more complex
This commit is contained in:
Talhadeveloperr
@@ -1,6 +1,11 @@
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"""
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Generates small illustrative dummy CSV datasets under data/dummy/ so the
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timetable template generator has something to schedule and render.
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Generates a larger, deliberately harder dummy CSV dataset under data/dummy/
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so the timetable template generator has something non-trivial to schedule
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and render — more departments/batches/sections than the previous small
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dataset, tighter room and instructor contention (fewer rooms/instructors
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relative to demand), a second block, and mixed lecture durations (90 vs
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120 minutes) so weaknesses in the scheduler surface as WARNINGs/conflicts
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instead of everything trivially fitting.
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Run once:
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python data/dummy/generate_dummy_data.py
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@@ -23,13 +28,18 @@ def write_csv(relative_path, header, rows):
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def main():
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# ---- blocks & rooms (Building/Block A only) ----
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# ---- blocks: two blocks now, Block A (bigger) and Block B (smaller) ----
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write_csv(
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"blocks/blocks.csv",
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["block_id", "block_name"],
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[["A", "Block A"]],
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[
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["A", "Block A"],
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["B", "Block B"],
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],
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)
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# ---- rooms: deliberately tight relative to demand (see offerings
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# below) so room contention is real, spread across both blocks ----
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write_csv(
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"blocks/rooms.csv",
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["room_id", "room_name", "block_id", "room_type", "capacity"],
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@@ -40,21 +50,32 @@ def main():
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["A-201", "Room 201", "A", "classroom", 60],
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["A-L01", "Computer Lab 1", "A", "lab", 35],
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["A-L02", "Computer Lab 2", "A", "lab", 35],
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["B-101", "Room 101 (B)", "B", "classroom", 50],
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["B-102", "Room 102 (B)", "B", "classroom", 50],
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["B-L01", "Electronics Lab", "B", "lab", 30],
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],
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)
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# ---- departments / programs / batches ----
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# batch_code = session(2) + program_id(6) e.g. 25021519
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batches_25 = [
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["25021519", "CS", "BSCS", "25"],
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["25021520", "CS", "BSSE", "25"],
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["25031519", "EE", "BSEE", "25"],
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]
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batches_26 = [
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["26021519", "CS", "BSCS", "26"],
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["26021520", "CS", "BSSE", "26"],
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["26031519", "EE", "BSEE", "26"],
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]
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# ---- departments / programs / batches: 3 departments, 2 programs
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# each, 2 sessions (25/26) -> 12 batches total (was 6) ----
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department_programs = {
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"CS": ["BSCS", "BSSE"],
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"EE": ["BSEE", "BSCE"],
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"BBA": ["BBA", "BSAF"],
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}
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program_codes = {
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"BSCS": "021519", "BSSE": "021520",
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"BSEE": "031519", "BSCE": "031520",
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"BBA": "041519", "BSAF": "041520",
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}
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batches_25 = []
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batches_26 = []
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for department, programs in department_programs.items():
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for program in programs:
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code = program_codes[program]
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batches_25.append([f"25{code}", department, program, "25"])
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batches_26.append([f"26{code}", department, program, "26"])
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write_csv(
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"batch_codes/25/batches.csv",
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@@ -70,62 +91,86 @@ def main():
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all_batches = batches_25 + batches_26
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# ---- courses ----
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# One row per subject: lectures/labs per week are counted separately so
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# a single course can carry both, and each course names its preferred
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# lecture/lab room (the scheduler falls back to another same-type room
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# only if the preferred one is busy).
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# is_general courses are shared across every batch of the same session
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# (so they can be scheduled together on Tuesday first, with no clash).
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# course_id, course_name, credit_hours, lectures_per_week,
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# labs_per_week, is_general, lecture_room_id, lab_room_id,
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# lecture_duration_minutes
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#
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# Mixed lecture durations: most courses are 90-min x N/week, but a few
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# are the special "2 hours once a week" case (lecture_duration_minutes
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# 120, lectures_per_week 1). Labs vary 0/1/2 per week. Deliberately
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# more courses per department than the room/instructor pool can
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# comfortably absorb without contention.
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courses = [
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# course_id, course_name, credit_hours, lectures_per_week, labs_per_week, is_general, lecture_room_id, lab_room_id
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["CS101", "Programming Fundamentals", 4, 2, 1, "no", "A-101", "A-L01"],
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["CS201", "Data Structures", 4, 2, 1, "no", "A-102", "A-L01"],
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["EE101", "Basic Electronics", 4, 2, 1, "no", "A-201", "A-L02"],
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["GEN101", "Functional English", 3, 2, 0, "yes", "A-103", ""],
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["GEN102", "Islamic Studies", 2, 1, 0, "yes", "A-103", ""],
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# CS department courses
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["CS101", "Programming Fundamentals", 4, 2, 1, "no", "A-101", "A-L01", 90],
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["CS201", "Data Structures", 4, 2, 1, "no", "A-102", "A-L01", 90],
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["CS301", "Operating Systems", 3, 1, 1, "no", "A-101", "A-L02", 120],
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["CS302", "Database Systems", 4, 2, 2, "no", "A-102", "A-L02", 90],
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# EE department courses
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["EE101", "Basic Electronics", 4, 2, 1, "no", "A-201", "A-L02", 90],
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["EE201", "Digital Logic Design", 4, 2, 1, "no", "B-101", "B-L01", 90],
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["EE301", "Signals and Systems", 3, 1, 0, "no", "B-102", "", 120],
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# BBA department courses
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["BBA101", "Principles of Management", 3, 2, 0, "no", "B-101", "", 90],
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["BBA201", "Financial Accounting", 3, 2, 0, "no", "B-102", "", 90],
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["BBA301", "Marketing Management", 3, 1, 0, "no", "A-103", "", 120],
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# General (shared) courses
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["GEN101", "Functional English", 3, 2, 0, "yes", "A-103", "", 90],
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["GEN102", "Islamic Studies", 2, 1, 0, "yes", "A-103", "", 90],
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["GEN103", "Pakistan Studies", 2, 1, 0, "yes", "B-101", "", 120],
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]
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write_csv(
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"courses/courses.csv",
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[
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"course_id", "course_name", "credit_hours", "lectures_per_week",
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"labs_per_week", "is_general", "lecture_room_id", "lab_room_id",
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"lecture_duration_minutes",
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],
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courses,
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)
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# ---- course offerings: course + section (a section groups the batches
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# taking that section together) + who teaches it ----
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# A course can have many sections; here each CS/EE batch of a session is
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# its own section (Section A/B/...), while general courses have a single
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# section "ALL" grouping every batch of that session.
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offerings = []
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dept_course = {"CS": ["CS101", "CS201"], "EE": ["EE101"]}
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# ---- course offerings: course + section (batches in that section) +
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# instructor ----
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# Deliberately tight instructor pool reused across many sections/
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# courses (some instructors will end up wanted in overlapping slots),
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# forcing real contention in Phase 2/Phase 3.
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dept_courses = {
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"CS": ["CS101", "CS201", "CS301", "CS302"],
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"EE": ["EE101", "EE201", "EE301"],
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"BBA": ["BBA101", "BBA201", "BBA301"],
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}
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# Small pools relative to (department batches x courses) demand.
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dept_instructors = {
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"CS": ["1001", "1002", "1003", "1006"],
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"CS": ["1001", "1002", "1003"],
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"EE": ["1004", "1005"],
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"BBA": ["1007", "1008"],
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}
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offerings = []
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section_letters = ["A", "B", "C", "D"]
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dept_section_counter = {"CS": 0, "EE": 0}
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dept_section_counter = {"CS": 0, "EE": 0, "BBA": 0}
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for batch_code, department, program, session in all_batches:
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section_id = section_letters[dept_section_counter[department] % len(section_letters)]
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dept_section_counter[department] += 1
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for i, course_id in enumerate(dept_course[department]):
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instr_pool = dept_instructors[department]
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instr_pool = dept_instructors[department]
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for i, course_id in enumerate(dept_courses[department]):
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# Deliberately narrow instructor rotation (2-3 instructors
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# covering 3-4 courses per department) so the same instructor
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# teaches multiple courses/sections -> instructor contention.
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instr = instr_pool[i % len(instr_pool)]
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offerings.append([course_id, section_id, batch_code, instr])
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# general courses: one offering per (course, session), section "ALL"
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# grouping every batch code sharing that session, separated by "|"
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# grouping every batch code sharing that session
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for session, batch_group in (
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("25", [b[0] for b in batches_25]),
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("26", [b[0] for b in batches_26]),
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):
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offerings.append(["GEN101", "ALL", "|".join(batch_group), "1002"])
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offerings.append(["GEN102", "ALL", "|".join(batch_group), "1006"])
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offerings.append(["GEN103", "ALL", "|".join(batch_group), "1006"])
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write_csv(
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"courses/course_offering.csv",
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@@ -143,6 +188,8 @@ def main():
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"1004": "Dr. Farah Iqbal",
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"1005": "Mr. Usman Ali",
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"1006": "Ms. Hira Shah",
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"1007": "Dr. Nadia Farooq",
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"1008": "Mr. Kamran Sheikh",
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}
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instructors = []
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for course_id, section_id, batch_codes, instr_code in offerings:
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@@ -154,10 +201,10 @@ def main():
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instructors,
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)
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# ---- students (roll_no = batch_code-seq) ----
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# ---- students (roll_no = batch_code-seq): 18 per batch (was 5) ----
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students = []
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for batch_code, department, program, session in all_batches:
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for seq in range(1, 6): # 5 sample students per batch
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for seq in range(1, 19):
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roll_no = f"{batch_code}-{seq:03d}"
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students.append([roll_no, batch_code, f"Student {roll_no}"])
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