJ0+eFO8Nyn5b2Pe6cOHC0KFDpR/KLvP38uXLYpXK8vPz27Ztm6qqqqenJ2exwszMzB9GhFZWVmx+LZ04i717964EhQEAAAAAAPwQIjb4efXv379bt24CgeDWrVsyT7hx4wYRrVq16ocLgRXL4sWLTUxMHj16VND+mDdu3ODxeBs3bhS3iESiX3/9NTk5uWfPnkuXLuWc/+TJE5mboooDkfPnzzs4OLCNqqqqo0aNEgqFZ86cuX37NrtVZdHdu3ePiGRulhoeHi75y9jYWPHxhg0bAgICzMzMjhw5wglKQkJC2OJTUlLY/QSkbx4dHS0ZsWVlZRUx5yqubt26DR48mGGYy5cvyzzh4cOHf/zxRxHvdvbsWfGcX0nsz1x6AbWUlBSZa7TduXPn2bNnenp6q1evLuKjWSX7sIjI1NTUzs6OiA4cOPD582fxXp/NmjVr1qxZaGiov7+/oaGheNFASStXrtTS0goICChotia7WJu0pKSkCRMmMAyzatWqzp07S3YlJCQIhcIC3vI7mzZtooJ/8gKBgB1KKZafn1+C/YIBAAAAAACkIWKDyoPdOFLm9pEyG3k83sGDB3V0dDZu3Ch9QmJi4qFDh1q1aiUdaZW4DJapqamrq2tubu62bduke2/evBkSEjJ79uwuXbqIG7du3Xrz5k0TE5Njx45xNpQMCAiIjo6WuT/pmDFjeDxeQEBAlSpVJAfEsdHbhg0b2rRpI709JedFOKEPu4x9Wloa5+THjx+zXWwWlp2dXbVqVbbr3r1769at4/F4R44c4USBmZmZnp6e7NRCbW1tdssC6ZtfunSJ3SOSvXlGRobkfYr7uRfydkS0d+/eKlWq/PXXX5KhHothmA0bNixcuLCge3LEx8efOnVKuj0hIaFx48bSS/KnpaX5+flxGkUi0Zo1a4ho8+bN4qhL8hWo4Ncs5MNiU2PpD0uM/ZLs2LGDs2co2z5p0qSCdslo0KDBypUr09PTZY40PHfuHLslK5/Pl2xnGGbixIkJCQldunRZuXIl5yo3N7dq1apJthT08Y0aNWrgwIEvX748fvy49NMPHjw4fvx48S8zMzMtLS1r1qz5zz//yHwXAAAAAACAYmAAKoXc3Fx2OpuFhcXHjx8lu/h8/pQpU4ioWrVq79+/51x4/fp1c3PzGTNmCAQCcWNCQkLHjh3bt28fExNTrDKEQiE729HAwOD58+eFnLlt2zYtLa0jR45INj5+/NjU1HTSpElZWVnixocPH7KL01+6dIlzk6tXr1avXr1GjRoFPYUdDXThwgVOO7tq1aNHjwq6kM/nT5o0iYgsLS2Tk5PF7d7e3mpqanp6eq9evRI3JiQkDBkyJCwsTFVVtV27dvn5+T4+Prt372YYJjU1lV1RbtGiRZxHREVFsQuuRUdHsy1setWyZcvs7Gzxaffv3585c6arqysRLV68mGGYpUuXPnv2jO0t2ecuEokOHDjA/jcwNDSUU1hAQECtWrX69ev36dMncaNAIJg6deqePXsK+olx7Nu3j4hq1ar133//SbbHx8dXr1795s2bko3sxq8WFhY9evR4+vSp5EPHjBmjpqb2559/ikQiziMuXrzIvoKnp2deXp50DYV8WI8ePeLxeJwPS1JGRoa2traamprkp88wTFxcHBEZGhry+fyC3l0gEMyePVtPT+/BgweS7WfOnBk4cKCVlRURsXsaiLtcXFyIyMjI6N27d5KX5Ofn//3335qamuPGjRM3Fv7xJSUl9enTR1dX9/jx45LtPj4+EydOzM/PF7dcuXKFvUn37t0LehcAAAAAAIAi4jGyZtMAVCy1atVKT0/n8Xg8Ho/9ZquoqFy4cKF79+4NGzZk1z4Xd/F4vFOnTrErRrFSUlLmzZv333//OTg4mJmZvXz58urVq5MmTVqxYgU7rqqIOnToEBERIVkGERkbG7PDdqT9999/M2fO1NTU7Nevn6am5oMHD54+fbp582bxpE5W7969/f39dXV1xePaGIbJycmJi4t7//49EbVu3To0NFTmI/bt2+fs7Pzp0yfOngYrV648ffp0VFSUzKs4P7S8vDwejyee33fz5s0//vgjKipq1qxZjRs3fvnyZXx8/Pbt201NTYcMGXLp0iULCws7O7uDBw+qqalt2LBh1apVRNSjRw9NTU32Dnw+PyEhITIykh2FlJ6eLh5k9++///7999/q6urTpk2rWrVqcHBwlSpVNm3aJBKJRo0adeHChcaNG0+ZMmXx4sVU0s998uTJ3t7e4i42c7l27ZrksMEvX74sWLDA19d39OjRVlZW8fHxDx48WLt2bc+ePWX+xKTt37//5MmTp06dmjFjhomJiY2NDcMwERERjx49Wr9+PTsNU+zKlSuDBg1q0KDBo0ePFi1apKGhYWtrm5WVxc6r3bt3b7t27STPX7x4MbsbgOS7q6mpBQQENG/eXPLMQj6sLVu2ODs7s3NC2Q+L8wpjxoz5+vUrG/9J6ty5s7W1NWc7AmkXLlxYvHhx9+7dO3XqlJubGxQUVK9ePWdn5zZt2rBLyxGRtbX1ixcvcnNzTU1NMzMza9Wq1bRpU7ZLJBJlZma+fPnyy5cvRPT7779v376diIry8TEMs3Pnzo0bN9rY2LALKT5+/Lhly5arV6+WnKeclZXl6Oj433//7d69e8CAAYW/DgAAAAAAQOEQsQF8k56e/t9//3358sXc3LxVq1bq6url89z4+PhXr14JhcK6des2adKkdG/O5/MjIyNbtGjBaU9PT09ISGjUqFGJ7/zu3buYmBiBQNC4ceM6deoQ0Z9//vngwQMPDw91dfXq1auX+M5sFPX+/Xttbe3mzZsbGxuLuxITEzU1NQ0NDUt882JJT08PDw/PyMioVatWixYtir4jJxElJCRoaWmxpcbFxT1//lxfX9/KykrmT0YcsbE7hyYmJj5//lwkEllZWbE/WzlJf1iszMzMzMzMgj4sdgW0WrVqcdpjY2OrVKkiPbdUGsMwz549e//+vZmZmbW1tY6ODhEFBASoqqoa/79ibWhbLHl5eWFhYQkJCVWrVm3RooXMleMAAAAAAABKCyI2ACgFa9euDQkJOX/+vPRgKPghTsQGAAAAAAAAFQ7+MgwA8nrz5s3GjRvDw8ORrwEAAAAAAMDPCTuKAoC8bt68KRQKJedRCgSCEydOKLCkigWjiQEAAAAAACo6RGwAIC92Wa5Zs2YlJiYSUW5u7uzZs4u1ctlPjl3R/8uXL8jaAAAAAAAAKiisxQYA8srLyxswYICfn5+mpmbTpk2joqJGjRrl4eGh6LoqgB07dsTGxl64cCE+Pp6I+vbt27x58zFjxtjY2Ci6NAAAAAAAACgGLJwEAPJSU1O7evXq0aNH79y5Q0QrV650cHBQdFEVg4mJiZaWFrtdKcMwDMPw+XwtLS1F1wUAAAAAAADFg1FsAAAAAAAAAAAAcsFabAAAAAAAAAAAAHJBxAYAAAAAAAAAACAXRGwAAAAAAAAAAAByQcQGAAAAxXDs2DELCws7OztFFwIAAAAAoEQQsQEAAECRfPjwwc7ObsKECQkJCfXr11d0OQAAAAAASgQRGwAAAPxYSEhI27Ztb968SUR//PGHm5uboisCAAAAAFAiaoouAAAAAJTdzZs3Bw8enJOTQ0Tu7u5TpkxRdEUAAAAAAMoFo9gAAACgMGFhYQ4ODmy+tn79euRrAAAAAADSeAzDKLoGAAAAUFJpaWnNmzf/8OEDEQ0ZMuTChQuKrggAAAAAQBlhFBsAAAAUaN68eWy+pq+v7+rqquhyAAAAAACUFCI2AAAAkC0gIOD48ePs8cKFC2vWrKnYegAAAAAAlBYmigIAAIBsvXr1unXrFhFpaWm9e/euWrVqiq4IAAAAAEBJYRQbAAAAyPDs2TM2XyOi3r17I18DAAAAACgEIjYAAACQ4fTp0+Ljnj17KrASAAAAAADlh4gNAAAAZLh69ar4uFevXgqsBAAAAABA+SFiAwAAAC6BQPDs2TP2uEqVKs2aNVNsPWL5+flZWVmKrqL0PX/+PCoqStFVlL6cnBxFl1CGKvfbcaSkpAQEBCi6CgAAAKWmpugCAAAAQOm8efNGIBCwx3Xr1uXxeIqth2GYPXv2HDp0iMfjaWhoJCYmDhkyZNWqVYaGhootrFRkZmb2799/5syZK1asUHQtpSA7O3vXrl3Xr1/X0NAQCAQJCQn16tVbuHChnZ2doksrBZX77Qrh5OSUk5PTrVs3RRcCAACgvDCKDQAAALjS0tLEx0ZGRgqshIjev3/fq1evHTt2nDhx4vHjx/fv33/48GF4eHjLli3fvHlTyIVRUVGjRo0KCQkpr0pLaNmyZfHx8ZVjk/fXr1/36NGjRo0aN27cuH79+u3btx88eKCmpta7d28HB4e8vDxFFyiXyv12hTh9+vSlS5cqx1cUAACg7CBiAwAAAK7c3FzxsZqaIse85+bm9urV6+nTpzdv3rSysmIbq1Wr5u3tTUQDBw4UCoUFXbt27dozZ84EBgaWU60lcvfu3X379im6CiIiPp8v5x1yc3N//fXXI0eO/Prrr+rq6myjkZHRsWPHDA0NL1y4MH/+fLnLVJjK/XaFSE1NnTdvnqKrAAAAqAAQsQEAAIDyWrt2bXR09LRp0+rUqSPZbmBgMHXq1IiIiH///bega2/evElEyjy1LTc3d82aNZ06dVJ0IRQVFcX5CZfA/fv3g4KC+vbtGxcXJ9lepUqVNm3aENGRI0cKiUSVXOV+u0IsXry4a9euiq4CAACgAkDEBgAAAEoqPj7excWFiGQudGVjY0NEBQ0Be/XqVWJiooGBQatWrcqyRrmsW7du3rx5yrCiXFpaWmJiopw3iYiIIKJ3795duXKF08WOQMzKynr16pWcT1GUyv12Bblx44ahoWH79u0VXQgAAEAFgIgNAAAAlJSvry+7vlX16tWle9mhQ9HR0TJXW2Pnh3bt2lVVVbWMyyyh8PDwt2/fDhkyRNGFlBp7e3tbW1sbG5thw4ZxutLT04mIx+PVrVtXAZWVhsr9djJlZWVt3rx5/fr1ii4EAACgYsCOogAAAKCkoqKi2AMdHR3pXnNzc21t7ZycnEePHtna2nJ6AwICiKh79+5lXGMJ5efnL1my5NixY4oupDTVqVPn8ePHMrtCQ0OJyMbGRl9fv3yLKjWV++1kcnZ2/uOPP3R1dRVdCAAAQMWAiA0AAACUVEZGBnugqakp8wRdXd2cnJynT5+yv3zw4EFycjJ7fPv2bSLi8/kXL14kImNj4y5dupR1wUW3fft2R0dHMzOzopzMMMzFixfv3LmTn5/v4ODQs2dPtt3f39/X15dhmH79+vXt27cs65VLeHh4ZGQkEW3dulW69+zZs7dv365bt+6sWbOk0xw+n//kyZP27dsr82jEQt6OJRAIfHx8AgMDu3TpMnz4cHF7bm7umTNnfv311/IotJiCg4MzMzP79Omj6EIAAAAqDERsAAAAoKRMTEzYA4FAIPOEnJwcIhIvP3/48OHMzEwiSkpK+vTpk7a29rNnz549e0ZEVlZWyhOxvX79+t69e2z290O5ubm//fZb+/btnZ2d37596+DgMG3atFWrVi1btszAwGDp0qWJiYmDBg2aM2fOsmXLyrryEsjLy1u0aBGPx9u6dWuPHj04vQsXLmzQoMGaNWvGjx/v7e19//59FZXvljGZOXPmoUOHrly5MmDAgHKsuqgKfzvW169fHRwc/P39ieiff/5ZsmSJOIxzdnZWzu04BALBihUrzpw5o+hCAAAAKhJEbAAAAKCkWrduzR5kZWVJ96amprLtbKxGRPv372cP9u7de+vWrS5dunh5eZVLpcXAMMyCBQsK2QhVkkgkGjdu3IoVK9i9HapVqzZx4sR169YRkZaWlrOzMxGNHDkyPj7+5s2byhaxpaenP3/+/K+//oqPjz9z5ozk6C3W0aNH69WrN2vWLCIyNze/ceNGaGio5Jzf/Px8b29vIqpfv355Vl4UP3w7sXnz5kVHR8+YMcPU1DQ0NHTnzp05OTnjx48/e/Zsdna2vb19eZZdRH/99df06dONjY0VXQgAAEBFgogNAAAAlFSfPn2MjY1TU1P/++8/a2trTq94YSzxfFKxO3fukLIuxObu7t6rV6969eoV5eQDBw7Y29uz+Rqrbt26eXl527ZtE28AyjCMqqrqjBkzyqTcEnn16tWyZctSU1PfvHnDMMy2bdv69evHOUcoFB4+fJgd20X/v5yZUCiUPOfp06fp6enm5uaNGzcun8qLoihvJxYTExMVFfXq1SstLS22JS0tzcfHZ/v27QkJCX5+fkV8aI8ePe7du1eygnk83pIlSzZu3FjE81+8ePHixYs1a9aU7HEAAAA/LURsAAAAoKSqVKmyefPmadOm3b17d8yYMZze06dPm5mZJSYmikQiTpfSRmyfPn06e/bs1atXi3KyQCA4dOjQw4cPJRtjYmKIyN7eXltbm225c+dOVlZWlSpVSr3aEmvcuLF4GmxcXNzYsWNnzJixb9++sWPHis/x9vbu06cPOy00IiLi+fPnxsbGkmEi/f+Cesr2ORbl7cT09PTOnz8vzteIyMjIyNbW1tPT89KlSxoaGkV8qIeHx/v370tWMI/Hk06oCyISiRYuXHjw4MGSPQsAAOBnhogNAAAAlNfUqVNPnTp17NixhQsXWlpaituvX7/eoEEDfX39xMTEqlWrSl7y8uXLpKQkXV1d6W1GFW7BggUuLi6c5cYKkpqaOn/+fB6PJ9n44MEDIurdu7e4RU1NTanyNY569erdvHmzRYsWjo6Oz549++uvv9h2oVAoXuaf3Vl1xIgRnMhJaaNSsYLeTkx6R4vIyMhFixYdP368WJ9a/fr1y2e27L///jts2LAaNWqUw7MAAAAqGURsAAAAUJoSEhJatGghXh+tWBo3bhwWFibZwuPxrl69unz5cjs7u927d/fs2ZPH4509e/bmzZvu7u47duwgomrVqklewuYynTp1UldXV1TZMp09e7ZRo0bNmzcv4m3Nzc05A6Nyc3ODg4OJqKCV9Qtx+/btiRMncmZiirEbSlhYWBR0ub6+flBQUMkW59LV1Z0zZ87ChQs3b97ct29fdoH/8ePHs70Mw3h6ehIRZ6Bifn7+3bt3qWgRWzl8fAWR+XYFefz48caNGz09PY2MjEr8xLLz9u3bGzdu+Pj4KLoQAACACgkRGwAAAJQmc3Nzd3f3koUdderUkW5UV1d3cXEZNWrU6dOnDx8+bG5u3qtXL3YiW3p6OknsisAq2dCnUi+bIy0tbc+ePUWcIlqQoKAgPp9vaWlZu3bt4l7btm3bf//9Ny8vT2ZvVFTUihUrXF1dC7pcT09PnsXvBw4cuHDhQoZh1qxZw07/FHv48OH79+/Nzc054VRoaGhGRoa5uXmjRo1+eP+y/vgKV8jbSbpx48bx48e9vLzEk3yVzfz583fs2MEZOAkAAABFhIgNAAAASllZbJLYrl27du3aSbZ8/PiRHXvVsWNHyfaAgAAq0ezCMt3bccuWLTo6Ohs2bJDuevXqFRH5+fnl5uYSkYmJyfz582XehE0PSzCEjYj09PSGDBlSUC87OK6QbTGLIjo6+tWrV/369ZMeP1izZk324MmTJyKRSHKqLDtmasSIEZz5s8V92bLemrNkbyd2/PjxkJCQw4cPF3GacPm7du1aTEwMO2mXg10Q8PXr16tWrWJbli1bpqenV671AQAAKD1EbAAAAMAlOdYpOztbgZUU4smTJ0RUtWpVydFPL1++TExMVMKF2Hr27MmZ0CqWkpJCRNra2oaGhkRkYGBQ0E3Y9LDwqYiK8uLFi5YtW+bn5zs5OXl4eHB6dXR0NDQ0BAJBZmbm58+fTU1NxV3srpqSq8uxlGqvgxK/HWvnzp3p6ek7d+4scQFTpkxhN10tAR6PN3ny5Llz5xZ+Wp06dSZNmiSzix0uqq6uzn5FiUhpg0IAAAAFQsQGAAAAXJJ/f1ZVVVVgJUSUlJQknVnQ/0czkyZN0tTUFDeyIRRnIbZt27Z169aNMwiunPXp06dPnz4yu1xdXdPS0jp37rxo0aJC7sDn84OCgoioS5cuku0xMTFubm5bt24txWpL4MWLF/n5+fT/A+I4srKy2CGH2traJiYm4naRSPTs2TMiat++veT5eXl59+7dI2WK2ErwdqwNGzZoaWmtXr2a0x4REXHw4EEXF5eiFDB27Ng2bdqUpHQiHo/XtWvXH57WpEmTJk2ayOx6/vx5SEhInTp1Cv+KAgAA/OQQsQEAAACXZMQmGWCVv+XLl2/evFl66FBubi67ZvzSpUsl29kZbZK5zNevX2/evLl48eJyqbc0xcfH371718HBQUtLi4hu3ryZm5traGjIWTts586dw4YNU1CN/9O4cWMVFZWWLVuuW7dOuve///5jDwYNGiT57crLyxMKherq6pwUNSQkJDMz08LCwsrKqkzLLqKSvR0R7dy5UyQSSX/98vPz582bt2LFiiIWYGdnZ2dnV/zCAQAAoPxgjDcAAAAor8DAQCJ6/Pgxp339+vWpqanbt2/nRDPspEv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", 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