Neutrino/NeutrinoParseTesting.ipynb

{
    "cells": [
        {
            "cell_type": "code",
            "execution_count": 86,
            "source": [
                "file = open(\"testB.neu\")\r\n",
                "dirty_blob = file.read()\r\n",
                "\r\n",
                "# get rid of comments and leading/trailing whitespace\r\n",
                "lines = dirty_blob.split(\"\\n\")\r\n",
                "for i, line in enumerate(lines):\r\n",
                "    if line.strip()[:2] == \"//\":\r\n",
                "        del lines[i]\r\n",
                "\r\n",
                "for i, line in enumerate(lines):\r\n",
                "    lines[i] = line.strip()\r\n",
                "\r\n",
                "tidy_blob = \" \".join(lines)"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 87,
            "source": [
                "data = []\r\n",
                "\r\n",
                "# get blocks\r\n",
                "for line in lines:\r\n",
                "    object = {}\r\n",
                "    if \"=\" in line:\r\n",
                "        keytype = line.split(\" \")[0].split(\":\")\r\n",
                "        key = keytype[0]\r\n",
                "        object[\"key\"] = key\r\n",
                "        if len(keytype) > 1:\r\n",
                "            type = keytype[1]\r\n",
                "            object[\"type\"] = type\r\n",
                "        data.append(object)"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 88,
            "source": [
                "words = tidy_blob.split(\" \")"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 89,
            "source": [
                "def extract_objects(keywords):\r\n",
                "    blocks = []\r\n",
                "    depth = -1\r\n",
                "    i = -1\r\n",
                "    last = \"\"\r\n",
                "    for word in keywords:\r\n",
                "        #print(f\"Word: {word} | Depth: {depth} | i: {i}\")\r\n",
                "        if word == \"{\":\r\n",
                "            depth += 1\r\n",
                "            if depth == 1:\r\n",
                "                #print(\"New block of depth 1\")\r\n",
                "                blocks.append({\"key\": last, \"object\": []})\r\n",
                "                i += 1\r\n",
                "        elif word == \"}\":\r\n",
                "            depth -= 1\r\n",
                "        else:\r\n",
                "            last = word\r\n",
                "        if depth >= 1:\r\n",
                "            blocks[i][\"object\"].append(word)\r\n",
                "    return blocks\r\n",
                "\r\n",
                "data = extract_objects(words)\r\n",
                "temp = []\r\n",
                "for blob in data:\r\n",
                "    temp.append(extract_objects(blob[\"object\"]))\r\n",
                "\r\n",
                "    \r\n",
                "\r\n",
                "temp"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "[[{'key': 'aa', 'object': ['{', 'aaa']},\n",
                            "  {'key': 'ab', 'object': ['{', 'aba']},\n",
                            "  {'key': 'ac', 'object': ['{', 'abb']}],\n",
                            " [{'key': 'ba', 'object': ['{']},\n",
                            "  {'key': 'bb', 'object': ['{']},\n",
                            "  {'key': 'bc', 'object': ['{']}],\n",
                            " [{'key': 'ba', 'object': ['{']},\n",
                            "  {'key': 'bb', 'object': ['{']},\n",
                            "  {'key': 'bc', 'object': ['{']}]]"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 89
                }
            ],
            "metadata": {}
        }
    ],
    "metadata": {
        "orig_nbformat": 4,
        "language_info": {
            "name": "python",
            "version": "3.7.8",
            "mimetype": "text/x-python",
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
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            "pygments_lexer": "ipython3",
            "nbconvert_exporter": "python",
            "file_extension": ".py"
        },
        "kernelspec": {
            "name": "python3",
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        },
        "interpreter": {
            "hash": "57baa5815c940fdaff4d14510622de9616cae602444507ba5d0b6727c008cbd6"
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