156 lines
5.3 KiB
Python
156 lines
5.3 KiB
Python
# Outcomes 2023
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# Tasks:
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#
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# - List all courses (in semester) in iLearn:
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# + SLOs associated with the course
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# + Whether they are current or inactive
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# + Whether they are attached to an assessment
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# + Whether and by how many students, they have been assessed
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#
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# - Fetch most current SLOs from Curricunet
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# + Assemble multiple versions of a (CQ) course and determine which semesters they apply to
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# + Whether they are present in the relevant classes in iLearn
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# + Insert SLO into course if not present
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# + Mark as inactive (change name) if necessary
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# - Issue:
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# + Course naming / sections joined...
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import concurrent.futures
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import pandas as pd
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from pipelines import fetch, url, header
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from courses import getCoursesInTerm
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import codecs, json
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from path_dict import PathDict
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NUM_THREADS = 20
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get_fresh = 0
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TERM = 180 # fa23
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sem_courses = getCoursesInTerm(TERM,get_fresh)
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def escape_commas(s):
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if ',' in s:
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return '"' + s.replace('"', '""') + '"'
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else:
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return s
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# shorter list for test?
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#sem_courses = sem_courses[:50]
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print("Got %i courses in current semester." % len(sem_courses))
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outputfile = codecs.open(f'cache/slo/outcomes_bycourse_{TERM}.output.txt','w','utf-8')
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outputfile.write( "coursename,assessed,courseid,outcome_id,points,title,displayname,description,guid\n")
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def course_slo_getter(q):
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(name,id) = q
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info = {'ilearnname':name,'ilearnid':id}
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print(" + Thread getting %s %s" % (str(name),str(id)))
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# Get GROUPS for a course
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u1 = url + "/api/v1/courses/%s/outcome_groups" % str(id)
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og_for_course = fetch(u1)
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if len(og_for_course):
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# There is a GROUP...
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for og in og_for_course:
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if "outcomes_url" in og:
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# There are OUTCOMES...
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outcomes = fetch(url + og["outcomes_url"])
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og['outcomes'] = outcomes
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og['full_outcomes'] = {}
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for oo in outcomes:
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print(" -> " + url + oo['outcome']['url'])
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this_outcome = fetch( url + oo['outcome']['url'] )
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og['full_outcomes'][this_outcome['id']] = this_outcome
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saveme = [name, this_outcome['assessed'], id, this_outcome['id'], this_outcome['points_possible'], this_outcome['title'], this_outcome['display_name'], this_outcome['description'], this_outcome['vendor_guid'] ]
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saveme2 = [escape_commas(str(x)) for x in saveme]
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outputfile.write(",".join(saveme2) + "\n")
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outputfile.flush()
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if type(og_for_course) == list:
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og_for_course.insert(0,info)
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else:
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og_for_course.update(info)
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print(" - Thread %s DONE" % str(id))
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return og_for_course
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raw_log = codecs.open('cache/outcome_raw_log.txt','w','utf-8')
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#raw_log.write( json.dumps(output,indent=2) )
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output = []
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with concurrent.futures.ThreadPoolExecutor(max_workers=NUM_THREADS) as pool:
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futures = []
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for C in sem_courses:
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print("Adding ", C['name'], C['id'], " to queue")
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futures.append( pool.submit(course_slo_getter, [C['name'], C['id']] ) )
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for future in concurrent.futures.as_completed(futures):
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output.append(future.result())
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print(future.result())
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raw_log.write( json.dumps(future.result(),indent=2) + "\n" )
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#print("-- Done")
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#print("results array has %i items" % len(results))
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#for r in concurrent.futures.as_completed(results):
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# output.append(r.result())
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def ilearn_shell_slo_to_csv(shell_slos):
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L = ['canvasid','name','crn','has_outcomes',]
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for i in range(1,11):
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L.append("o%i_id" % i)
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L.append("o%i_vendor_guid" % i)
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L.append("o%i_desc" % i)
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L.append("o%i_assd" % i)
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df = pd.DataFrame(columns=L)
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for S in shell_slos:
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short = S[0]
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this_crs = {'canvasid':short['ilearnid'], 'name':short['ilearnname'], 'has_outcomes':0, }
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if len(S)>1:
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full = S[1]
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this_crs['has_outcomes'] = 1
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i = 1
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for o in full['outcomes']:
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try:
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this_id = int(o['outcome']['id'])
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this_crs['o%i_id' % i] = o['outcome']['id']
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except Exception as e:
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this_crs['o%i_id' % i] = '!'
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try:
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this_crs['o%i_desc' % i] = full['full_outcomes'][this_id]['description']
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except Exception as e:
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this_crs['o%i_desc' % i] = '!'
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try:
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assessed = 0
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if full['full_outcomes'][this_id]['assessed'] == 'True':
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assessed = 1
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this_crs['o%i_assd' % i] = assessed
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except Exception as e:
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this_crs['o%i_assd' % i] = '!'
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try:
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this_crs['o%i_vendor_guid' % i] = full['full_outcomes'][this_id]['vendor_guid']
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except Exception as e:
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this_crs['o%i_vendor_guid' % i] = '!'
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i += 1
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df2 = pd.DataFrame(this_crs, columns = df.columns, index=[0])
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df = pd.concat( [df, df2], ignore_index = True )
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df.to_csv('cache/outcome.csv')
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print(df)
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ilearn_shell_slo_to_csv(output)
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