201 lines
5.2 KiB
Plaintext
201 lines
5.2 KiB
Plaintext
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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": 1,
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"id": "e02ccafe-e04d-4312-acba-e41cf7b1c021",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/smgr/.pyenv/versions/co4/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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}
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],
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"source": [
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"from co3 import Mapper\n",
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"\n",
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"import vegetables"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c0914069-7f3c-4213-8d34-f7566033e054",
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"metadata": {},
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"source": [
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"## Development notes\n",
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"- No registry actually needs to take place if there's a default type2component map or one supplied on creation. Can just collect right out of the gate\n",
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"- Need connective function (type to collation) and attribute map. Do we need to this with a subclass? If a func is passed in on init, I can type it appropriately I guess `Callable[[type[CO3],str,str|None],dict]`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "7d80f7b9-7458-4ad4-8c1a-3ea56e796b4e",
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"metadata": {},
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"outputs": [],
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"source": [
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"vegetable_mapper = Mapper(\n",
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" vegetables.Vegetable,\n",
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" vegetables.vegetable_schema\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d24d31b4-c4a6-4a1e-8bea-c44378aadfdd",
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"metadata": {},
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"outputs": [],
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"source": [
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"# not valid; tables need to be wrapped in CO3 Components\n",
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"vegetable_mapper.attach(\n",
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" vegetables.Vegetable,\n",
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" vegetables.vegetable_table,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "f9408562-bf50-4522-909c-318557f85948",
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"metadata": {},
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"outputs": [],
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"source": [
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"# manually attach component\n",
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"vegetable_mapper.attach(\n",
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" vegetables.Tomato,\n",
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" vegetables.vegetable_schema.get_component('tomato'),\n",
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" coll_groups={\n",
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" 'aging': vegetables.vegetable_schema.get_component('tomato_aging_states'),\n",
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" 'cooking': vegetables.vegetable_schema.get_component('tomato_cooking_states'),\n",
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" },\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "05fdd404-87ee-4187-832f-2305272758ae",
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"metadata": {},
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"outputs": [],
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"source": [
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"# attach by name in schema\n",
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"vegetable_mapper.attach(\n",
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" vegetables.Tomato,\n",
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" 'tomato',\n",
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" coll_groups={\n",
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" 'aging': 'tomato_aging_states',\n",
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" 'cooking': 'tomato_cooking_states',\n",
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" },\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e9b6af49-a69d-41cc-beae-1b6f171cd2f5",
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"metadata": {},
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"outputs": [],
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"source": [
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"# attach entire type hierarchy w/ type->name map\n",
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"vegetable_mapper.attach_hierarchy(\n",
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"# this might make more sense during init\n",
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" lambda x:x.__name__.lower())\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "2e4336ab-5b5f-484d-815d-164d4b6f40a0",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'co3_root': vegetables.Vegetable,\n",
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" 'schema': <co3.schemas.SQLSchema at 0x74ac03f5c8c0>,\n",
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" 'collector': <co3.collector.Collector at 0x74ac0357ae70>,\n",
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" 'composer': <co3.composer.Composer at 0x74ac0357a4b0>,\n",
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" 'attribute_comps': {vegetables.Tomato: <co3.components.SQLTable at 0x74ac09d4a720>},\n",
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" 'collation_groups': defaultdict(dict,\n",
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" {vegetables.Tomato: {'aging': <co3.components.SQLTable at 0x74ac03f5cad0>,\n",
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" 'cooking': <co3.components.SQLTable at 0x74ac03f5cb00>}})}"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"vars(vegetable_mapper)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "c16786d4-0b71-42d9-97f7-7893c542104e",
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"metadata": {},
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"outputs": [],
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"source": [
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"tomato = vegetables.Tomato('t1', 5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "884d6753-c763-4e71-824a-711436e203e1",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<vegetables.Tomato at 0x74ac082bacc0>"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tomato"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "137d0bf1-940d-448c-91e9-01e7fc4f31b4",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "co3",
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"language": "python",
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"name": "co3"
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},
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"language_info": {
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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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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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