#Titan Robotics Team 2022: Super Script
#Written by Arthur Lu & Jacob Levine
#Notes:
#setup:

__version__ = "1.0.4.001"

__changelog__ = """changelog:
1.0.4.001:
    - grammar fixes
1.0.4.000:
    - actually pushes to firebase
1.0.3.001:
    - processes data more efficiently
1.0.3.000:
    - actually processes data
1.0.2.000:
    - added data reading from folder
    - nearly crashed computer reading from 20 GiB of data
1.0.1.000:
    - added data reading from file
    - added superstructure to code
1.0.0.000:
    - added import statements (revolutionary)
""" 

__author__ = (
    "Arthur Lu <arthurlu@ttic.edu>, "
    "Jacob Levine <jlevine@ttic.edu>,"
    )

import firebase_admin
from firebase_admin import credentials
from firebase_admin import firestore
import analysis
import titanlearn
import visualization
import os
import glob
import numpy as np

def titanservice():
    
    # Use a service account
    cred = credentials.Certificate('keys/firebasekey.json')
    firebase_admin.initialize_app(cred)

    db = firestore.client()

    #get all the data

    analysis.generate_data("data/bdata.csv", 100, 5, -10, 10)

    source_dir = 'data'
    file_list = glob.glob(source_dir + '/*.csv') #supposedly sorts by alphabetical order, skips reading teams.csv because of redundancy
    data = []
    files = [fn for fn in glob.glob('data/*.csv') 
             if not os.path.basename(fn).startswith('teams')]

    #for file_path in file_list:
    #	if not os.path.basename(file_list).startswith("teams")
    #		data.append(analysis.load_csv(file_path))

    for i in files:
            data.append(analysis.load_csv(i))

    stats = []
    measure_stats = []
    teams = analysis.load_csv("data/teams.csv")

    #assumes that team number is in the first column, and that the order of teams is the same across all files
    #unhelpful comment
    for measure in data: #unpacks 3d array into 2ds

            measure_stats = []

            for i in range(len(measure)): #unpacks into specific teams

                    ofbest_curve = [None]
                    r2best_curve = [None]

                    line = measure[i]

                    #print(line)

                    x = list(range(len(line)))
                    eqs, rmss, r2s, overfit = analysis.optimize_regression(x, line, 10, 1)

                    beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "min_overfit")

                    #print(eqs, rmss, r2s, overfit)
                    
                    ofbest_curve.append(beqs)
                    ofbest_curve.append(brmss)
                    ofbest_curve.append(br2s)
                    ofbest_curve.append(boverfit)
                    ofbest_curve.pop(0)

                    #print(ofbest_curve)

                    beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "max_r2s")

                    r2best_curve.append(beqs)
                    r2best_curve.append(brmss)
                    r2best_curve.append(br2s)
                    r2best_curve.append(boverfit)
                    r2best_curve.pop(0)

                    #print(r2best_curve)
                    
                    measure_stats.append(teams[i] + ["|"] +  list(analysis.basic_stats(line, 0, 0)) + ["|"] + list(analysis.histo_analysis(line, 1, -3, 3)) + ["|"] + ofbest_curve + ["|"] + r2best_curve)

            stats.append(list(measure_stats))
            
    json_out = {}
                    
    for i in range(len(stats)):
            json_out[files[i]]=str(stats[i])

    print(json_out)

    db.collection(u'stats').document(u'stats-noNN').set(json_out)