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{
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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 Relational 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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}
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@ -1,135 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 86,
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"source": [
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"file = open(\"testB.neu\")\r\n",
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"dirty_blob = file.read()\r\n",
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"\r\n",
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"# get rid of comments and leading/trailing whitespace\r\n",
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"lines = dirty_blob.split(\"\\n\")\r\n",
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"for i, line in enumerate(lines):\r\n",
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" if line.strip()[:2] == \"//\":\r\n",
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" del lines[i]\r\n",
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"\r\n",
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"for i, line in enumerate(lines):\r\n",
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" lines[i] = line.strip()\r\n",
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"\r\n",
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"tidy_blob = \" \".join(lines)"
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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": 87,
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"source": [
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"data = []\r\n",
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"\r\n",
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"# get blocks\r\n",
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"for line in lines:\r\n",
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" object = {}\r\n",
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" if \"=\" in line:\r\n",
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" keytype = line.split(\" \")[0].split(\":\")\r\n",
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" key = keytype[0]\r\n",
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" object[\"key\"] = key\r\n",
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" if len(keytype) > 1:\r\n",
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" type = keytype[1]\r\n",
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" object[\"type\"] = type\r\n",
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" data.append(object)"
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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": 88,
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"source": [
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"words = tidy_blob.split(\" \")"
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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": 89,
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"source": [
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"def extract_objects(keywords):\r\n",
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" blocks = []\r\n",
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" depth = -1\r\n",
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" i = -1\r\n",
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" last = \"\"\r\n",
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" for word in keywords:\r\n",
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" #print(f\"Word: {word} | Depth: {depth} | i: {i}\")\r\n",
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" if word == \"{\":\r\n",
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" depth += 1\r\n",
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" if depth == 1:\r\n",
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" #print(\"New block of depth 1\")\r\n",
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" blocks.append({\"key\": last, \"object\": []})\r\n",
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" i += 1\r\n",
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" elif word == \"}\":\r\n",
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" depth -= 1\r\n",
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" else:\r\n",
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" last = word\r\n",
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" if depth >= 1:\r\n",
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" blocks[i][\"object\"].append(word)\r\n",
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" return blocks\r\n",
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"\r\n",
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"data = extract_objects(words)\r\n",
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"temp = []\r\n",
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"for blob in data:\r\n",
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" temp.append(extract_objects(blob[\"object\"]))\r\n",
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"\r\n",
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" \r\n",
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"\r\n",
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"temp"
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],
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"[[{'key': 'aa', 'object': ['{', 'aaa']},\n",
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" {'key': 'ab', 'object': ['{', 'aba']},\n",
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" {'key': 'ac', 'object': ['{', 'abb']}],\n",
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|
||||||
" [{'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
|
|
||||||
},
|
|
||||||
"pygments_lexer": "ipython3",
|
|
||||||
"nbconvert_exporter": "python",
|
|
||||||
"file_extension": ".py"
|
|
||||||
},
|
|
||||||
"kernelspec": {
|
|
||||||
"name": "python3",
|
|
||||||
"display_name": "Python 3.7.8 64-bit"
|
|
||||||
},
|
|
||||||
"interpreter": {
|
|
||||||
"hash": "57baa5815c940fdaff4d14510622de9616cae602444507ba5d0b6727c008cbd6"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 2
|
|
||||||
}
|
|
||||||
File diff suppressed because one or more lines are too long
81
example.neu
81
example.neu
@ -1,81 +0,0 @@
|
|||||||
// & declares a key pointing to object instantiated by that declaration (in this case, we're using integers in hex)
|
|
||||||
// keys are NOT indices - ultimately they should be ingested as keys to a dictionary containing each object in the Neutrino file
|
|
||||||
// * gets a reference to a key's object (Neutrino will try to preserve this reference in the target software whenever possible)
|
|
||||||
// # gets a copy from a cached object's key (Neutrino will simply replace this reference with a copy of its value - this is simply to consolidate repetitive data in-exchange)
|
|
||||||
|
|
||||||
// overall structure:
|
|
||||||
/*
|
|
||||||
meta = {},
|
|
||||||
graph = {
|
|
||||||
scene = {},
|
|
||||||
assets = {}
|
|
||||||
},
|
|
||||||
cache = {}
|
|
||||||
*/
|
|
||||||
|
|
||||||
meta = {
|
|
||||||
//
|
|
||||||
},
|
|
||||||
graph = {
|
|
||||||
// note - "scene" and "assets" are essentially namespaces
|
|
||||||
// the scene itself
|
|
||||||
scene = {
|
|
||||||
&0: object = {
|
|
||||||
name: string = "SM_LargeWindow_A",
|
|
||||||
mesh: mesh = *2,
|
|
||||||
transform: trans = #b
|
|
||||||
},
|
|
||||||
&1: object = {
|
|
||||||
name: string = "SM_LargeWindow_A2",
|
|
||||||
mesh: mesh = *2,
|
|
||||||
transform: trans = #b
|
|
||||||
}
|
|
||||||
},
|
|
||||||
// assets used by the scene
|
|
||||||
assets {
|
|
||||||
&2: mesh {
|
|
||||||
source: path = "/Assets/Props/LargeWindowA.obj",
|
|
||||||
materials: [mat] = [*4]
|
|
||||||
},
|
|
||||||
&4: mat {
|
|
||||||
name: string = "Simple Glass",
|
|
||||||
parent: shader = *7,
|
|
||||||
// "shader.props" is a subtype of the "shader" type, which is just a namespace that keeps it from being mixed up with other "props" subtypes (like "mesh.props", etc.)
|
|
||||||
parameters: shader.props = {
|
|
||||||
albedo: tex = *6,
|
|
||||||
roughness: float = 0.15,
|
|
||||||
normal: vec4 = #a
|
|
||||||
}
|
|
||||||
},
|
|
||||||
&5: mat {
|
|
||||||
name: string = "Blockout Grey",
|
|
||||||
parent: shader = *7,
|
|
||||||
parameters: shader.props = {
|
|
||||||
albedo: vec4 = (0.5, 0.5, 0.5, 1.0),
|
|
||||||
roughness: float = 0.85,
|
|
||||||
normal: vec4 = #a
|
|
||||||
}
|
|
||||||
},
|
|
||||||
&6: tex {
|
|
||||||
source: path = "/Assets/Textures/T_WindowGrime.png"
|
|
||||||
},
|
|
||||||
&7: shader {
|
|
||||||
source: path = "/Assets/Shaders/PBRBasic.wgsl"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
},
|
|
||||||
// anonymous objects shared by multiple other objects
|
|
||||||
cache {
|
|
||||||
// this vector is common as a position or rotation value
|
|
||||||
&8: vec3 = (0.0, 0.0, 0.0),
|
|
||||||
// this vector is common as a scale value
|
|
||||||
&9: vec3 = (1.0, 1.0, 1.0),
|
|
||||||
// this vector is common as a normal value
|
|
||||||
&a: vec4 = (0.5, 0.5, 1.0, 1.0),
|
|
||||||
// this transform is common because it's the default
|
|
||||||
&b: trans = {
|
|
||||||
position: vec3 = #8,
|
|
||||||
rotation: vec3 = #8,
|
|
||||||
scale: vec3 = #9
|
|
||||||
}
|
|
||||||
}
|
|
||||||
131
python/NeutrinoJSONTest.ipynb
Normal file
131
python/NeutrinoJSONTest.ipynb
Normal file
@ -0,0 +1,131 @@
|
|||||||
|
{
|
||||||
|
"cells": [
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 1,
|
||||||
|
"source": [
|
||||||
|
"# just returns a random string\r\n",
|
||||||
|
"import random\r\n",
|
||||||
|
"import string\r\n",
|
||||||
|
"from neutrino.encode import *\r\n",
|
||||||
|
"def random_string(length):\r\n",
|
||||||
|
" return ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(length))\r\n",
|
||||||
|
"\r\n",
|
||||||
|
"# create test scene\r\n",
|
||||||
|
"test_scene = new_scene(name = \"Neutrino Test Scene\", cache = True)\r\n",
|
||||||
|
"\r\n",
|
||||||
|
"# populate assets\r\n",
|
||||||
|
"asset_names = []\r\n",
|
||||||
|
"for i in range(10):\r\n",
|
||||||
|
" name = random_string(8)\r\n",
|
||||||
|
" add_asset(test_scene, name, \"Assets/TestAsset.obj\")\r\n",
|
||||||
|
" asset_names.append(name)\r\n",
|
||||||
|
"\r\n",
|
||||||
|
"# populate objects in scene\r\n",
|
||||||
|
"for i in range(50):\r\n",
|
||||||
|
" spawn_object(test_scene, random_string(8), random.choice(asset_names))\r\n",
|
||||||
|
"\r\n",
|
||||||
|
"save_scene(test_scene, False)"
|
||||||
|
],
|
||||||
|
"outputs": [],
|
||||||
|
"metadata": {}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 2,
|
||||||
|
"source": [
|
||||||
|
"import json\r\n",
|
||||||
|
"from neutrino.decode import *\r\n",
|
||||||
|
"\r\n",
|
||||||
|
"imported_data = json.load(open(\"NeutrinoTestScene.json\"))\r\n",
|
||||||
|
"\r\n",
|
||||||
|
"for key, value in imported_data[\"graph\"][\"scene\"].items():\r\n",
|
||||||
|
" value = (imported_data[\"cache\"][\"names\"][value[0][1:]], value[1])\r\n",
|
||||||
|
" print(value)"
|
||||||
|
],
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"output_type": "stream",
|
||||||
|
"name": "stdout",
|
||||||
|
"text": [
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'OBK1NYBK'], '@0x4': '*0x9', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'KXKKGWH1'], '@0x4': '*0x1', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'FP8K8N8K'], '@0x4': '*0x7', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'MOGF2L64'], '@0x4': '*0x1', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'Z8EFSXJH'], '@0x4': '*0x8', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '1ZMPRUS6'], '@0x4': '*0x5', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'QVQSXH1U'], '@0x4': '*0x5', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'Q0F9YKF3'], '@0x4': '*0x2', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'RS1HURTI'], '@0x4': '*0x3', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '9IT2UDE7'], '@0x4': '*0x2', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'JJZ9VA2P'], '@0x4': '*0x3', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'PE6Y5NBE'], '@0x4': '*0x5', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '41VJPUWO'], '@0x4': '*0x2', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '3D63B4QE'], '@0x4': '*0x4', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '5JZIUIZ9'], '@0x4': '*0x6', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'ZD6DD8E1'], '@0x4': '*0x1', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'MIRYB8QW'], '@0x4': '*0x3', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '9HEY01NR'], '@0x4': '*0xa', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '3EK2Y8LS'], '@0x4': '*0x4', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'KJYNHF9B'], '@0x4': '*0x8', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '1WUOY60X'], '@0x4': '*0x5', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'W2L3HTUI'], '@0x4': '*0x2', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'K4L6USDR'], '@0x4': '*0x6', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'ZF5RNV1N'], '@0x4': '*0x1', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '62KC64SW'], '@0x4': '*0x7', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '1QCUVQII'], '@0x4': '*0x8', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '0IVRI09E'], '@0x4': '*0x3', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'J8J492ET'], '@0x4': '*0x9', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'MJJTBO2L'], '@0x4': '*0xa', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '5QAA9XJZ'], '@0x4': '*0x6', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '21GNFNFG'], '@0x4': '*0x7', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '132YJS72'], '@0x4': '*0x5', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'E7GT5ZZ0'], '@0x4': '*0x1', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '26DIERE1'], '@0x4': '*0x2', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'KTSB2GMU'], '@0x4': '*0x4', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'ETRS6ZK2'], '@0x4': '*0x2', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '8X2ZYUGQ'], '@0x4': '*0x5', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'L09LNFXL'], '@0x4': '*0x4', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '4MHVZCEE'], '@0x4': '*0x7', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'YV5R9UAL'], '@0x4': '*0x6', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '0BRGWQ75'], '@0x4': '*0x6', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'W53N74LW'], '@0x4': '*0xa', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'S1PLWUWH'], '@0x4': '*0x1', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'I7GJKHL6'], '@0x4': '*0x8', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'IPZGY627'], '@0x4': '*0xa', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '3LAE7CYV'], '@0x4': '*0x8', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'SUPYOPZB'], '@0x4': '*0x7', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', '2TSTYW4P'], '@0x4': '*0xa', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'E47HYX2O'], '@0x4': '*0x1', '@0x5': '#0x4'}]\n",
|
||||||
|
"['@0xa', {'@0x1': ['@0x1', 'KE36JYS7'], '@0x4': '*0x2', '@0x5': '#0x4'}]\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {}
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"orig_nbformat": 4,
|
||||||
|
"language_info": {
|
||||||
|
"name": "python",
|
||||||
|
"version": "3.7.8",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"file_extension": ".py"
|
||||||
|
},
|
||||||
|
"kernelspec": {
|
||||||
|
"name": "python3",
|
||||||
|
"display_name": "Python 3.7.8 64-bit"
|
||||||
|
},
|
||||||
|
"interpreter": {
|
||||||
|
"hash": "57baa5815c940fdaff4d14510622de9616cae602444507ba5d0b6727c008cbd6"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 2
|
||||||
|
}
|
||||||
1
python/NeutrinoTestScene.json
Normal file
1
python/NeutrinoTestScene.json
Normal file
File diff suppressed because one or more lines are too long
BIN
python/__pycache__/neutrino.cpython-37.pyc
Normal file
BIN
python/__pycache__/neutrino.cpython-37.pyc
Normal file
Binary file not shown.
BIN
python/neutrino/__pycache__/decode.cpython-37.pyc
Normal file
BIN
python/neutrino/__pycache__/decode.cpython-37.pyc
Normal file
Binary file not shown.
BIN
python/neutrino/__pycache__/encode.cpython-37.pyc
Normal file
BIN
python/neutrino/__pycache__/encode.cpython-37.pyc
Normal file
Binary file not shown.
30
python/neutrino/decode.py
Normal file
30
python/neutrino/decode.py
Normal file
@ -0,0 +1,30 @@
|
|||||||
|
import json
|
||||||
|
|
||||||
|
def uncache_scene(in_data):
|
||||||
|
pure_data = {
|
||||||
|
"meta": in_data["meta"],
|
||||||
|
"graph": in_data["graph"]
|
||||||
|
}
|
||||||
|
raw_json = json.dumps(pure_data)
|
||||||
|
|
||||||
|
# cache objects
|
||||||
|
raw_cache = json.dumps(in_data["cache"])
|
||||||
|
for key, value in in_data["cache"]["objects"].items():
|
||||||
|
pointer = "#" + key
|
||||||
|
raw_cache = raw_cache.replace(f'"{pointer}"', json.dumps(value))
|
||||||
|
unpacked_object_cache = json.loads(raw_cache)
|
||||||
|
|
||||||
|
# objects
|
||||||
|
for key, value in unpacked_object_cache.items():
|
||||||
|
print(json.dumps(value))
|
||||||
|
pointer = "#" + key
|
||||||
|
raw_json = raw_json.replace(f'"{pointer}"', json.dumps(value))
|
||||||
|
|
||||||
|
# names
|
||||||
|
for key, value in in_data["cache"]["names"].items():
|
||||||
|
raw_json = raw_json.replace("@" + key, value)
|
||||||
|
|
||||||
|
|
||||||
|
out_data = json.loads(raw_json)
|
||||||
|
|
||||||
|
return out_data
|
||||||
189
python/neutrino/encode.py
Normal file
189
python/neutrino/encode.py
Normal file
@ -0,0 +1,189 @@
|
|||||||
|
import json
|
||||||
|
|
||||||
|
# create a new scene graph
|
||||||
|
def new_scene(name, cache = True):
|
||||||
|
# create empty neutrino data
|
||||||
|
data = {
|
||||||
|
"meta": {
|
||||||
|
"name": ("name", name),
|
||||||
|
"scale": ("float", 1.0),
|
||||||
|
"asset_path": ("path", "./")
|
||||||
|
},
|
||||||
|
"graph": {
|
||||||
|
"scene": {},
|
||||||
|
"assets": {}
|
||||||
|
},
|
||||||
|
"cache": {
|
||||||
|
"names": {},
|
||||||
|
"objects": {}
|
||||||
|
},
|
||||||
|
"internal": {
|
||||||
|
"cache": cache,
|
||||||
|
"max_object_key": {"index": 0},
|
||||||
|
"max_name_key": {"index": 0}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
# return that empty data
|
||||||
|
return data
|
||||||
|
|
||||||
|
# write the data to a JSON file
|
||||||
|
def save_scene(data, readable = False):
|
||||||
|
# cache the scene
|
||||||
|
if data["internal"]["cache"]:
|
||||||
|
cache_scene(data)
|
||||||
|
|
||||||
|
# create working copy of the scene data
|
||||||
|
clean_data = data.copy()
|
||||||
|
|
||||||
|
# get rid of internal data (not to be exported)
|
||||||
|
del clean_data["internal"]
|
||||||
|
|
||||||
|
filename = data["meta"]["name"][1].replace(" ", "") + ".json"
|
||||||
|
with open(filename, "w") as outfile:
|
||||||
|
if readable:
|
||||||
|
json.dump(clean_data, outfile, indent = 4)
|
||||||
|
else:
|
||||||
|
json.dump(clean_data, outfile)
|
||||||
|
|
||||||
|
# get a new indexed object key and track it
|
||||||
|
def new_key(index):
|
||||||
|
# get the indexed key
|
||||||
|
key = hex(index["index"] + 1)
|
||||||
|
|
||||||
|
# index the max key
|
||||||
|
index["index"] += 1
|
||||||
|
|
||||||
|
return key
|
||||||
|
|
||||||
|
# returns a cached name key from a string
|
||||||
|
def name_key(data, name):
|
||||||
|
if data["internal"]["cache"]:
|
||||||
|
name_pointer = ""
|
||||||
|
|
||||||
|
# retrieve the proper key if it exists
|
||||||
|
for key, value in data["cache"]["names"].items():
|
||||||
|
if value == name:
|
||||||
|
name_pointer = key
|
||||||
|
|
||||||
|
# if the name pointer is still empty, make a new key and add it to the cache
|
||||||
|
if name_pointer == "":
|
||||||
|
name_pointer = new_key(data["internal"]["max_name_key"])
|
||||||
|
data["cache"]["names"][name_pointer] = name
|
||||||
|
|
||||||
|
return "@" + name_pointer
|
||||||
|
else:
|
||||||
|
return name
|
||||||
|
|
||||||
|
# add an asset to the graph
|
||||||
|
def add_asset(data, name, path):
|
||||||
|
asset_data = {
|
||||||
|
name_key(data, "name"): (name_key(data, "name"), name),
|
||||||
|
name_key(data, "file"): (name_key(data, "path"), path)
|
||||||
|
}
|
||||||
|
|
||||||
|
# add the asset to the graph
|
||||||
|
data["graph"]["assets"][new_key(data["internal"]["max_object_key"])] = (name_key(data, "asset"), asset_data)
|
||||||
|
|
||||||
|
# add an object to the scene
|
||||||
|
def spawn_object(data, name, asset):
|
||||||
|
object_data = {
|
||||||
|
name_key(data, "name"): (name_key(data, "name"), name),
|
||||||
|
name_key(data, "asset"): "",
|
||||||
|
name_key(data, "transform"): (name_key(data, "transform"), {
|
||||||
|
name_key(data, "position"): (name_key(data, "vec3"), [0.0, 0.0, 0.0]),
|
||||||
|
name_key(data, "rotation"): (name_key(data, "vec3"), [0.0, 0.0, 0.0]),
|
||||||
|
name_key(data, "scale"): (name_key(data, "vec3"), [1.0, 1.0, 1.0])
|
||||||
|
})
|
||||||
|
}
|
||||||
|
|
||||||
|
# get an asset key by the provided name
|
||||||
|
for key, value in data["graph"]["assets"].items():
|
||||||
|
if value[1][name_key(data, "name")][1] == asset:
|
||||||
|
object_data[name_key(data, "asset")] = f"*{key}"
|
||||||
|
|
||||||
|
# add the object to the scene
|
||||||
|
data["graph"]["scene"][new_key(data["internal"]["max_object_key"])] = (name_key(data, "object"), object_data)
|
||||||
|
|
||||||
|
# recursively cache a single typeval tuple object
|
||||||
|
def cache_typeval(cache, typeval):
|
||||||
|
# ignore if not typeval
|
||||||
|
if type(typeval) == tuple:
|
||||||
|
for key, value in typeval[1].items():
|
||||||
|
# refuse to cache pointers (that's just... that would just be a nightmare)
|
||||||
|
if type(value) == str:
|
||||||
|
is_pointer = ("*" in value)
|
||||||
|
else:
|
||||||
|
is_pointer = False
|
||||||
|
if not is_pointer:
|
||||||
|
# cache member objects if it's a dictionary object
|
||||||
|
if type(value[1]) == dict:
|
||||||
|
cache_typeval(cache, value)
|
||||||
|
|
||||||
|
value_hash = hash(str(value))
|
||||||
|
|
||||||
|
# track in cache
|
||||||
|
if value_hash not in cache["objects"]:
|
||||||
|
cache_pointer = new_key(cache["key_index"])
|
||||||
|
cache["objects"][value_hash] = {"key": cache_pointer, "value": value, "count": 1}
|
||||||
|
else:
|
||||||
|
cache_pointer = cache["objects"][value_hash]["key"]
|
||||||
|
cache["objects"][value_hash]["count"] += 1
|
||||||
|
|
||||||
|
# replace real value with hash
|
||||||
|
typeval[1][key] = "#" + cache_pointer
|
||||||
|
|
||||||
|
# if there's only one instance of a certain value, convert it back to the original value and destroy the cached version
|
||||||
|
def uncache_typeval(cache, typeval):
|
||||||
|
for key, value in typeval[1].items():
|
||||||
|
# refuse to cache pointers (that's just... that would just be a nightmare)
|
||||||
|
if type(value) == str:
|
||||||
|
is_pointer = ("*" in value)
|
||||||
|
else:
|
||||||
|
is_pointer = False
|
||||||
|
if not is_pointer:
|
||||||
|
# cache member objects if it's a dictionary object
|
||||||
|
if type(value[1]) == dict:
|
||||||
|
uncache_typeval(cache, value)
|
||||||
|
|
||||||
|
value_hash = hash(str(value))
|
||||||
|
|
||||||
|
# check if it occurs only once
|
||||||
|
cache_key = value.replace("#", "")
|
||||||
|
if cache[cache_key]["count"] <= 1:
|
||||||
|
# replace the cache pointer in the scene data with its original value
|
||||||
|
typeval[1][key] = cache[cache_key]["value"]
|
||||||
|
|
||||||
|
# delete this object from the cache
|
||||||
|
del cache[cache_key]
|
||||||
|
|
||||||
|
# cache the scene
|
||||||
|
def cache_scene(data):
|
||||||
|
containers = [
|
||||||
|
data["graph"]["scene"],
|
||||||
|
data["graph"]["assets"]
|
||||||
|
]
|
||||||
|
|
||||||
|
# build a cache of value hashes and pointers
|
||||||
|
hash_cache = {"key_index": {"index": 0}, "objects": {}}
|
||||||
|
for objects in containers:
|
||||||
|
for key, value in objects.items():
|
||||||
|
cache_typeval(hash_cache, value)
|
||||||
|
|
||||||
|
# create a cache hashed with pointer keys instead of value hashes
|
||||||
|
key_cache = {}
|
||||||
|
for key, value in hash_cache["objects"].items():
|
||||||
|
key_cache[value["key"]] = {"value": value["value"], "count": value["count"]}
|
||||||
|
|
||||||
|
# prune the cache to only redirect repeat values
|
||||||
|
for objects in containers:
|
||||||
|
for key, value in objects.items():
|
||||||
|
uncache_typeval(key_cache, value)
|
||||||
|
|
||||||
|
# create a serialized cache usable by neutrino
|
||||||
|
serial_cache = {}
|
||||||
|
for key, value in key_cache.items():
|
||||||
|
serial_cache[key] = value["value"]
|
||||||
|
|
||||||
|
# add that cache to the neutrino scene data
|
||||||
|
data["cache"]["objects"] = serial_cache
|
||||||
Loading…
Reference in New Issue
Block a user