mirror of
https://github.com/titanscouting/tra-analysis.git
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133 lines
3.3 KiB
Plaintext
133 lines
3.3 KiB
Plaintext
{
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import firebase_admin\n",
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"from firebase_admin import credentials\n",
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"from firebase_admin import firestore\n",
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"import csv\n",
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"import numpy as np\n",
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"# Use a service account\n",
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"cred = credentials.Certificate(r'../keys/fsk.json')\n",
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"#add your own key as this is public. email me for details\n",
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"firebase_admin.initialize_app(cred)\n",
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"\n",
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"db = firestore.client()"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"teams=db.collection('data').document('team-2022').collection(\"Midwest 2019\").get()\n",
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"full=[]\n",
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"tms=[]\n",
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"for team in teams:\n",
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" data=[]\n",
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" tms.append(team.id)\n",
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" reports=db.collection('data').document('team-2022').collection(\"Midwest 2019\").document(team.id).collection(\"matches\").get()\n",
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" for report in reports:\n",
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" data.append(db.collection('data').document('team-2022').collection(\"Midwest 2019\").document(team.id).collection(\"matches\").document(report.id).get().to_dict())\n",
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" full.append(data)"
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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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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"def expcsv(loc,data):\n",
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" with open(loc+'.csv', 'w', newline='', encoding='utf-8') as csvfile:\n",
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" w = csv.writer(csvfile, delimiter=',', quotechar=\"\\\"\", quoting=csv.QUOTE_MINIMAL)\n",
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" for i in data:\n",
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" w.writerow(i)"
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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": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"def keymatch(ld):\n",
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" keys=set([])\n",
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" for i in ld:\n",
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" for j in i.keys():\n",
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" keys.add(j)\n",
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" kl=list(keys)\n",
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" data=[]\n",
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" for i in kl:\n",
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" data.append([i])\n",
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" for i in kl:\n",
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" for j in ld:\n",
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" try:\n",
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" (data[kl.index(i)]).append(j[i])\n",
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" except:\n",
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" (data[kl.index(i)]).append(\"\")\n",
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" return data\n",
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"wn=[]\n",
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"for i in full:\n",
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" wn.append(np.transpose(np.array(keymatch(i))).tolist())\n",
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"for i in range(len(wn)):\n",
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" expcsv(tms[i],wn[i])"
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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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"metadata": {},
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"outputs": [],
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"source": []
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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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"metadata": {},
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"outputs": [],
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"source": []
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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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"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": "Python 3",
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"language": "python",
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"name": "python3"
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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": "3.6.5"
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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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