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{
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"with open(\"data/nebuloss.json\", \"r\") as f:\n",
" data = json.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def split_dict(d, n):\n",
" \"\"\"\n",
" Splits a dictionary into n dictionaries with almost equal number of items.\n",
"\n",
" Parameters:\n",
" - d (dict): The input dictionary.\n",
" - n (int): The number of dictionaries to split into.\n",
"\n",
" Returns:\n",
" - list of dict: A list of n dictionaries.\n",
" \"\"\"\n",
" items = list(d.items())\n",
" length = len(items)\n",
" \n",
" # Calculate the size of each chunk\n",
" chunk_size = length // n\n",
" remainder = length % n\n",
"\n",
" # Split the items into chunks\n",
" chunks = []\n",
" start = 0\n",
"\n",
" for i in range(n):\n",
" if remainder:\n",
" end = start + chunk_size + 1\n",
" remainder -= 1\n",
" else:\n",
" end = start + chunk_size\n",
" chunks.append(dict(items[start:end]))\n",
" start = end\n",
"\n",
" return chunks\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"chunk_1, chunk_2, chunk_3, chunk_4 = split_dict(data, n=4)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"with open(\"data/nebuloss_1.json\", \"w\") as f:\n",
" json.dump(chunk_1, f, indent=4)\n",
"with open(\"data/nebuloss_2.json\", \"w\") as f:\n",
" json.dump(chunk_2, f, indent=4)\n",
"with open(\"data/nebuloss_3.json\", \"w\") as f:\n",
" json.dump(chunk_3, f, indent=4)\n",
"with open(\"data/nebuloss_4.json\", \"w\") as f:\n",
" json.dump(chunk_4, f, indent=4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "hackenv",
"language": "python",
"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": "3.10.12"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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