245 lines
12 KiB
Plaintext
245 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 217,
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"source": [
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"import json\r\n",
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"\r\n",
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"# create a new scene graph\r\n",
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"def new_scene(name):\r\n",
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" # create empty neutrino data\r\n",
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" data = {\r\n",
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" \"meta\": {\r\n",
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" \"name\": (\"name\", name),\r\n",
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" \"scale\": (\"float\", 1.0),\r\n",
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" \"asset_path\": (\"path\", \"./\"),\r\n",
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" },\r\n",
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" \"graph\": {\r\n",
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" \"scene\": {},\r\n",
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" \"assets\": {}\r\n",
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" },\r\n",
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" \"internal\": {\r\n",
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" \"max_object_key\": {\"index\": 0},\r\n",
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" \"max_cache_key\": {\"index\": 0}\r\n",
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" }\r\n",
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" }\r\n",
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"\r\n",
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" # return that empty data\r\n",
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" return data\r\n",
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"\r\n",
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"# write the data to a JSON file\r\n",
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"def save_scene(data, readable):\r\n",
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" # create working copy of the scene data\r\n",
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" clean_data = data.copy()\r\n",
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"\r\n",
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" # get rid of internal data (not to be exported)\r\n",
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" del clean_data[\"internal\"]\r\n",
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" \r\n",
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" filename = data[\"meta\"][\"name\"][1].replace(\" \", \"\") + \".json\"\r\n",
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" with open(filename, \"w\") as outfile:\r\n",
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" if readable:\r\n",
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" json.dump(clean_data, outfile, indent = 4)\r\n",
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" else:\r\n",
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" json.dump(clean_data, outfile)\r\n",
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"\r\n",
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"# get a new indexed object key and track it\r\n",
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"def new_key(index):\r\n",
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" # get the indexed key\r\n",
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" key = hex(index[\"index\"] + 1)\r\n",
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"\r\n",
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" # index the max key\r\n",
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" index[\"index\"] += 1\r\n",
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"\r\n",
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" return key\r\n",
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"\r\n",
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"# add an asset to the graph\r\n",
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"def add_asset(data, name, path):\r\n",
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" asset_data = {\r\n",
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" \"name\": (\"name\", name),\r\n",
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" \"file\": (\"path\", path)\r\n",
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" }\r\n",
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" \r\n",
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" # add the asset to the graph\r\n",
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" data[\"graph\"][\"assets\"][new_key(data[\"internal\"][\"max_object_key\"])] = (\"asset\", asset_data)\r\n",
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"\r\n",
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"# add an object to the scene\r\n",
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"def spawn_object(data, name, asset):\r\n",
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" object_data = {\r\n",
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" \"name\": (\"name\", name),\r\n",
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" \"asset\": \"\",\r\n",
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" \"trans\": (\"trans\", {\r\n",
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" \"position\": (\"vec3\", [0.0, 0.0, 0.0]),\r\n",
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" \"rotation\": (\"vec3\", [0.0, 0.0, 0.0]),\r\n",
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" \"scale\": (\"vec3\", [1.0, 1.0, 1.0])\r\n",
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" })\r\n",
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" }\r\n",
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"\r\n",
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" # get an asset key by the provided name\r\n",
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" for key, value in data[\"graph\"][\"assets\"].items():\r\n",
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" if value[1][\"name\"][1] == asset:\r\n",
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" object_data[\"asset\"] = f\"*{key}\"\r\n",
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"\r\n",
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" # add the object to the scene\r\n",
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" data[\"graph\"][\"scene\"][new_key(data[\"internal\"][\"max_object_key\"])] = (\"object\", object_data)"
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Implement SPORC for storage/memory optimization\r\n",
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"(Single-Pointer Objective Cache)"
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 218,
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"source": [
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"# recursively cache a single typeval tuple object\r\n",
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"def cache_typeval(cache, typeval):\r\n",
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" # ignore if not typeval\r\n",
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" if type(typeval) == tuple:\r\n",
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" for key, value in typeval[1].items():\r\n",
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" # refuse to cache pointers (that's just... that would just be a nightmare)\r\n",
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" if type(value) == str:\r\n",
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" is_pointer = (\"*\" in value)\r\n",
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" else:\r\n",
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" is_pointer = False\r\n",
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" if not is_pointer:\r\n",
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" # cache member objects if it's a dictionary object\r\n",
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" if type(value[1]) == dict:\r\n",
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" cache_typeval(cache, value)\r\n",
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"\r\n",
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" value_hash = hash(str(value))\r\n",
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"\r\n",
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" # track in cache\r\n",
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" if value_hash not in cache[\"objects\"]:\r\n",
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" cache_pointer = new_key(cache[\"key_index\"])\r\n",
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" cache[\"objects\"][value_hash] = {\"key\": cache_pointer, \"value\": value, \"count\": 1}\r\n",
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" else:\r\n",
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" cache_pointer = cache[\"objects\"][value_hash][\"key\"]\r\n",
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" cache[\"objects\"][value_hash][\"count\"] += 1\r\n",
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"\r\n",
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" # replace real value with hash\r\n",
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" typeval[1][key] = \"#\" + cache_pointer\r\n",
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"\r\n",
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"# if there's only one instance of a certain value, convert it back to the original value and destroy the cached version\r\n",
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"def uncache_typeval(cache, typeval):\r\n",
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" for key, value in typeval[1].items():\r\n",
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" # refuse to cache pointers (that's just... that would just be a nightmare)\r\n",
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" if type(value) == str:\r\n",
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" is_pointer = (\"*\" in value)\r\n",
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" else:\r\n",
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" is_pointer = False\r\n",
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" if not is_pointer:\r\n",
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" # cache member objects if it's a dictionary object\r\n",
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" if type(value[1]) == dict:\r\n",
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" uncache_typeval(cache, value)\r\n",
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"\r\n",
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" value_hash = hash(str(value))\r\n",
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"\r\n",
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" # check if it occurs only once\r\n",
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" cache_key = value.replace(\"#\", \"\")\r\n",
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" if cache[cache_key][\"count\"] <= 1:\r\n",
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" # replace the cache pointer in the scene data with its original value\r\n",
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" typeval[1][key] = cache[cache_key][\"value\"]\r\n",
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"\r\n",
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" # delete this object from the cache\r\n",
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" del cache[cache_key]\r\n",
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"\r\n",
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"# cache the scene\r\n",
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"def cache_scene(data):\r\n",
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" # add the cache object to the scene data\r\n",
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" data[\"cache\"] = {}\r\n",
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"\r\n",
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" containers = [\r\n",
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" data[\"graph\"][\"scene\"],\r\n",
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" data[\"graph\"][\"assets\"]\r\n",
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" ]\r\n",
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"\r\n",
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" # build a cache of value hashes and pointers\r\n",
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" hash_cache = {\"key_index\": {\"index\": 0}, \"objects\": {}}\r\n",
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" for objects in containers:\r\n",
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" for key, value in objects.items():\r\n",
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" cache_typeval(hash_cache, value)\r\n",
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"\r\n",
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" # create a cache hashed with pointer keys instead of value hashes\r\n",
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" key_cache = {}\r\n",
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" for key, value in hash_cache[\"objects\"].items():\r\n",
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" key_cache[value[\"key\"]] = {\"value\": value[\"value\"], \"count\": value[\"count\"]}\r\n",
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"\r\n",
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" # prune the cache to only redirect repeat values\r\n",
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" for objects in containers:\r\n",
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" for key, value in objects.items():\r\n",
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" uncache_typeval(key_cache, value)\r\n",
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"\r\n",
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" # create a serialized cache usable by neutrino\r\n",
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" serial_cache = {}\r\n",
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" for key, value in key_cache.items():\r\n",
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" serial_cache[key] = value[\"value\"]\r\n",
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"\r\n",
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" # add that cache to the neutrino scene data\r\n",
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" data[\"cache\"] = serial_cache"
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 219,
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"source": [
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"# just returns a random string\r\n",
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"import random\r\n",
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"import string\r\n",
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"def random_string(length):\r\n",
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" return ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(length))\r\n",
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"\r\n",
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"# create test scene\r\n",
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"test_scene = new_scene(\"Neutrino Test Scene\")\r\n",
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"\r\n",
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"# populate assets\r\n",
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"asset_names = []\r\n",
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"for i in range(10):\r\n",
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" name = random_string(8)\r\n",
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" add_asset(test_scene, name, \"Assets/TestAsset.obj\")\r\n",
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" asset_names.append(name)\r\n",
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"\r\n",
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"# populate objects in scene\r\n",
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"for i in range(50):\r\n",
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" spawn_object(test_scene, random_string(8), random.choice(asset_names))\r\n",
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"\r\n",
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"cache_scene(test_scene)\r\n",
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"save_scene(test_scene, False)"
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],
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"outputs": [],
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"metadata": {}
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}
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],
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"metadata": {
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"orig_nbformat": 4,
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"language_info": {
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"name": "python",
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"version": "3.7.8",
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"mimetype": "text/x-python",
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"pygments_lexer": "ipython3",
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"nbconvert_exporter": "python",
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"file_extension": ".py"
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3.7.8 64-bit"
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},
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"interpreter": {
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"hash": "57baa5815c940fdaff4d14510622de9616cae602444507ba5d0b6727c008cbd6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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} |