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devvyn/-chess-sandbox.ipynb

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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": " chess-sandbox.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"interpreter": {
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
},
"kernelspec": {
"display_name": "Python 3.9.7 64-bit",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/devvyn/ef306e54d590bad76bf4ba92745f6810/-chess-sandbox.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4iv4tQjV7oH_"
},
"source": [
"# Project chessviz"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2XauvHmX_dNm"
},
"source": [
"## Introduction\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mw2U5H_GBhL5"
},
"source": [
"**Target Audience:**\n",
"- Anyone curious what Python looks like in action in a very simple level of quick demonstration\n",
"- Anyone curious about the usefulness of computation notebooks like this one for talking about code or showing data.\n",
"\n",
"**What Happens:**\n",
"- We demonstrate that Python can help humans think about chess in real life\n",
"- We arrive at a minimum viable product, a core piece of code for a future application that can accept a text snippet and show you a chess board diagram\n",
"- I make mistakes and admit them\n",
"- You see what the process looks like from start to finish"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "llGf4brpe9j4"
},
"source": [
"## Hello, curious developer or tech enthusiast. Journey with me through something new and interesting!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6rD30zph5aSu"
},
"source": [
"I opened this notebook in Google [Colaboratory][colab] to play with a Python chess library I just heard about, because I've gotten good results from prototyping and playing in [Jupyter] notebooks in the past, and I'm excited to try out the notebook-driven research approach, this time in the familiar platform of Google Apps and Google Drive.\n",
"\n",
"My computer (2011 MacBook Air) is having a difficult time with Docker, PyCharm, and even the Chrome web browser. Still, if I only launch one browser tab to use Colaboratory, most of the computation to support my work is on Google's servers, not my tired old portable. I feel I should be able to keep working until newer equipment is on hand.\n",
"\n",
"[![Jupyter logo](https://jupyter.org/assets/nav_logo.svg)][jupyter]\n",
"\n",
"[colab]: https://colab.research.google.com\n",
"[jupyter]: https://jupyter.org\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ox4F314mx3_I"
},
"source": [
"## `python-chess`, a library to try!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T9LtxD_CxvcN"
},
"source": [
"I want to play with a [chess library](python-chess) for Python I just heard about, so let's dive in!\n",
"\n",
"[python-chess]: https://github.com/niklasf/python-chess"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "djexSWeBSIXy"
},
"source": [
"### Install `python-chess` Package From PyPI"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KD1RrBPqf9T5"
},
"source": [
"\n",
"Since I know the package name of the library I want to poke around in, and it's not a standard library module, I'll go ahead and install it in the Python environment with good old `pip`:"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aqtoX4X-RA6Q",
"outputId": "26596bb3-223b-4d37-96d1-5262c55bb2b7"
},
"source": [
"%pip install python-chess"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: python-chess in /usr/local/lib/python3.7/dist-packages (0.23.11)\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nXMu9-1NhPFm"
},
"source": [
"Splendid! Normally it would be necessary to install non-standard libraries such as `python-chess`, but it just so happens that Google Colabaratory has this one preinstalled in the Python environment used by this notebook."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qyS78zLwB7FV"
},
"source": [
"### Import and Try `python-chess`"
]
},
{
"cell_type": "code",
"metadata": {
"id": "JAuPftQ6Rswx"
},
"source": [
"import chess"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "HZgAn3rLZSGC"
},
"source": [
"From my brief look at the README of the chess library, I believe the board will initialize as the standard setup before the first move.\n",
"\n",
"I can show the chessboard visually in a notebook simply by stating it as a stand-alone expression."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 421
},
"id": "kF00DVdSSStc",
"outputId": "0f86b601-66a1-4938-d07c-f6526d17a056"
},
"source": [
"board = chess.BaseBoard()\n",
"board"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"BaseBoard('rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR')"
],
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},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FN6TojemabfJ"
},
"source": [
"My threshold of success for this \"chess visualizer\" project I'm prototyping here is to be able to conjure up a board diagram like the one just displayed here with the correct positions of all pieces given a complete list of moves in order.\n",
"\n",
"My brother-in-law is a beginner like me, so we started a casual slow game on a physical board a few weeks ago. We play a move every day or two. Here's the current game as a standard algebraic notation text move list, copy and pasted from a note:"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "201wGWsIHz3p"
},
"source": [
"```\n",
"202109\tKalman\tDevvyn \n",
"1.\tNc3\tNf6\n",
"2.\tNf3\tNc6\n",
"3.\ta3\ta6\n",
"4.\th3\th5\n",
"5.\tg3\td5\n",
"6.\te3\tg6\n",
"7.\tb3\tb5\n",
"8.\td3\tQe6\n",
"9.\tBe2\tBb7\n",
"10.\tBb2\te6\n",
"11.\tRg1\tQd6\n",
"12.\tb4\ta5\n",
"13.\tg4\ta4\n",
"14.\tRg3\th4\n",
"15.\tQd2\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AE_dGcaXH32X"
},
"source": [
"\n",
"\n",
"I have no idea how to apply this sequence of moves into data in the `python-chess` board model, so it's time to take a look at the documentation."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Jbv-hR1msgtO"
},
"source": [
"### `python-chess` Documentation\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0LwQgXKc7Ui5"
},
"source": [
"I started at the the [main] document page for `python-chess` on ReadTheDocs, but found the important starting point on the [core] page to find the fundamentals for the input stage of the intended task. In particular, [parse_san](https://python-chess.readthedocs.io/en/latest/core.html#chess.Board.parse_san) seems promising for the heavy lifting of data entry, or [push_san](https://python-chess.readthedocs.io/en/latest/core.html#chess.Board.push_san), which promises to parse and then push the interpreted move to the stack of game moves on the board. I'd just need to ensure I'm only feeding the model one move at a time and in the correct order to get the bare minimum into useable action.\n",
"\n",
"[main]: https://python-chess.readthedocs.io/en/latest/\n",
"[core]: https://python-chess.readthedocs.io/en/latest/core.html\n",
"[MVP]: https://en.wikipedia.org/wiki/Minimum_viable_product"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "M5jg9El5HjL-"
},
"source": [
"### One Move at a Time"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "F7VESOGPJatz"
},
"source": [
"First three moves again, for reference:\n",
"\n",
"```\n",
"202109\tKalman\tDevvyn \n",
"1.\tNc3\tNf6\n",
"2.\tNf3\tNc6\n",
"3.\ta3\ta6\n",
"```\n",
"\n",
"And the first line contains no moves, so that would be discarded. For now, I'll mentally skip it and try visualizing the board after each move or two."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "b4ko33nSJ_t0"
},
"source": [
"#### Unexpected Error\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 166
},
"id": "77kPy1cJIGe7",
"outputId": "acd64a5a-2d84-47a9-bc9f-fd617f3ab1f7"
},
"source": [
"board.push_san('Nc3')"
],
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "AttributeError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-52c4f7df5ef7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mboard\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_san\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Nc3'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'BaseBoard' object has no attribute 'push_san'"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5QfKFr5tsz47"
},
"source": [
"\n",
"Hmm, I thought that would work. Why is `board` not mutable; why is it only a reference to the `BaseBoard()` object? I mean, don't get me wrong, I'm a big fan of functional programming, so I like this, but how do I make the move and see the board from the initial starting point? Let's see the [docs][main] again, and try following an example that has already demonstrated the expected outcome.\n",
"\n",
"I see a difference I overlooked! I used `chess.BaseBoard`, which has no move stack and cannot therefore represent a chess game, rather than `chess.Board`, which *does* include a move stack and variables for representing a game. (It also has the `is_valid()` function that we'll need if we go beyound blindly trusting the player-provided move list.)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ecAoPEPVAA5L"
},
"source": [
"board = chess.Board()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8JhPEQF0ALy_",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 421
},
"outputId": "954cbe7a-dd3f-4446-a9eb-2e85bb5f4af6"
},
"source": [
"board"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Board('rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1')"
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},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bpMlAYeGBFDV"
},
"source": [
"Now we should be able to show the board after pushing a text snippet expressing one move."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AiBr09wKDcl5",
"outputId": "90898775-8701-48e5-a369-da60364ba248"
},
"source": [
"board.push_san('Nc3')"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Move.from_uci('b1c3')"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 421
},
"id": "nH6yKDPmDme1",
"outputId": "abc0489d-24db-4abf-a5a2-e5fbfa45c28e"
},
"source": [
"board"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Board('rnbqkbnr/pppppppp/8/8/8/2N5/PPPPPPPP/R1BQKBNR b KQkq - 1 1')"
],
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},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HkpUJ3MrD0CR"
},
"source": [
"#### What We Learned From This Unexpected Error"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-hpTUcE2ELJI"
},
"source": [
"There are similar board constructors in the chess library, and using the immutable one as if it's the stateful one will raise an `AttributeError`, which is great! It would be even more trouble if it didn't stop execution almost immediately because we'd still be spending time and energy on it."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ugZOvb84GCD4"
},
"source": [
"### Parsing Pythonically"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2zlYLYMDGOHH"
},
"source": [
"I'd like to use comprehensions and generators to efficiently process the text into properly arranged chess move structures, which can be sent to the board model lazily and expressed in functional programming terms. To do this, I must think ahead to the next step in developing this working demonstration and note that a push action doesn't return transformed data as output but instead modifies the state of the board and therefore cannot be easily enclosed in the same execution context as the lazy function chain. We'll setup a versatile parsing expression which operates lazily aka just-in-time, but can easily be serialized or iterated over to feed other parts of the chessviz system."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oEzPi3uZO99v"
},
"source": [
"Let's manually copy and paste that move list snippet into a reference for Python:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Fh6JdyzzKMyJ"
},
"source": [
"move_list_text = \"\"\"\\\n",
"202109\tKalman\tDevvyn \n",
"1.\tNc3\tNf6\n",
"2.\tNf3\tNc6\n",
"3.\ta3\ta6\n",
"4.\th3\th5\n",
"5.\tg3\td5\n",
"6.\te3\tg6\n",
"7.\tb3\tb5\n",
"8.\td3\tQe6\n",
"9.\tBe2\tBb7\n",
"10.\tBb2\te6\n",
"11.\tRg1\tQd6\n",
"12.\tb4\ta5\n",
"13.\tg4\ta4\n",
"14.\tRg3\th4\n",
"15.\tQd2\"\"\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "3RbRT8JhPNUY"
},
"source": [
"Let's quickly see that as a list data sructure:"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hMFQXSnDKw5b",
"outputId": "7840e29f-d9a1-46ce-86b8-fd325878a16c"
},
"source": [
"move_list_text.splitlines()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['202109\\tKalman\\tDevvyn ',\n",
" '1.\\tNc3\\tNf6',\n",
" '2.\\tNf3\\tNc6',\n",
" '3.\\ta3\\ta6',\n",
" '4.\\th3\\th5',\n",
" '5.\\tg3\\td5',\n",
" '6.\\te3\\tg6',\n",
" '7.\\tb3\\tb5',\n",
" '8.\\td3\\tQe6',\n",
" '9.\\tBe2\\tBb7',\n",
" '10.\\tBb2\\te6',\n",
" '11.\\tRg1\\tQd6',\n",
" '12.\\tb4\\ta5',\n",
" '13.\\tg4\\ta4',\n",
" '14.\\tRg3\\th4',\n",
" '15.\\tQd2']"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XYTF0BkSPaIv"
},
"source": [
"And since I feel especially rusty today, I'm going quickly confirm that my simplistic parsing strategy of splitting each line by tabs works the way I remember:"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "-626u3HJRCqO",
"outputId": "2ced0e3a-2761-4577-c6bb-ee8f544f32b1"
},
"source": [
"move_list_text.splitlines()[0]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'202109\\tKalman\\tDevvyn '"
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VLeZJ3jcSd8q",
"outputId": "f4515772-dca3-4232-8e52-7f9086d682ed"
},
"source": [
"move_list_text.splitlines()[0].split(sep='\\t')"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['202109', 'Kalman', 'Devvyn ']"
]
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Zuqyx_a5TSho"
},
"source": [
"This stands as proof that it works step-by-step and serves as a reference for a fall-back point when I run into our next unexpected development event. Although I'm tempted to spend time refreshing my knowledge of regular expressions, our goal is an MVP-only race to the finish line for extendable and demonstratable core pieces. Let's continue with this quick-and-dirty hack job and be proud of our pace!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Nx3sfiJL746b"
},
"source": [
"HONESTY BIT: I messed around with some generator expressions and format strings for quite a while before getting to this point!"
]
},
{
"cell_type": "code",
"metadata": {
"id": "aOURlE4Y9Qzc",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f8dbed72-7db7-416b-871e-76f1eae0d750"
},
"source": [
"for line_number, line_text in enumerate(move_list_text.splitlines()):\n",
" print(line_text)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"202109\tKalman\tDevvyn \n",
"1.\tNc3\tNf6\n",
"2.\tNf3\tNc6\n",
"3.\ta3\ta6\n",
"4.\th3\th5\n",
"5.\tg3\td5\n",
"6.\te3\tg6\n",
"7.\tb3\tb5\n",
"8.\td3\tQe6\n",
"9.\tBe2\tBb7\n",
"10.\tBb2\te6\n",
"11.\tRg1\tQd6\n",
"12.\tb4\ta5\n",
"13.\tg4\ta4\n",
"14.\tRg3\th4\n",
"15.\tQd2\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Nzz0SW_fjQdQ"
},
"source": [
"Very simple, could be better. First, since the tabs are printing, we can't see that the columns aren't parsed. I can't help but feel like I could be using `pandas` to work more quickly! Still, that isn't necessary to meet MVP threshold, so let me acknowledge the urge to embrace the perfectionistic tendency heartily but instead choose to do the opposite. Moving on! Where were we? Oh yeah, columns and rows!"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4x1QDP93lbl8",
"outputId": "7b888631-7f70-4a07-8e76-0fe1f3f14ff3"
},
"source": [
"for line_text in move_list_text.splitlines():\n",
" for column_number, column_text in enumerate(line_text.split('\\t')):\n",
" print(column_number, column_text)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"0 202109\n",
"1 Kalman\n",
"2 Devvyn \n",
"0 1.\n",
"1 Nc3\n",
"2 Nf6\n",
"0 2.\n",
"1 Nf3\n",
"2 Nc6\n",
"0 3.\n",
"1 a3\n",
"2 a6\n",
"0 4.\n",
"1 h3\n",
"2 h5\n",
"0 5.\n",
"1 g3\n",
"2 d5\n",
"0 6.\n",
"1 e3\n",
"2 g6\n",
"0 7.\n",
"1 b3\n",
"2 b5\n",
"0 8.\n",
"1 d3\n",
"2 Qe6\n",
"0 9.\n",
"1 Be2\n",
"2 Bb7\n",
"0 10.\n",
"1 Bb2\n",
"2 e6\n",
"0 11.\n",
"1 Rg1\n",
"2 Qd6\n",
"0 12.\n",
"1 b4\n",
"2 a5\n",
"0 13.\n",
"1 g4\n",
"2 a4\n",
"0 14.\n",
"1 Rg3\n",
"2 h4\n",
"0 15.\n",
"1 Qd2\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pBIq_ZWZmvsw"
},
"source": [
"Alright, this code proves the point that simplistic parsing works well enough to break a rich text table into rows and columns if it's copied and pasted into pure text format for our code to digest.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "b5-mmnwPzo3o"
},
"source": [
"HONESTY BIT: I spent a bunch of time looking at recipes for the itertools library, but after a while, felt that the sprit of MVP was begging me to stop showing off, so on we go again!"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NaMSeVdKyeXG",
"outputId": "16795471-a29a-4a05-a5cc-1e26056841b4"
},
"source": [
"def slice_text(text):\n",
" for line in move_list_text.splitlines()[1:]:\n",
" for column_text in line.split('\\t')[1:]:\n",
" print(column_text)\n",
"slice_text(move_list_text)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Nc3\n",
"Nf6\n",
"Nf3\n",
"Nc6\n",
"a3\n",
"a6\n",
"h3\n",
"h5\n",
"g3\n",
"d5\n",
"e3\n",
"g6\n",
"b3\n",
"b5\n",
"d3\n",
"Qe6\n",
"Be2\n",
"Bb7\n",
"Bb2\n",
"e6\n",
"Rg1\n",
"Qd6\n",
"b4\n",
"a5\n",
"g4\n",
"a4\n",
"Rg3\n",
"h4\n",
"Qd2\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wps3MqWP0UO_"
},
"source": [
"This shows the moves coming out perfectly as text, the same way they would be fed to `board.push_san()` if we replace `print()`."
]
},
{
"cell_type": "code",
"metadata": {
"id": "Tq8oA7qKuiDx"
},
"source": [
"def board_after(moves_text):\n",
" board = chess.Board()\n",
" for line in move_list_text.splitlines()[1:]:\n",
" for column_text in line.split('\\t')[1:]:\n",
" board.push_san(column_text)\n",
" return board"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "TP2nHIsh0wpa"
},
"source": [
"I skipped ahead to try this on my own, because I was nervous it would make a fool of me. However, I'm showing you the result anyways, so I didn't need to hide my attempt. We're doing this together, so take a look with me!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nijxXUKQEDXe"
},
"source": [
"#### Unexpected Error"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 329
},
"id": "AlEeO5HOx7wG",
"outputId": "1a07d015-d268-40c4-a2a2-18950782d423"
},
"source": [
"board_after(move_list_text)"
],
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "ValueError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-21-c93baa415356>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mboard_after\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmove_list_text\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-19-cd2a6ef73e0d>\u001b[0m in \u001b[0;36mboard_after\u001b[0;34m(moves_text)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmove_list_text\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn_text\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\t'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mboard\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_san\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn_text\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mboard\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/chess/__init__.py\u001b[0m in \u001b[0;36mpush_san\u001b[0;34m(self, san)\u001b[0m\n\u001b[1;32m 2623\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mraises\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mneither\u001b[0m \u001b[0mlegal\u001b[0m \u001b[0mnor\u001b[0m \u001b[0ma\u001b[0m \u001b[0mnull\u001b[0m \u001b[0mmove\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2624\u001b[0m \"\"\"\n\u001b[0;32m-> 2625\u001b[0;31m \u001b[0mmove\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_san\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2626\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmove\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2627\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmove\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/chess/__init__.py\u001b[0m in \u001b[0;36mparse_san\u001b[0;34m(self, san)\u001b[0m\n\u001b[1;32m 2610\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2611\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmatched_move\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2612\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"illegal san: {} in {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2613\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2614\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmatched_move\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: illegal san: 'Qe6' in r1bqkb1r/2p1pp2/p1n2np1/1p1p3p/8/PPNPPNPP/2P2P2/R1BQKB1R b KQkq - 0 8"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5I4tARjw1c83"
},
"source": [
"Well, it appears that I recorded an invalid move! This is awesome because I didn't realize I'd done that, and it's conceivable that I may have played an invalid move on my opponent! Let's see the board before that move is reached. If we catch the `ValueError` and break out of all loops, we can show the board after all *valid* moves are played, and perhaps make a decision about the recorded moves and state of the game between the opponents."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 682
},
"id": "tSirVnK02lVB",
"outputId": "dbb988e1-dece-465d-c245-95c24a98d284"
},
"source": [
"def board_after(moves_text):\n",
" board = chess.Board()\n",
" break_now = False\n",
" for line in move_list_text.splitlines()[1:]:\n",
" for column_text in line.split('\\t')[1:]:\n",
" try:\n",
" print(board.push_san(column_text))\n",
" except ValueError:\n",
" break_now = True\n",
" break\n",
" if break_now == True:\n",
" break\n",
" return board\n",
"board_after(move_list_text)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"b1c3\n",
"g8f6\n",
"g1f3\n",
"b8c6\n",
"a2a3\n",
"a7a6\n",
"h2h3\n",
"h7h5\n",
"g2g3\n",
"d7d5\n",
"e2e3\n",
"g7g6\n",
"b2b3\n",
"b7b5\n",
"d2d3\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Board('r1bqkb1r/2p1pp2/p1n2np1/1p1p3p/8/PPNPPNPP/2P2P2/R1BQKB1R b KQkq - 0 8')"
],
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5.997,31 6,26 C 6,24 12,14 12,14 C 12,14 13.89,12.1 14,10.5 C 13.27,9.506 13.5,8.5 13.5,7.5 C 14.5,6.5 16.5,10 16.5,10 L 18.5,10 C 18.5,10 19.28,8.008 21,7 C 22,7 22,10 22,10\" style=\"fill:#ffffff; stroke:#000000;\" /><path d=\"M 9.5 25.5 A 0.5 0.5 0 1 1 8.5,25.5 A 0.5 0.5 0 1 1 9.5 25.5 z\" style=\"fill:#000000; stroke:#000000;\" /><path d=\"M 15 15.5 A 0.5 1.5 0 1 1 14,15.5 A 0.5 1.5 0 1 1 15 15.5 z\" style=\"fill:#000000; stroke:#000000;\" transform=\"matrix(0.866,0.5,-0.5,0.866,9.693,-5.173)\" /></g><g class=\"white bishop\" fill=\"none\" fill-rule=\"evenodd\" id=\"white-bishop\" stroke=\"#000\" stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"1.5\"><g fill=\"#fff\" stroke-linecap=\"butt\"><path d=\"M9 36c3.39-.97 10.11.43 13.5-2 3.39 2.43 10.11 1.03 13.5 2 0 0 1.65.54 3 2-.68.97-1.65.99-3 .5-3.39-.97-10.11.46-13.5-1-3.39 1.46-10.11.03-13.5 1-1.354.49-2.323.47-3-.5 1.354-1.94 3-2 3-2zM15 32c2.5 2.5 12.5 2.5 15 0 .5-1.5 0-2 0-2 0-2.5-2.5-4-2.5-4 5.5-1.5 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},
"metadata": {},
"execution_count": 22
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hvXR45HT7y-c"
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"source": [
"And here we can see white's last move, and the position black's queen is supposedly stationed. From d8, the black queen cannot possibly move to e6, so either an earlier move was omitted or black made an illegal move at this point.\n",
"\n",
"Since I was playing black in this game, I have to think back to playing at this point and I recall moving my queen twice to get it into a pinning position. There mest have been other errors, but without catching this upon the first error, I think this game is incomplete and best to be discarded."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SIXaXllsos2C"
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"source": [
"\n",
"#### MVP Caveat\n",
"It's worth noting right now that as the note-taker in this chess game, I chose to include a header row and three columns for turn number, white move, and black move. It's likely that other users of a future chessviz app will sometimes use completly different text formats."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mm9OOEcR9VDu"
},
"source": [
"## Conclusion"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fPAOG_RJ9dCj"
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"source": [
"We set out with a goal to explore the `python-chess` library, see how quickly we can hack out a demo of something new, and shape a minimal core of a useful software application. As an application development goal, I chose to write a core bit of code that can accept a copy-and-pasted snippet of recorded chess moves from a note on my phone and see what the board looks like. Although it seems that data was fundamentally flawed, the spirit of the goal was well tested, and we've identified some key areas for iteration as the product goes to release outside of this notebook:\n",
"\n",
"- chess rules violation handling\n",
"- flexible handling of unstructured text\n",
"- general exception handling\n",
"- useable output without Colab or Jupyter to display the board in a live environment"
]
}
]
}
@devvyn
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devvyn commented Nov 17, 2021 •
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If you can’t see the chess board on screen, I encourage you to open it in colab or download and view it in nbviewer or a live notebook environment of your choice to get the visuals and play with the data.

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