# Outcomes 2023

# Tasks:
#
# - List all courses (in semester) in iLearn:
#     + SLOs associated with the course
#     + Whether they are current or inactive
#     + Whether they are attached to an assessment
#     + Whether and by how many students, they have been assessed
#
# - Fetch most current SLOs from Curricunet
#     + Assemble multiple versions of a (CQ) course and determine which semesters they apply to
#     + Whether they are present in the relevant classes in iLearn
#     + Insert SLO into course if not present
#     + Mark as inactive (change name) if necessary


# - Issue:
#     + Course naming / sections joined...

import concurrent.futures
import pandas as pd
from pipelines import fetch, url, header
from courses import getCoursesInTerm
import codecs, json
from path_dict import PathDict


NUM_THREADS = 8
get_fresh = 0
sem_courses = getCoursesInTerm(176,get_fresh)

# shorter list for test?
#sem_courses = sem_courses[:50]

print("Got %i courses in current semester." % len(sem_courses))


def course_slo_getter(q):
    (name,id) = q
    info = {'ilearnname':name,'ilearnid':id}
    print("  + Thread getting %s %s" % (str(name),str(id)))
    u1 = url + "/api/v1/courses/%s/outcome_groups" % str(id)
    og_for_course = fetch(u1)
    if len(og_for_course):
        for og in og_for_course:
            if "outcomes_url" in og:
                outcomes = fetch(url + og["outcomes_url"])
                og['outcomes'] = outcomes 
                og['full_outcomes'] = {}
                for oo in outcomes:
                    print(" -> " + url + oo['outcome']['url'])
                    this_outcome = fetch( url + oo['outcome']['url'] )
                    og['full_outcomes'][this_outcome['id']] = this_outcome
    og_for_course.insert(0,info)
    print("  - Thread %s DONE" % str(id))
    return og_for_course


output = []
with concurrent.futures.ThreadPoolExecutor(max_workers=NUM_THREADS) as pool:
    results = []
    for C in sem_courses:
        print("Adding ", C['name'], C['id'], " to queue")
        results.append( pool.submit(course_slo_getter, [C['name'], C['id']] ) )
        
    print("--  Done")
    print("results array has %i items" % len(results))
    
    for r in concurrent.futures.as_completed(results):
        output.append(r.result())

raw_log = codecs.open('cache/outcome_raw_log.txt','w','utf-8')
raw_log.write( json.dumps(output,indent=2) )


def ilearn_shell_slo_to_csv(shell_slos):
    L = ['canvasid','name','crn','has_outcomes',]
    for i in range(1,11):
        L.append("o%i_id" % i)
        L.append("o%i_vendor_guid" % i)
        L.append("o%i_desc" % i)
        L.append("o%i_assd" % i)
    df = pd.DataFrame(columns=L)
    for S in shell_slos:
        short = S[0]
        this_crs = {'canvasid':short['ilearnid'], 'name':short['ilearnname'], 'has_outcomes':0, }
        if len(S)>1:
            full = S[1]
            this_crs['has_outcomes'] = 1
            
            i = 1
            
            for o in full['outcomes']:
                try:
                    this_id = int(o['outcome']['id'])
                    this_crs['o%i_id' % i] = o['outcome']['id']
                except Exception as e:
                    this_crs['o%i_id' % i] = '!'
                try:
                    this_crs['o%i_desc' % i] = full['full_outcomes'][this_id]['description']
                except Exception as e:
                    this_crs['o%i_desc' % i] = '!'
                try:
                    assessed = 0
                    if full['full_outcomes'][this_id]['assessed'] == 'True':
                        assessed = 1
                    this_crs['o%i_assd' % i] = assessed
                except Exception as e:
                    this_crs['o%i_assd' % i] = '!'
                try:
                    this_crs['o%i_vendor_guid' % i] = full['full_outcomes'][this_id]['vendor_guid']
                except Exception as e:
                    this_crs['o%i_vendor_guid' % i] = '!'
                
                i += 1
                
        df2 = pd.DataFrame(this_crs, columns = df.columns, index=[0])
        df = pd.concat( [df, df2], ignore_index = True )
        
    df.to_csv('cache/outcome.csv')
    print(df)
    
        
        
        
ilearn_shell_slo_to_csv(output)