├── CourseScheduling ├── Course.py ├── Graph.py ├── Schedule.py ├── Scheduling.py ├── __init__.py └── priodict.py ├── DataHelper ├── WebSoc.py ├── __init__.py └── loadData.py ├── LICENSE ├── README.md ├── example.py ├── example2.py ├── info ├── avoid.txt ├── fullcourses.txt ├── fullcourses_new.txt ├── specializations.txt ├── taken.txt ├── taken2.txt └── widthFunc.txt ├── reports ├── report-final.pdf └── research-initial-plan.pdf └── test ├── avoid.txt ├── courseTest.py ├── taken.txt ├── test_spec.txt ├── test_widthFunc.txt └── testcourses.txt /CourseScheduling/Course.py: -------------------------------------------------------------------------------- 1 | class Course: 2 | # let self to be course v 3 | 4 | def __init__(self, name: str, units: int, 5 | quarter_codes: set, prereq: list, is_upper_only=False): 6 | self.name = name 7 | self.units = units # Total units v requires. 8 | self.quarterCodes = quarter_codes # In what quarters the department offers this course 9 | self.isUpperOnly = is_upper_only # true if it is an upper only course 10 | self.prereq = prereq # in conjunctive normal form, AND of ORs 11 | self.prereqBool = [None] * len(prereq) # bool info for satisfied prereqs 12 | self.successors = set() # a set of (successor, successor's prereqBool index) 13 | self.label = None # label of a course 14 | self.dependentIndex = -1 # The largest layer index of v's dependent schedule.default use -1 to note None 15 | self.requirements = set() # A set of requirements that v can satisfy. 16 | 17 | @property 18 | def courseValue(self): 19 | """ 20 | calculate the value of v based on the number of requirements v can satisfy 21 | :return: the value of a course 22 | """ 23 | return -len(self.requirements) 24 | 25 | def __str__(self): 26 | return " label: {label}\n units: {units}\n quarterCodes: {qc}\n " \ 27 | "isUpperOnly: {iuo}\n prereq: {prereq}\n prereqBool: {pb}\n" \ 28 | " successors: {successors}\n dependentIndex: {di}\n " \ 29 | "requirements: {req}\n".format(label=self.label, units=self.units, 30 | qc=self.quarterCodes, iuo=self.isUpperOnly, 31 | prereq=self.prereq, pb=self.prereqBool, 32 | successors=self.successors, 33 | di=self.dependentIndex, 34 | req=self.requirements) 35 | 36 | def prereq_list(self): 37 | """ 38 | :return: a list of v's prerequisites. (without the AND/OR form) 39 | """ 40 | return [c for OR in self.prereq for c in OR] 41 | 42 | def prereq_is_satisfied(self): 43 | """ 44 | :return: true if all prereq of v are satisfied. 45 | """ 46 | return all(self.prereqBool) 47 | 48 | def unsatisfied_prereq(self): 49 | """ 50 | :return: a set of (still require) courses in v's prereq 51 | """ 52 | result = set() 53 | for index, OR in enumerate(self.prereq): 54 | if not self.prereqBool[index]: 55 | result = result.union(OR) 56 | return result 57 | 58 | def has_dependent(self, L_i): 59 | """ 60 | check if layer with index L_i is a layer lower than v's dependent index 61 | if true, L_i is not a valid layer for course v. 62 | 63 | :param L_i: layer index 64 | :return: true if the layer is lower than v's dependent index 65 | """ 66 | return self.dependentIndex==None or self.dependentIndex >= L_i 67 | 68 | def tag_prereq(self, Bi, cid): 69 | """ 70 | tag that course with id 'cid' satisfy v's prereq OR set with index Bi 71 | 72 | :param Bi: B^i, the index of the OR set in v.prereq 73 | :param cid: the id of a course (the key for course in graph) 74 | """ 75 | if Bi >= len(self.prereq) or cid not in self.prereq[Bi]: 76 | raise Exception( 77 | "Course {cid} not exists in OR set with index {Bi}".format(cid=cid, Bi=Bi)) 78 | self.prereqBool[Bi] = cid 79 | 80 | -------------------------------------------------------------------------------- /CourseScheduling/Graph.py: -------------------------------------------------------------------------------- 1 | """ 2 | The course graph for scheduling. It uses a basic adjacency structure for graph 3 | """ 4 | from collections import deque 5 | 6 | __author__ = "Jenny Zeng" 7 | __email__ = "jennyzengzzh@gmail.com" 8 | 9 | class CourseGraph: 10 | def __init__(self, G: dict, r_detail: dict,R:dict, avoid=None, taken=None): 11 | """ 12 | :param G: a dict object representing the graph for courses 13 | :param r_detail: a **detail** requirement table. It is required. 14 | format: {r_name: [set1, set2,...]} 15 | :param R: a requirements table counting the number of courses required for 16 | each requirements 17 | :param avoid: a set of cids 18 | :param taken: a set of cids 19 | """ 20 | self.G = G 21 | self.update_successors() 22 | if avoid: 23 | self.add_avoid(avoid) 24 | if taken: 25 | self.update_taken(taken) 26 | 27 | self.update_requirements(r_detail, R) 28 | 29 | # labeling is done here because we know the requirements 30 | self.labeling() 31 | 32 | def __str__(self): 33 | return ";\n ".join("{}:\n{}".format(k, v) for k, v in self.G.items()) 34 | 35 | def __contains__(self, item): 36 | return item in self.G 37 | 38 | def __getitem__(self, item): 39 | return self.G[item] 40 | 41 | def __setitem__(self, key, value): 42 | self.G[key] = value 43 | 44 | def add_avoid(self, cids: set): 45 | """ 46 | :param cids: a list of cid, showing the courses you want to avoid 47 | :return: 48 | """ 49 | for cid in cids: 50 | if cid in self.G: 51 | del self.G[cid] 52 | 53 | def items(self): 54 | """ 55 | :return: (cid, course) in self.G graph 56 | """ 57 | return self.G.items() 58 | 59 | def __delitem__(self, key): 60 | del self.G[key] 61 | 62 | def labeling(self): 63 | """ 64 | label courses according to their longest distance to the sink 65 | ( to the courses without any successors) 66 | """ 67 | for cid, course in list(self.G.items()): 68 | if course.courseValue == 0: 69 | del self.G[cid] 70 | else: 71 | course.label = course.courseValue 72 | 73 | topological_order, starts = self._topological_order() 74 | for v in topological_order: 75 | for u in self.G[v].prereq_list(): 76 | if u in self.G: 77 | self.G[u].label = min((self.G[v].label + self.G[u].courseValue), 78 | self.G[u].label) 79 | return starts 80 | 81 | def update_taken(self, cids): 82 | """ 83 | update the taken information. remove courses in cids from the graph, and 84 | tag the prereq of those courses' successors. 85 | :param cids: a list of courses one've taken 86 | """ 87 | for cid in cids: 88 | if cid in self.G: 89 | for child, index in self.G[cid].successors: 90 | if child in self.G: 91 | self.G[child].tag_prereq(index, cid) 92 | del self.G[cid] 93 | 94 | def update_successors(self): 95 | """ 96 | update successors info for courses after adding courses into the graph 97 | """ 98 | for k, v in self.G.items(): 99 | for index, OR in enumerate(v.prereq): 100 | for cid in OR: 101 | if cid in self.G: 102 | self.G[cid].successors.add((k, index)) 103 | 104 | def update_requirements(self, R_detail:dict, R: dict): 105 | """ 106 | add a dict of requirements into the courses. will not remove the requirements already exist in the graph 107 | note: after update requirements, one should update the labels to enable the change 108 | 109 | :param Rs: a set of requirements 110 | """ 111 | for requirement, AND in R_detail.items(): 112 | for index, OR in enumerate(AND): 113 | if R[requirement][index]: 114 | for cid in OR: 115 | if cid in self.G: 116 | self.G[cid].requirements.add((requirement, index)) 117 | 118 | 119 | def _topological_order(self): 120 | """ 121 | :return: a topological ordering for the items in graph 122 | direction is u -> u.prereq (from sink) 123 | """ 124 | from collections import Counter 125 | from copy import deepcopy 126 | 127 | C = deque() # collection of nodes with no incoming edges 128 | D = Counter() # counter for counting the number of incoming edges 129 | for k, v in self.G.items(): 130 | for w in v.prereq_list(): 131 | if w in self.G: 132 | D[w] += 1 133 | for k, v in self.G.items(): # find nodes at sink 134 | if D[k] == 0: 135 | C.append(k) 136 | output = [] 137 | starts = deepcopy(C) 138 | while C: 139 | cid = C.popleft() 140 | output.append(cid) 141 | for w in self.G[cid].prereq_list(): 142 | if w in self.G and w not in output: 143 | D[w] -= 1 144 | if D[w] == 0: 145 | C.append(w) 146 | if len(output) != len(self.G): 147 | raise Exception("exist cycle, cannot get a topological order") 148 | return output, starts 149 | -------------------------------------------------------------------------------- /CourseScheduling/Schedule.py: -------------------------------------------------------------------------------- 1 | """ 2 | A schedule that consists of a few layers (quarters). 3 | """ 4 | 5 | __author__ = "Jenny Zeng" 6 | __email__ = "jennyzengzzh@gmail.com" 7 | 8 | 9 | class Schedule: 10 | """a schedule""" 11 | 12 | def __init__(self, widths): 13 | self.L = [[]] 14 | self.curWidths = [0] 15 | self.widths = widths # maximum width for each layer 16 | 17 | def __len__(self): 18 | return len(self.L) 19 | 20 | def __str__(self): 21 | output = "" 22 | for index, layer in enumerate(self.L): 23 | output += "\nlayer: {index}, with width {curw} and max {wmax}\n".format( 24 | index=index, curw=self.curWidths[index], wmax=self.max_width(index)) 25 | output += "; ".join([str(cid) for cid in layer]) + "\n" 26 | return output 27 | 28 | def clear_empty(self): 29 | """ 30 | clear empty layers at tops until the top layer is a non-empty layer 31 | :return: 32 | """ 33 | while (not self.L[-1]) and (not self.curWidths[-1]): 34 | self.L.pop() 35 | self.curWidths.pop() 36 | 37 | def add_layer(self): 38 | """ 39 | add a empty layer above the top layer, and mark its curWidth to be 0 40 | """ 41 | self.L.append([]) 42 | self.curWidths.append(0) 43 | 44 | def add_course(self, i, cid, c_units): 45 | """ 46 | :param i: index of layer L_i 47 | :param cid: id of a course, that is, the key of a course in graph 48 | :param c_units: course units 49 | :return: None 50 | """ 51 | while i >= len(self.L): 52 | self.add_layer() 53 | self.L[i].append(cid) 54 | self.curWidths[i] += c_units 55 | 56 | def max_width(self, i): 57 | """ 58 | :param i: index for layer L_i 59 | :return: max_width for layer L_i 60 | """ 61 | if i in self.widths: 62 | return self.widths[i] 63 | else: 64 | return self.widths["else"] 65 | 66 | def layer_is_full(self, i, c_units): 67 | """ 68 | :param i: index for layer L_i 69 | :param c_units: new course units 70 | :return: true if adding this course would make L_i exceed 71 | its maximum width 72 | """ 73 | 74 | return (i < len(self.L)) and (self.curWidths[i] + c_units > self.max_width(i)) 75 | -------------------------------------------------------------------------------- /CourseScheduling/Scheduling.py: -------------------------------------------------------------------------------- 1 | """ 2 | Course Scheduling class that will generate schedules a few times according to the input 3 | upper bound range and output the best one. 4 | """ 5 | from copy import deepcopy 6 | 7 | from CourseScheduling.Graph import CourseGraph 8 | from CourseScheduling.Schedule import Schedule 9 | from CourseScheduling.priodict import priorityDictionary as priodict 10 | from CourseScheduling.Course import Course 11 | import warnings 12 | __author__ = "Jenny Zeng" 13 | __email__ = "jennyzengzzh@gmail.com" 14 | 15 | 16 | class Scheduling: 17 | def __init__(self, start_q=0, total_quarter_codes=6): 18 | self.total_quarter_codes = total_quarter_codes 19 | self.start_q = start_q 20 | 21 | def get_best_schedule(self, G: CourseGraph, L: Schedule, R: dict, from_u: int, to_u: int): 22 | """ 23 | :param G: CourseGraph 24 | :param L: Schedule 25 | :param R: brief requirements table 26 | :param from_u: left upper bound range, inclusive 27 | :param to_u: right upper bound range, inclusive 28 | :return: 29 | best: the schedule with the min makespan among all schedules generated 30 | best_u: the upper bound for the best schedule 31 | best_r: How the schedule satisfy the requirements. if number >= 1, 32 | it means there is still some requirements it does not satisfy. 33 | """ 34 | best_L = None 35 | best_u = None 36 | best_r = None 37 | for u in range(from_u, to_u + 1): 38 | G_temp = deepcopy(G) 39 | L_temp = deepcopy(L) 40 | R_temp = deepcopy(R) 41 | schedule = self.get_single_schedule(G_temp, L_temp, R_temp, u) 42 | if schedule and (not best_L or len(schedule) > len(best_L)) \ 43 | and not self._violates_upper(G, L, u): 44 | best_L = schedule 45 | best_u = u 46 | best_r = R_temp 47 | if any([any(i) for i in best_r.values()]): 48 | warnings.warn("Not all requirements are satisfied.") 49 | 50 | return best_L, best_u, best_r 51 | 52 | def get_single_schedule(self, G: CourseGraph, L: Schedule, R, u: int): 53 | """ 54 | :param G: a labeled course graph G 55 | :param L: empty schedule 56 | :param u: upper bound layer index 57 | :param R: requirements table 58 | :return: return the schedule without checking if it violates the upper standing 59 | """ 60 | PQ = self._init_priodict(G) 61 | while PQ: 62 | current = PQ.smallest() 63 | del PQ[current] 64 | cur_course = G[current] 65 | if self._course_satisfy_any_requirements(cur_course, R): # assign this course 66 | assigned_index = self.find_course_assign_index(cur_course, L, u) 67 | L.add_course(assigned_index, current, cur_course.units) 68 | self._expand_queue(G, current, PQ, assigned_index) 69 | self.tag_requirement(R, cur_course) 70 | return L 71 | 72 | def find_course_assign_index(self, v: Course, L: Schedule, u: int): 73 | """ 74 | single course assignment 75 | :param v: course 76 | :param L: schedule 77 | :param u: upperBound index 78 | :return: the index of the layer where v will be assigned 79 | """ 80 | step = len(L) - 1 81 | i = step 82 | if (not self._valid(L, step, v)) or v.has_dependent(step): 83 | i += 1 84 | while not self._valid(L, i, v) and (not v.isUpperOnly or i >= u): 85 | # add new empty layer L_i above current highest layer 86 | # L.add_layer() 87 | i += 1 88 | 89 | lastStep = i 90 | step -= 1 91 | while (v.isUpperOnly and step >= u) or (not v.isUpperOnly and step >= 0): 92 | if v.has_dependent(step): 93 | break 94 | elif self._valid(L, step, v): 95 | lastStep = step 96 | step -= 1 97 | 98 | return lastStep 99 | 100 | def _violates_upper(self, G: CourseGraph, L: Schedule, u: int): 101 | """ 102 | :param G: CourseGraph 103 | :param L: Schedule 104 | :param u: upper bound index 105 | :return: if the schedule violates the upper bound. If violates, return True 106 | """ 107 | # first check R is all 0 108 | # if any([any(i) for i in R.values()]): 109 | # return True 110 | if u > len(L): return True 111 | # check if a upper only class is in lower division 112 | for clist in L.L[:u]: 113 | for cid in clist: 114 | if G[cid].isUpperOnly: 115 | return True 116 | return False 117 | 118 | def tag_requirement(self, R, v: Course): 119 | """ 120 | after we assign the course v to the schedule, we check what requirements it satisfies 121 | :param R: requirements table 122 | :param v: Course 123 | """ 124 | for requirement, index in v.requirements: 125 | R[requirement][index] = max(0, R[requirement][index] - 1) 126 | 127 | def _expand_queue(self, G, cid, PQ: priodict, assigned_index: int): 128 | """ 129 | after we assign course v to the schedule, we expand the priority queue with new ready coruses 130 | at the same time, we also tag prereq in course cid's successors in the corresponding OR set. 131 | :param G: CourseGraph 132 | :param cid: course id, the key in G 133 | :param PQ: Priority queue 134 | :param assigned_index: where cid is assigned in L. 135 | """ 136 | for child, OR_index in G[cid].successors: 137 | if child not in G: 138 | continue 139 | child_course = G[child] 140 | if not child_course.prereqBool[OR_index]: 141 | child_course.tag_prereq(OR_index, cid) 142 | child_course.dependentIndex = max(assigned_index, G[child].dependentIndex) 143 | 144 | if child_course.prereq_is_satisfied(): 145 | PQ[child] = child_course.label 146 | 147 | def _course_satisfy_any_requirements(self, v: Course, R): 148 | """ 149 | :param v: course 150 | :param R: Requirements table 151 | :return: True if v satisfy any requirements in R. 152 | """ 153 | for name, index in v.requirements: 154 | if R[name][index] > 0: 155 | return True 156 | 157 | return False 158 | 159 | def _init_priodict(self, G: CourseGraph): 160 | """ 161 | initialize the priodict with current ready courses 162 | :param G: 163 | :return: 164 | """ 165 | PQ = priodict() 166 | for cid, course in G.items(): 167 | if not course.unsatisfied_prereq(): # course has no prereq 168 | PQ[cid] = course.label 169 | return PQ 170 | 171 | 172 | def _valid(self, L: Schedule, i: int, v: Course): 173 | """ 174 | For a course v, we define a layer L_i with 175 | M_i+v.units < W(L_i) and (i mod 6) in v.quarterCodes 176 | to be a valid layer of v. 177 | 178 | :param L: current schedule 179 | :param i: index for layer L_i 180 | :param v: course 181 | :return: true if valid 182 | """ 183 | return (not L.layer_is_full(i, v.units)) and ((i + self.start_q) % self.total_quarter_codes) in v.quarterCodes 184 | -------------------------------------------------------------------------------- /CourseScheduling/__init__.py: -------------------------------------------------------------------------------- 1 | from CourseScheduling.Course import Course 2 | from CourseScheduling.Graph import CourseGraph 3 | from CourseScheduling.Scheduling import Scheduling 4 | from CourseScheduling.Schedule import Schedule 5 | 6 | 7 | ## two helper functions 8 | 9 | def is_upper_standing(applied_units, upper_units): 10 | """ 11 | :param applied_units: How many units the user has applied 12 | :param upper_units: If applied_units >= upper_units, the user is upper division standing 13 | :return: True if the user is upper division standing 14 | """ 15 | return applied_units > upper_units 16 | 17 | 18 | def update_requirements(R_detail, R, taken): 19 | """ 20 | Update the requirements table according to the courses the user has already taken 21 | :param R_detail: a detail requirements table, showing which course cid satisfy which requirement at each set 22 | :param R: a brief requirements table, showing for each requirements, how many courses is required for each subset 23 | :param taken: a set of courses that the user've taken. They will be removed from requirements table 24 | :return: updated R_detail, R. (but original R_detail and R are changed!!!) 25 | """ 26 | for rid, rlist in R_detail.items(): 27 | for index, rset in enumerate(rlist): 28 | for cid in set(rset): 29 | if cid in taken: 30 | R[rid][index] = max(R[rid][index] - 1, 0) 31 | R_detail[rid][index].remove(cid) 32 | return R_detail, R 33 | -------------------------------------------------------------------------------- /CourseScheduling/priodict.py: -------------------------------------------------------------------------------- 1 | # http://code.activestate.com/recipes/117228/ 2 | # Priority dictionary using binary heaps 3 | # David Eppstein, UC Irvine, 8 Mar 2002 4 | # modified by Jenny Zeng for Python3 compatibility, 4 Dec 2016 5 | 6 | from __future__ import generators 7 | 8 | 9 | class priorityDictionary(dict): 10 | def __init__(self): 11 | '''Initialize priorityDictionary by creating binary heap 12 | of pairs (value,key). Note that changing or removing a dict entry will 13 | not remove the old pair from the heap until it is found by smallest() or 14 | until the heap is rebuilt.''' 15 | self.__heap = [] 16 | dict.__init__(self) 17 | 18 | def smallest(self): 19 | '''Find smallest item after removing deleted items from heap.''' 20 | if len(self) == 0: 21 | raise IndexError("smallest of empty priorityDictionary") 22 | heap = self.__heap 23 | while heap[0][1] not in self or self[heap[0][1]] != heap[0][0]: 24 | lastItem = heap.pop() 25 | insertionPoint = 0 26 | while 1: 27 | smallChild = 2 * insertionPoint + 1 28 | if smallChild + 1 < len(heap) and \ 29 | heap[smallChild] > heap[smallChild + 1]: 30 | smallChild += 1 31 | if smallChild >= len(heap) or lastItem <= heap[smallChild]: 32 | heap[insertionPoint] = lastItem 33 | break 34 | heap[insertionPoint] = heap[smallChild] 35 | insertionPoint = smallChild 36 | return heap[0][1] 37 | 38 | def __iter__(self): 39 | '''Create destructive sorted iterator of priorityDictionary.''' 40 | 41 | def iterfn(): 42 | while len(self) > 0: 43 | x = self.smallest() 44 | yield x 45 | del self[x] 46 | 47 | return iterfn() 48 | 49 | def __setitem__(self, key, val): 50 | '''Change value stored in dictionary and add corresponding 51 | pair to heap. Rebuilds the heap if the number of deleted items grows 52 | too large, to avoid memory leakage.''' 53 | dict.__setitem__(self, key, val) 54 | heap = self.__heap 55 | if len(heap) > 2 * len(self): 56 | self.__heap = [(v, k) for k, v in self.items()] 57 | self.__heap.sort() # builtin sort likely faster than O(n) heapify 58 | else: 59 | newPair = (val, key) 60 | insertionPoint = len(heap) 61 | heap.append(None) 62 | while insertionPoint > 0 and \ 63 | newPair < heap[(insertionPoint - 1) // 2]: 64 | heap[insertionPoint] = heap[(insertionPoint - 1) // 2] 65 | insertionPoint = (insertionPoint - 1) // 2 66 | heap[insertionPoint] = newPair 67 | 68 | def setdefault(self, key, val): 69 | '''Reimplement setdefault to call our customized __setitem__.''' 70 | if key not in self: 71 | self[key] = val 72 | return self[key] 73 | -------------------------------------------------------------------------------- /DataHelper/WebSoc.py: -------------------------------------------------------------------------------- 1 | """ 2 | a crawler that crawl course information on UCI website 3 | """ 4 | from bs4 import BeautifulSoup 5 | import requests 6 | import urllib.parse 7 | import re 8 | 9 | __author__ = "Jenny Zeng" 10 | __email__ = "jennyzengzzh@gmail.com" 11 | 12 | 13 | class WebSoc: 14 | url = "https://www.reg.uci.edu/perl/WebSoc" 15 | 16 | def __init__(self): 17 | self.formData = { 18 | "YearTerm": "2017-03", 19 | "ShowComments": "on", 20 | "ShowFinals": "on", 21 | "Breadth": "ANY", 22 | "Dept": "COMPSCI", 23 | "CourseNum": "", 24 | "Division": "ANY", 25 | "CourseCodes": "", 26 | "InstrName": "", 27 | "CourseTitle": "", 28 | "ClassType": "ALL", 29 | "Units": "", 30 | "Days": "", 31 | "StartTime": "", 32 | "EndTime": "", 33 | "MaxCap": "", 34 | "FullCourses": "ANY", 35 | "FontSize": 100, 36 | "CancelledCourses": "Exclude", 37 | "Bldg": "", 38 | "Room": "", 39 | "Submit": "Display Web Results" 40 | } 41 | self.quarterCode = {0: "2017-92", 1: "2016-03", 2: "2016-14", 42 | 3: "2016-92", 4: "2017-03", 5: "2017-14"} 43 | self.reqURL = "https://www.reg.uci.edu/cob/prrqcgi?" 44 | self.prereqInfo = {'action': 'view_all', 'term': 201792, 'dept': None} 45 | self.depts = [] 46 | 47 | def main(self, depts, filename): 48 | for dept in depts: 49 | print("----------------------------") 50 | print("writing depts: ", dept) 51 | lines = self.makeDeptPrereqRequest(dept) 52 | self._writeDeptCouresInfo(dept, lines, filename) 53 | 54 | def makeDeptPrereqRequest(self, dept): 55 | self.prereqInfo['dept'] = dept 56 | url = self.reqURL + urllib.parse.urlencode(self.prereqInfo) 57 | resp = requests.get(url) 58 | return BeautifulSoup(resp.content, "lxml" 59 | ).find_all( 60 | class_=["course", "title", "prereq"]) 61 | 62 | def _writeDeptCouresInfo(self, dept, lines, filename): 63 | with open(filename, 'a') as f: 64 | for i in range(0, len(lines), 3): 65 | CourseNum, title, prereqs, condition = self._extractInfoFromLine(lines[i:i + 3]) 66 | units, quarters = self._getMatchingUnitAndQuarter(dept, CourseNum) 67 | if quarters: 68 | f.write(dept.replace(" ", "") + ";" + CourseNum + ";" + title + ";" + str(prereqs) + 69 | ";" + units + ";" + str(quarters) + ";" + str(condition) + "\n") 70 | print("wrote course", dept, CourseNum) 71 | 72 | def _extractInfoFromLine(self, info): 73 | num = info[0].a['name'] 74 | title = [i for i in info[1].stripped_strings][0] 75 | prereqs, condition = self._getPrereqs(info[2].get_text("")) 76 | return num, title, prereqs, condition 77 | 78 | def _getMatchingUnitAndQuarter(self, dept, CourseNum): 79 | quarters = set() 80 | units = None 81 | for key, val in self.quarterCode.items(): 82 | temp = self._getInfoByCourseNum(val, dept, CourseNum) 83 | if temp: 84 | quarters.add(key) 85 | units = temp 86 | if quarters: 87 | return units, quarters 88 | else: 89 | return None, None 90 | 91 | def _getPrereqs(self, prereq): 92 | condition = False 93 | prereq = re.sub('|
|\\r|\\n|<.*?td.*?>', "", prereq).strip() 94 | if "AND" in prereq: 95 | L = prereq.split("AND") 96 | else: 97 | L = [prereq] 98 | output = [] 99 | for ors in L: 100 | courses = ors.split("OR") 101 | orSet = set() 102 | for course in courses: 103 | course = re.sub("\(|\)| (\( min grade.*?\))| (\( min score.*?\))|(coreq)|( )|(recommended)", 104 | "", course).replace("&", "&").replace("coreq", "") 105 | # add upper division standing 106 | if course == 'UPPERDIVISIONST': 107 | condition = True 108 | else: 109 | regexp = re.compile(r"(ONLY)|(^NO)|(^AP)|(BETTER)|(COMPUTERSCI&ENGRMAJ) \ 110 | |(ENTRYLEVELWRITING)|(LOWERDIVISIONWRITING)|(^PLACEMENT)|(COMPUTERSCI&ENGRMAJ)|(=)|(>)") 111 | if not regexp.search(course): 112 | orSet.add(course) 113 | if orSet: output.append(orSet) 114 | return output, condition 115 | 116 | def _getInfoByCourseNum(self, YearTerm, Dept, CourseNum): 117 | """currently I am only getting the quarters and units 118 | ['34260', 'Lec', 'A', '4', 'HIRSCHBERG, D.', 'MWF 10:00-10:50', 'PCB 1100', 'Mon, Mar 20, 10:30-12:30pm','246', '157 / 173', 'n/a', '309', 'A', 'Bookstore', 'Web', 'OPEN'] 119 | """ 120 | formData = self.formData.copy() 121 | formData.update({"CourseNum": CourseNum, "Dept": Dept, "YearTerm": YearTerm}) 122 | resp = requests.post(self.url, data=formData) 123 | soup = BeautifulSoup(resp.content, "lxml") 124 | lines = soup.find_all(valign="top") 125 | 126 | # get info list 127 | if lines and [i for i in lines[0].stripped_strings][0].endswith(CourseNum): 128 | for line in lines[1:]: 129 | L = [i for i in line.stripped_strings] 130 | if L[1] == "Lec" or L[1] == "Sem": # get units 131 | return L[3] 132 | return None 133 | 134 | def forSingleCourse(self, dept, CourseNum): 135 | quarters = set() 136 | unit = None 137 | for key, val in self.quarterCode.items(): 138 | print("searching term " + str(key)) 139 | info = self._getInfoByCourseNum(val, dept, CourseNum) 140 | if info: 141 | quarters.add(key) 142 | unit = info 143 | if unit: 144 | print(";" + unit + ";" + str(quarters)) 145 | 146 | 147 | if __name__ == "__main__": 148 | websoc = WebSoc() 149 | websoc.main(["I&C SCI", "COMPSCI", "MATH", "STATS", "IN4MATX", "WRITING"], "fullcourses_new.txt") 150 | -------------------------------------------------------------------------------- /DataHelper/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jennyzeng/CourseScheduling/734e261c48f38f524cbaf7daced2ef1fb1aa2f44/DataHelper/__init__.py -------------------------------------------------------------------------------- /DataHelper/loadData.py: -------------------------------------------------------------------------------- 1 | """ 2 | A helper class that load information from txt files and return in python format. 3 | Refer to the dir: 'info/test/' to see the example format. 4 | """ 5 | import re 6 | 7 | from CourseScheduling.Course import Course 8 | 9 | __author__ = "Jenny Zeng" 10 | __email__ = "jennyzengzzh@gmail.com" 11 | 12 | 13 | class DataLoading: 14 | @staticmethod 15 | def load_width_func_table(filename): 16 | """ 17 | width func format: (refer to info/test/widthFunc.txt) 18 | each line: 19 | [layer index]: [max width (int or float)] 20 | or 21 | else: [max width (int or float)] 22 | """ 23 | wdict = {} 24 | with open(filename, 'r') as f: 25 | for line in f: 26 | line = line.strip().split(":") 27 | if line[0] == "else": 28 | wdict["else"] = int(line[1]) 29 | else: 30 | wdict[int(line[0])] = int(line[1]) 31 | return wdict 32 | 33 | @staticmethod 34 | def load_courses(prereq_filename, show_upper=True): 35 | """ 36 | load courses in the file to the graph 37 | :param G: graph 38 | :param prereq_filename: filename 39 | :param show_upper: if true, config upper, otherwise all courses is not upper only. 40 | :return a dict showing the graph, where key is the unique [dept key+ course num] and value is a Course Object 41 | course format: (refer to info/test/fullcourses_new.txt) 42 | [dept key];[course num];[course name];[prereq];[units];[{quarter code}];[is upper only] 43 | 44 | note: the prereq is in Conjunctive normal form. each item in list is a OR set, and 45 | each OR set in the list is AND relationship. Courses in the same OR set has OR relationship 46 | 47 | e.g. COMPSCI;122A;INTRO TO DATA MGMT;[{'EECS114', 'CSE43', 'I&CSCI33'}];4;{0, 1, 2, 3, 4, 5};False 48 | """ 49 | G = dict() 50 | with open(prereq_filename, 'r') as f: 51 | for line in f: 52 | info = line.strip().split(";") 53 | 54 | G["".join(info[0:2])] = Course(name=info[2], units=int(info[4]), 55 | quarter_codes=eval(info[5]), prereq=eval(info[3]), 56 | is_upper_only=eval(info[6])if show_upper else False) 57 | return G 58 | 59 | @staticmethod 60 | def load_requirements(requirements, filename): 61 | """ 62 | :param requirements: the list of requirements you want to get from the file 63 | :param filename: the name of the file storing the requirements information 64 | :return: 65 | hashTable: store Course nums set for each spec 66 | is only used for updating the graph 67 | R: a table of requirements. 68 | stores the corresponding require num for that course nums set. 69 | each requirement is a AND of ORs. 70 | unlike the prerequisite, requirements require students to take a specific 71 | number of courses in ORs, not only one. 72 | suppose the requirement name is k, and the index of OR is i, then 73 | R[k][i] = n, where n is the number of courses required for OR set i. 74 | 75 | requirements file format: (refer to the info/test/specializations.txt) 76 | [requirement name] 77 | [require number(int)] 78 | { 79 | ... 80 | a list of require courses [dept key][space][course num] 81 | ... 82 | } 83 | ... 84 | other sets for this requirement 85 | ... 86 | ; [use ; to denote the end of this requirement] 87 | """ 88 | hashTable = {} 89 | R = {} 90 | with open(filename) as f: 91 | content = f.read().split(";") 92 | for requirement in content: 93 | requirement = requirement.strip().split('\n') 94 | if requirement[0] not in requirements: 95 | continue 96 | hashTable[requirement[0]] = [] 97 | R[requirement[0]] = [] 98 | i = 1 99 | 100 | while i < len(requirement): 101 | if re.match("^(all)$|^([1-9][0-9]*)$", requirement[i]): 102 | hashTable[requirement[0]].append(set()) 103 | R[requirement[0]].append(requirement[i]) 104 | i += 1 # skip { 105 | 106 | elif requirement[i] == "}": 107 | # change keys at end 108 | if R[requirement[0]][-1] == "all": 109 | R[requirement[0]][-1] = len(hashTable[requirement[0]][-1]) 110 | elif R[requirement[0]][-1] == "recommend": # TODO: need modify later 111 | R[requirement[0]][-1] = len(hashTable[requirement[0]][-1]) // 2 112 | else: 113 | R[requirement[0]][-1] = eval(R[requirement[0]][-1]) 114 | i += 1 115 | elif "{" in requirement[i]: 116 | i += 1 117 | else: 118 | hashTable[requirement[0]][-1].add(requirement[i].replace(" ", "")) 119 | i += 1 120 | 121 | return hashTable, R 122 | 123 | @staticmethod 124 | def load_taken(filename): 125 | """ 126 | taken.txt file is in the following format: (refer to info/test/taken.txt) 127 | first line: start quarter code 128 | second line: total units applied 129 | then, each line on and after third line shows a cid that you've taken.txt 130 | :return: start quater code, units, a set of cid 131 | 132 | """ 133 | with open(filename) as f: 134 | cids = set() 135 | startQ = int(f.readline()) 136 | totalUnits = int(f.readline()) 137 | for cid in f.readlines(): 138 | cid = cid.strip() 139 | cids.add(cid) 140 | return startQ, totalUnits, cids 141 | 142 | @staticmethod 143 | def load_avoid(filename): 144 | """ 145 | :return: a set of cids showing the courses that you want to avoid 146 | format: (refer to info/test/avoid.txt) 147 | each line is a course in format [dept key][course number] 148 | """ 149 | avoids = set() 150 | with open(filename) as f: 151 | for cid in f.readlines(): 152 | cid = cid.strip() 153 | avoids.add(cid) 154 | return avoids 155 | 156 | 157 | if __name__ == "__main__": 158 | # graph = CoursesGraph() 159 | # DataLoading.loadCourses(graph) 160 | # graph.updateSatisfies() 161 | # print(graph) 162 | # print(DataLoading.load_requirements( 163 | # requirements=["Lower-division", "Upper-division", "Intelligent Systems"], 164 | # filename="info/test/specializations.txt")) 165 | 166 | print(DataLoading.load_requirements(requirements=["firstReq", "secondReq"], 167 | filename="test/test_spec.txt")) 168 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Course Scheduling 2 | 3 | ## Introduction 4 | Course Scheduling is a 2 quarters ICS honor research project at University of California, Irvine, and was finalized in June 2017. Zhaohua (Jenny) Zeng is the student and Professor David Eppstein is the advisor for this project. The course scheduling algorithm was inspired by the Coffman-Graham algorithm and the Hu's Algorithm. This algorithm can generate a four-year plan or a partial plan for students at University of California, Irvine, considering the course prerequisites, quarters that a course will be offered, Univ/major/specialization requirements, etc.. 5 | 6 | ## Examples 7 | You can refer to the example.py to see how the data is loaded to the course scheduling graph and how to generate a schedule. Instead of using txt files to store data, it is also possible to build a database, and input preprocessed data into the Course Scheduling API. 8 | 9 | ## Recent Update 10 | I formed a team after I finalized the research project. Now we are working on building a website for the course scheduling algorithm, and the expected release time is late-August, 2017. If you have any suggestions, feel free to email me. 11 | Website development code is in the repository: [CourseScheduling-Web](https://github.com/jennyzeng/CourseScheduling-Web) 12 | 13 | ## Reports 14 | ### Final Report 15 | In the [final report](reports/report-final.pdf), there is an introduction to two related algorithms, the Coffman-Graham algorithm and the Hu's algorithm, as well as a detailed explanation about how the algorithm works. 16 | 17 | ### Initial Plan 18 | In my initial plan, I illustrated my main idea of the course scheduling project, 19 | described the Coffman-Graham algorithm that I will be working on, and some difficulties as well as interesting points. 20 | [research-initial-plan.pdf](reports/research-initial-plan.pdf) 21 | -------------------------------------------------------------------------------- /example.py: -------------------------------------------------------------------------------- 1 | """ 2 | an example about how to load data and generate a schedule 3 | """ 4 | import CourseScheduling as cs 5 | from DataHelper.loadData import DataLoading 6 | import time 7 | __author__ = "Jenny Zeng" 8 | __email__ = "jennyzengzzh@gmail.com" 9 | 10 | if __name__ == '__main__': 11 | start_time = time.time() 12 | 13 | # config upper standing units 14 | upper_units = 90 15 | # load taken info 16 | startQ, applied_units, taken = DataLoading.load_taken(filename="info/taken2.txt") 17 | # load avoid info 18 | avoid = DataLoading.load_avoid(filename="info/avoid.txt") 19 | # load graph, config if user is upper standing 20 | G = DataLoading.load_courses(prereq_filename="info/fullcourses_new.txt", 21 | show_upper=cs.is_upper_standing(applied_units, upper_units)) 22 | # load requirement sheet 23 | R_detail, R = DataLoading.load_requirements( 24 | requirements=["University", "GEI", "GEII", "GEIII", "GEIV", 25 | "GEV", "GEVI", "GEVII", "GEVIII", "CS-Lower-division", "CS-Upper-division", 26 | "Intelligent Systems" 27 | ], 28 | filename="info/specializations.txt") 29 | 30 | # update requirement table based on the taken information 31 | cs.update_requirements(R_detail, R, taken) 32 | print(R_detail) 33 | print(R) 34 | # load max width for each quarter 35 | max_widths = DataLoading.load_width_func_table("info/widthFunc.txt") 36 | 37 | # construct CourseGraph. graph is labeled after init 38 | graph = cs.CourseGraph(G, r_detail=R_detail, R=R, avoid=avoid, taken=taken) 39 | 40 | # construct Schedule with width func requirements 41 | L = cs.Schedule(widths=max_widths) 42 | # construct the scheduling class 43 | generator = cs.Scheduling(start_q=startQ) 44 | # get the best schedule when the upper bound ranges from 0 to 10, inclusive. 45 | L, best_u, best_r = generator.get_best_schedule(graph, L, R, 0, 10) 46 | print(L) 47 | print(best_u) 48 | print(R_detail) 49 | print(best_r) 50 | print("--- %s seconds ---" % (time.time() - start_time)) 51 | # in terminal, type: 52 | # python -m cProfile example.py 53 | # to see the time and calls for each function 54 | -------------------------------------------------------------------------------- /example2.py: -------------------------------------------------------------------------------- 1 | """ 2 | an example about how to load data and generate a schedule 3 | """ 4 | import CourseScheduling as cs 5 | from DataHelper.loadData import DataLoading 6 | import time 7 | __author__ = "Jenny Zeng" 8 | __email__ = "jennyzengzzh@gmail.com" 9 | 10 | if __name__ == '__main__': 11 | start_time = time.time() 12 | 13 | # config upper standing units 14 | upper_units = 10 15 | # load taken info 16 | startQ, applied_units, taken = DataLoading.load_taken(filename="test/taken.txt") 17 | # load avoid info 18 | avoid = DataLoading.load_avoid(filename="test/avoid.txt") 19 | # load graph, config if user is upper standing 20 | G = DataLoading.load_courses(prereq_filename="test/testcourses.txt", 21 | show_upper=cs.is_upper_standing(applied_units, upper_units)) 22 | # load requirement sheet 23 | R_detail, R = DataLoading.load_requirements( 24 | requirements=["firstReq","secondReq"], 25 | filename="test/test_spec.txt") 26 | 27 | # update requirement table based on the taken information 28 | cs.update_requirements(R_detail, R, taken) 29 | 30 | # load max width for each quarter 31 | max_widths = DataLoading.load_width_func_table("test/test_widthFunc.txt") 32 | 33 | # construct CourseGraph. graph is labeled after init 34 | graph = cs.CourseGraph(G, r_detail=R_detail, R=R, avoid=avoid, taken=taken) 35 | 36 | # construct Schedule with width func requirements 37 | L = cs.Schedule(widths=max_widths) 38 | # construct the scheduling class 39 | generator = cs.Scheduling(start_q=startQ) 40 | # get the best schedule when the upper bound ranges from 0 to 10, inclusive. 41 | L, best_u, best_r = generator.get_best_schedule(graph, L, R, 0, 10) 42 | print(L) 43 | print(best_u) 44 | print(R_detail) 45 | print(best_r) 46 | print(graph) 47 | print("--- %s seconds ---" % (time.time() - start_time)) 48 | # in terminal, type: 49 | # python -m cProfile example.py 50 | # to see the time and calls for each function 51 | -------------------------------------------------------------------------------- /info/avoid.txt: -------------------------------------------------------------------------------- 1 | COMPSCI141 -------------------------------------------------------------------------------- /info/fullcourses.txt: -------------------------------------------------------------------------------- 1 | I&CSCI;90;NEW STUDENTS SEMINR;[];1;{0,3};False 2 | I&CSCI;6B;BOOLEAN ALG & LOGIC;[];4;{0, 1, 2, 3, 4, 5};False 3 | I&CSCI;6D;DISCRET MATH FOR CS;[{'I&CSCI6B'}];4;{0, 1, 2, 3, 4, 5};False 4 | I&CSCI;10;HOW COMPUTERS WORK;[];4;{0, 2, 5};False 5 | I&CSCI;31;INTRO TO PROGRMMING;[];4;{0, 1, 2, 3, 4, 5};False 6 | I&CSCI;32;PROG SOFTWARE LIBR;[{'I&CSCI31', 'CSE41'}];4;{0, 1, 2, 3, 4, 5};False 7 | I&CSCI;33;INTERMEDIATE PRGRMG;[{'CSE42', 'I&CSCI32'}];4;{0, 1, 2, 3, 4, 5};False 8 | I&CSCI;45C;PROGRAM IN C/C++;[{'I&CSCI22', 'IN4MATX42', 'EECS40', 'CSE43', 'I&CSCI33', 'CSE22'}];4;{0, 1, 2, 3, 4, 5};False 9 | I&CSCI;45J;PROGRAMMING IN JAVA;[{'I&CSCI33', 'CSE43'}];4;{0, 1, 3};False 10 | I&CSCI;46;DATA STRC IMPL&ANLS;[{'CSE45C', 'I&CSCI45C'}];4;{0, 1, 2, 3, 4, 5};False 11 | I&CSCI;51;INTRO COMPUTER ORG;[{'I&CSCIH21', 'IN4MATX42', 'I&CSCI21', 'I&CSCI31', 'CSE21', 'CSE41'}, {'I&CSCI6B'}];6;{0, 1, 2, 3, 4, 5};False 12 | I&CSCI;53+53L;PRINCP IN SYS DESGN;[{'I&CSCI51'}];6;{1, 2, 4, 5};False 13 | I&CSCI;60;CMP GAMES & SOCIETY;[];4;{0, 2, 3};False 14 | I&CSCI;62;GAME TECH&INT MEDIA;[{'I&CSCIH21', 'IN4MATX42', 'I&CSCI21', 'I&CSCI31', 'CSE21', 'CSE41'}];4;{2, 5};False 15 | I&CSCI;139W;CRITICAL WRITING;[{'WRITINGLOW2'}];4;{0, 1, 2, 3, 4, 5};True 16 | I&CSCI;161;DES&ANALYS OF ALGOR;[{'I&CSCI65', 'I&CSCI45C'}];4;{1, 4};False 17 | I&CSCI;162;MODELNG &WORLD BLDG;[{'COMPSCI112'}];4;{1, 3};False 18 | I&CSCI;163;MOBILE & UBI GAMES;[{'I&CSCI61'}, {'I&CSCIH21', 'I&CSCI10', 'I&CSCI21', 'I&CSCI31', 'IN4MATX41', 'CSE21', 'CSE41'}];4;{0, 5};False 19 | I&CSCI;166;GAME DESIGN;[{'I&CSCI61'}, {'I&CSCI52', 'IN4MATX43'}];4;{2, 5};False 20 | I&CSCI;167;MULTIPLAYER SYSTEMS;[{'I&CSCI51'}];4;{1, 4};False 21 | I&CSCI;168;COMP&NETWRK SECURTY;[{'I&CSCI52', 'IN4MATX43'}, {'I&CSCI167'}];4;{2, 5};False 22 | I&CSCI;169A;CAPSTONE GAME I;[{'I&CSCI168'}];4;{0, 3};False 23 | I&CSCI;169B;CAPSTONE GAME II;[{'I&CSCI169A'}];4;{1, 4};False 24 | COMPSCI;111;DIGITAL IMAGE PROC;[{'CSE23', 'CSE46', 'I&CSCI46', 'I&CSCIH23', 'I&CSCI23'}, {'I&CSCI6D'}, {'MATH6G', 'MATH3A', 'I&CSCI6N'}];4;{0, 3};False 25 | COMPSCI;112;COMPUTER GRAPHICS;[{'I&CSCI33', 'I&CSCI22', 'I&CSCIH22', 'CSE22', 'CSE43'}, {'CSE45C', 'I&CSCI45C'}, {'MATH6G', 'MATH3A', 'I&CSCI6N'}];4;{0, 4};False 26 | COMPSCI;113;CMPTR GAME DEVLPMNT;[{'COMPSCI171', 'IN4MATX121', 'I&CSCI166', 'ART106B', 'I&CSCI163', 'COMPSCI112'}];4;{0, 3};False 27 | COMPSCI;114;PROJ IN ADV GRAPHIC;[{'COMPSCI112'}, {'CSE45C', 'I&CSCI45C'}, {'COMPSCI161'}, {'CSE161'}, {'COMPSCI164'}, {'COMPSCI165'}];4;{2, 5};False 28 | COMPSCI;115;COMPUTER SIMULATION;[{'I&CSCI6B'}, {'MATH6G', 'I&CSCI6N'}, {'STATS67'}, {'I&CSCI51'}, {'I&CSCI52', 'IN4MATX43'}];4;{1, 4};True 29 | COMPSCI;116;COMP PHOTO & VISION;[{'I&CSCI6D'}, {'MATH6G', 'MATH3A', 'I&CSCI6N'}, {'MATH2B'}, {'CSE23', 'CSE46', 'I&CSCI46', 'I&CSCIH23', 'I&CSCI23'}];4;{1, 4};False 30 | COMPSCI;117;PROJ IN COMP VISION;[{'I&CSCI6D'}, {'MATH6G', 'MATH3A', 'I&CSCI6N'}, {'MATH2B'}, {'CSE23', 'CSE46', 'I&CSCI46', 'I&CSCIH23', 'I&CSCI23'}, {'COMPSCI116', 'COMPSCI178', 'COMPSCI171', 'COMPSCI112'}];4;{2};False 31 | COMPSCI;121;INFRMTION RETRIEVAL;[{'I&CSCI45J', 'CSE45C', 'I&CSCI45C'}, {'STATS7', 'STATS67'}];4;{1, 2, 4};False 32 | COMPSCI;122A;INTRO TO DATA MGMT;[{'I&CSCI33', 'EECS114', 'CSE43'}];4;{0, 1, 2, 3, 4, 5};False 33 | COMPSCI;122B;PROJ DATA&WEB APPS;[{'EECS116', 'COMPSCI122A'}, {'I&CSCI45J'}];4;{1, 2, 4, 5};False 34 | COMPSCI;122C;PRINCIPLS DATA MGMT;[{'COMPSCI122A'}, {'COMPSCI143A'}, {'COMPSCI152'}];4;{0, 3};False 35 | COMPSCI;125;NXT GEN SRCH SYSTMS;[{'CSE21', 'IN4MATX41', 'I&CSCI21', 'I&CSCI31', 'CSE41'}];4;{1, 4};True 36 | COMPSCI;131;PARLLEL DIST CMPTNG;[{'I&CSCI53+53L', 'COMPSCI143A'}, {'COMPSCI143A'}];4;{1, 4};False 37 | COMPSCI;132;COMPUTER NETWORKS;[{'EECS55', 'STATS67'}];4;{0, 2, 3, 5};False 38 | COMPSCI;133;ADV COMPUTER NETWKS;[{'COMPSCI132'}];4;{1, 4};False 39 | COMPSCI;134;COMP&NETWRK SECURTY;[{'I&CSCI6D'}, {'I&CSCI22', 'IN4MATX42', 'CSE43', 'I&CSCI33', 'I&CSCIH22', 'CSE22'}, {'EECS116', 'COMPSCI143A', 'COMPSCI132', 'CSE104', 'COMPSCI122A'}];4;{0, 4};False 40 | COMPSCI;137;INTERNET APPS ENGR;[{'COMPSCI132', 'EECS148'}, {'I&CSCI45J'}];4;{2, 5};False 41 | COMPSCI;141;CONCEPT PGMG LANG I;[{'CSE31', 'I&CSCI51', 'EECS31'}, {'I&CSCI46', 'CSE46'}];4;{0, 3};False 42 | COMPSCI;142A;COMPILERS&INTPRETER;[{'IN4MATX101', 'CSE141', 'COMPSCI141'}];4;{1, 4};False 43 | COMPSCI;143A;PRNCPLS OPERTNG SYS;[{'CSE23', 'CSE46', 'I&CSCI46', 'I&CSCIH23', 'I&CSCI23'}, {'CSE31', 'I&CSCI51', 'EECS31'}];4;{0, 1, 2, 3, 5};False 44 | COMPSCI;143B;PROJ IN OPERTNG SYS;[{'COMPSCI143A', 'CSE104'}];4;{1, 4};False 45 | COMPSCI;145+145L;EMBEDDED SOFTWARE;[{'I&CSCI46', 'CSE46'}, {'CSE132', 'EECS112', 'I&CSCI51'}];6;{2};False 46 | COMPSCI;146;MULTITASK OPER SYS;[{'CSE23', 'CSE46', 'I&CSCI46', 'I&CSCIH23', 'I&CSCI23'}, {'I&CSCI51'}, {'COMPSCI143A'}];4;{2, 5};False 47 | COMPSCI;151;DIGITAL LOG DESIGN;[{'CSE23', 'I&CSCI23', 'CSE43', 'CSE46', 'I&CSCI46', 'I&CSCIH23', 'I&CSCI33'}, {'I&CSCI51'}, {'I&CSCI6B'}, {'I&CSCI6D'}];4;{0, 3};False 48 | COMPSCI;152;COMPUTR SYST ARCHIT;[{'COMPSCI151'}];4;{1, 4};False 49 | COMPSCI;153;LOGIC DESIGN LAB;[{'COMPSCI151'}];4;{4};False 50 | COMPSCI;154;COMPUTER DESIGN LAB;[{'COMPSCI151'}];4;{2, 5};False 51 | COMPSCI;161;DES&ANALYS OF ALGOR;[{'CSE23', 'CSE46', 'I&CSCI46', 'I&CSCIH23', 'I&CSCI23'}, {'I&CSCI6B'}, {'I&CSCI6D'}, {'MATH2B'}];4;{0, 1, 3, 4};False 52 | COMPSCI;162;FORMAL LANG & AUTM;[{'CSE23', 'CSE46', 'I&CSCI46', 'I&CSCIH23', 'I&CSCI23'}, {'MATH2A'}, {'MATH2B'}, {'I&CSCI6B'}, {'I&CSCI6D'}];4;{1, 3};False 53 | COMPSCI;163;GRAPH ALGORITHMS;[{'COMPSCI161', 'CSE161'}];4;{2, 5};False 54 | COMPSCI;164;COMPUTATIONAL GEO;[{'CSE23', 'CSE46', 'I&CSCI46', 'I&CSCIH23', 'I&CSCI23'}];4;{4};False 55 | COMPSCI;165;PROJ ALG & DATA STR;[{'COMPSCI161', 'CSE161'}, {'I&CSCI45C'}];4;{2,5};False 56 | COMPSCI;167;APPLIED CRYPTO;[{'COMPSCI161', 'CSE161'}];4;{1};True 57 | COMPSCI;169;INTRO OPTIMIZATION;[{'MATH6G', 'MATH3A', 'I&CSCI6N'}, {'STATS67'}];4;{0, 3};False 58 | COMPSCI;171;INTRO ARTIFCL INTEL;[{'STATS67'}, {'CSE23', 'CSE46', 'I&CSCI46', 'I&CSCIH23', 'I&CSCI23'}, {'MATH2B'}];4;{0, 1, 3, 4};False 59 | COMPSCI;172B;NRL NTWKS&DEEP LRNG;[{'COMPSCI178', 'MATH121A', 'COMPSCI273A', 'STATS120A'}, {'COMPSCI273A', 'COMPSCI178', 'MATH121A', 'STATS120B'}];4;{1};False 60 | COMPSCI;175;PROJECT IN AI;[{'COMPSCI171'}, {'COMPSCI178'}];4;{1, 2, 4};False 61 | COMPSCI;177;APP OF PROB IN CS;[{'MATH2B'}, {'STATS67'}, {'I&CSCI6B'}, {'I&CSCI6D'}, {'MATH6G', 'MATH3A', 'I&CSCI6N'}];4;{2};False 62 | COMPSCI;178;MACHINE/DATA MINING;[{'I&CSCI6B'}, {'I&CSCI6D'}, {'MATH6G', 'MATH3A', 'I&CSCI6N'}, {'MATH2B'}, {'STATS7', 'STATS67'}];4;{1, 4};False 63 | COMPSCI;179;ALGRTMS GRAPH MDLS;[{'CSE23', 'CSE46', 'I&CSCI46', 'I&CSCI23'}, {'MATH2A'}, {'MATH2B'}, {'STATS67'}];4;{3};False 64 | COMPSCI;183;INTRO COMP BIOLOGY;[{'MATH2J', 'STATS7', 'MATH2D', 'STATS8'}];4;{0, 3};False 65 | COMPSCI;184A;ALGRTHMS FOR MOLBIO;[{'MATH6G', 'MATH3A', 'I&CSCI6N'}];4;{0, 4};False 66 | COMPSCI;184C;COMPUTATNL SYS BIO;[{'COMPSCI184A'}];4;{2, 5};False 67 | COMPSCI;190;SPECIAL TOPICS;[{'COMPSCI143A'}];4;{2, 4, 5};False 68 | COMPSCI;201;CRYPTO PROTOCOLS;[{'COMPSCI263', 'COMPSCI260'}];4;{1};False 69 | COMPSCI;202;APPLIED CRYPTY;[{'COMPSCI260'}, {'COMPSCI263'}];4;{0, 3};False 70 | COMPSCI;203;NTWK & DIST SYS SEC;[{'COMPSCI132', 'EECS148'}];4;{1, 4};False 71 | COMPSCI;211B;COMPGRPHC &VISULZTN;[{'COMPSCI211A'}];4;{4};False 72 | COMPSCI;212;MULTILMED SYS APPS;[{'COMPSCI143A'}, {'COMPSCI161'}, {'COMPSCI131'}, {'COMPSCI132'}, {'COMPSCI133'}];4;{1};False 73 | COMPSCI;213;INTRO TO VIS PERCEP;[{'MATH121A'}];4;{1};False 74 | COMPSCI;216;IMAGE UNDERSTANDING;[{'I&CSCI6D'}, {'MATH6G', 'MATH3A', 'I&CSCI6N'}, {'MATH2B'}, {'I&CSCI46', 'CSE46'}];4;{2, 4};False 75 | COMPSCI;221;INFO RETRIEVAL;[{'COMPSCI161'}, {'COMPSCI171'}, {'MATH6G', 'MATH3A', 'I&CSCI6N'}];4;{1, 4};False 76 | COMPSCI;222;PRINCIPLS DATA MGMT;[{'COMPSCI122A'}, {'COMPSCI143A'}, {'COMPSCI152'}];4;{0, 3};False 77 | COMPSCI;223;PROC&DSTD DATA MGMT;[{'COMPSCI222'}, {'COMPSCI131'}];4;{2, 5};False 78 | COMPSCI;225;NXT GEN SRCH SYSTMS;[{'CSE21', 'IN4MATX41', 'I&CSCI21', 'I&CSCI31', 'CSE41'}];4;{1, 4};False 79 | COMPSCI;232;CMPTR & COMM NTWRKS;[{'COMPSCI132', 'EECS148'}];4;{1, 3};False 80 | COMPSCI;236;WIRELESS NETWORKING;[{'COMPSCI132', 'EECS148'}];4;{2, 4};False 81 | COMPSCI;241;ADV COMPILER CONSTR;[{'COMPSCI142A'}];4;{1, 4};False 82 | COMPSCI;244;INTR EMBED UBIQ SYS;[{'I&CSCI51'}, {'COMPSCI152'}, {'MATH6G', 'I&CSCI6D', 'MATH3A', 'I&CSCI6N'}, {'COMPSCI161'}];4;{0, 3};False 83 | COMPSCI;245;SW 4 EMBEDDED SYS;[{'I&CSCI51'}, {'COMPSCI152'}, {'COMPSCI161'}, {'MATH6G', 'I&CSCI6D', 'MATH3A', 'I&CSCI6N'}];4;{5};False 84 | COMPSCI;248B;UBIQ COMP INTERACTN;[{'IN4MATX231'}, {'IN4MATX241'}];4;{1};False 85 | COMPSCI;250A;CMPTR SYS ARCHITECT;[{'COMPSCI152'}];4;{2};False 86 | COMPSCI;252;INTRO TO CMPTR DSGN;[{'COMPSCI151'}, {'COMPSCI152'}];4;{1};False 87 | COMPSCI;260;FUND ALGORITHMS;[{'COMPSCI161'}];4;{0, 1, 3, 4};False 88 | COMPSCI;261;DATA STRUCTURES;[{'I&CSCI46'}, {'COMPSCI161'}];4;{2, 4};False 89 | COMPSCI;263;ANALYSIS OF ALGRTHM;[{'COMPSCI161'}, {'COMPSCI261'}];4;{1};False 90 | COMPSCI;265;GRAPH ALGORITHMS;[{'COMPSCI161'}, {'COMPSCI261'}];4;{2, 5};False 91 | COMPSCI;266;COMPTNL GEOMETRY;[{'COMPSCI161'}, {'COMPSCI261'}];4;{4};False 92 | COMPSCI;268;INTRO OPTIMIZATION;[{'STATS67'}, {'MATH6G', 'MATH3A', 'I&CSCI6N'}];4;{0, 3};False 93 | COMPSCI;273A;MACHINE LEARNING;[{'COMPSCI271'}, {'COMPSCI206'}];4;{0, 3};False 94 | COMPSCI;274A;PROB LEARNING;[{'COMPSCI206'}];4;{1, 4};False 95 | COMPSCI;274B;GRAPHICAL MODELS;[{'COMPSCI274A'}];4;{5};False 96 | COMPSCI;274C;NRL NTWKS&DEEP LRNG;[{'STATS120A'}, {'COMPSCI273A', 'COMPSCI178', 'MATH121A', 'STATS120B'}];4;{1};False 97 | COMPSCI;278;PROBABILITY MODELS;[{'STATS120A'}];4;{5};False 98 | COMPSCI;284C;COMPUTATNL SYS BIO;[{'COMPSCI284B', 'BIOSCI99', 'COMPSCI284A'}, {'COMPSCI284B', 'MATH2D', 'COMPSCI284A'}, {'MATH2J', 'COMPSCI284B', 'COMPSCI284A'}];4;{2, 5};False 99 | MATH;2A;CALCULUS;[];4;{0, 1, 2, 3, 4, 5};False 100 | MATH;2B;CALCULUS;[{'MATH5A', 'MATH2A'}];4;{0, 1, 2, 3, 4, 5};False 101 | MATH;2D;MULTIVAR CALCULUS;[{'MATH2B', 'MATH5B'}];4;{0, 1, 2, 3, 4, 5};False 102 | MATH;2E;MULTIVAR CALCULUS;[{'MATH2D', 'MATHH2D'}];4;{0, 1, 2, 3, 4, 5};False 103 | MATH;3A;INTRO LINEAR ALGBRA;[{'MATH2B', 'MATH5B'}];4;{0, 1, 2, 3, 4, 5};False 104 | MATH;3D;ELEM DIFF EQUATIONS;[{'MATH2B'}, {'MATH2D', 'MATHH2D'}, {'MATH2J', 'MATH3A'}];4;{0, 1, 2, 3, 4, 5};False 105 | MATH;4;MATH FOR ECONOMISTS;[{'MATH2B'}];4;{0, 1, 2, 3, 4, 5};False 106 | MATH;5A;CALCULUS LIFE SCIEN;[{'MATH1B'}];4;{0, 1, 2, 3, 4, 5};False 107 | MATH;5B;CALCULUS LIFE SCIEN;[{'MATH5A', 'MATH2A'}];4;{0, 1, 2, 3, 4, 5};False 108 | MATH;8;FUNCTIONS &MODELING;[{'MATH2A'}];4;{2, 5};False 109 | MATH;9;INTRO PROG NUM ANLY;[{'MATH2A'}];4;{0, 1, 2, 3, 4, 5};False 110 | MATH;13;INTRO ABSTRACT MATH;[{'I&CSCI6D', 'MATH6D', 'MATH2A'}];4;{0, 1, 2, 3, 4, 5};False 111 | MATH;105A;NUMERICAL ANALYSIS;[{'MATH6G', 'MATH3A'}];4;{0, 3};False 112 | MATH;105B;NUMERICAL ANALYSIS;[{'MATH105A'}];4;{1, 4};False 113 | MATH;107;NUMERICAL DIFF EQNS;[{'MATH3D'}, {'MATH105B'}];4;{2, 5};False 114 | MATH;112A;INT PARTIAL DIF EQU;[{'MATH2D'}, {'MATH3D'}];4;{0, 3};False 115 | MATH;112B;INT PARTIAL DIF EQU;[{'MATH2E'}, {'MATH112A'}];4;{1, 4};False 116 | MATH;112C;INT PARTIAL DIF EQU;[{'MATH112B'}];4;{2, 5};False 117 | MATH;113A;MATH MODELNG IN BIO;[{'MATH2B'}];4;{0, 3};False 118 | MATH;113B;MATH MODELNG IN BIO;[{'MATH113A'}];4;{1, 4};False 119 | MATH;115;MATH MODELING;[{'MATH6G', 'MATH3A', 'MATH6C'}, {'MATH3D'}, {'ENGRMAE140', 'MATH112A'}];4;{2, 5};False 120 | MATH;117;DYNAMICAL SYSTEMS;[{'MATH3D'}, {'MATH140A'}];4;{1, 4};False 121 | MATH;118;THRY DIFF EQUATIONS;[{'MATH3D'}, {'MATH140A'}];4;{0, 3};False 122 | MATH;119;BNDRY VALUE PRBLMS;[{'MATH3D'}, {'MATH140A'}];4;{2};False 123 | MATH;120A;INTRO GROUP THEORY;[{'MATH6G', 'MATH3A'}, {'MATH13'}];4;{0, 1, 2, 3, 4};False 124 | MATH;120B;INTRO RINGS/FIELDS;[{'MATH120A'}];4;{1, 2, 4, 5};False 125 | MATH;120C;INTRO GALOIS THEORY;[{'MATH120B'}];4;{2, 5};False 126 | MATH;H120A;HON GRAD ALGBR I;[{'MATH3A'}];5;{0, 3};False 127 | MATH;H120B;HON GRAD ALGBR II;[{'MATHH120A'}];5;{1, 4};False 128 | MATH;121A;LINEAR ALGEBRA;[{'MATH6C', 'MATH6G', 'MATH3A'}, {'MATH13'}];4;{0, 1, 2, 3, 4, 5};False 129 | MATH;121B;LINEAR ALGEBRA;[{'MATH121A'}];4;{1, 2, 4, 5};False 130 | MATH;130A;PROB&STOCHASTC PROC;[{'MATH2A'}, {'MATH2B'}, {'MATH6G', 'MATH3A'}];4;{0, 1, 2, 3, 4};False 131 | MATH;130B;PROB&STOCH PROCESS;[{'MATH131A', 'MATH130A', 'STATS120A'}];4;{1, 2, 4};False 132 | MATH;130C;PROB & STOCH PROC;[{'MATH130B'}];4;{2, 5};False 133 | MATH;133A;STAT MTHD FINANCE I;[{'MATH131A', 'MATH130A', 'STATS120A'}];4;{1, 4};False 134 | MATH;133B;STAT MTHD FNANCE II;[{'MATH133A'}];4;{2, 5};False 135 | MATH;140A;ELEMENTARY ANALYSIS;[{'MATH2D'}, {'MATH3A'}, {'MATH13'}];4;{0, 1, 2, 3, 4};False 136 | MATH;140B;ELEMENTARY ANALYSIS;[{'MATH140A'}];4;{1, 2, 4, 5};False 137 | MATH;140C;ANALY SEV VARIABLES;[{'MATH140B'}];4;{2, 5};False 138 | MATH;H140A;HON GRAD ANLYS I;[{'MATH2E'}, {'MATH3A'}, {'MATH13'}];5;{0, 3};False 139 | MATH;H140B;HON GRAD ANLYS II;[{'MATHH140A'}];5;{1, 4};False 140 | MATH;141;INTRO TOPOLOGY;[{'MATH140A'}];4;{2, 5};False 141 | MATH;147;COMPLEX ANALYSIS;[{'MATH140A'}];4;{1, 2, 3, 4};False 142 | MATH;150;INTRO TO MATH LOGIC;[{'I&CSCI6B', 'MATH13', 'MATH6B'}, {'MATH13', 'MATH6D', 'I&CSCI6D'}];4;{0, 3};False 143 | MATH;161;MODERN GEOMETRY;[{'MATH13', 'I&CSCI6B'}, {'MATH13', 'I&CSCI6D'}];4;{1, 2, 4, 5};False 144 | MATH;162A;INTRO DIFFRNTL GEOM;[{'MATH2E'}, {'MATH3A'}, {'MATH3D'}];4;{1, 4};False 145 | MATH;162B;INTRO DIFFRNTL GEOM;[{'MATH162A'}];4;{2};False 146 | MATH;173A;INTRO CRYPTOLOGY;[{'MATH2B'}, {'MATH6G', 'MATH3A'}, {'MATH13', 'I&CSCI6B'}, {'MATH13', 'I&CSCI6D'}];4;{0, 3};False 147 | MATH;175;COMBINATORICS;[{'MATH2B'}, {'MATH13'}];4;{1, 4};False 148 | MATH;176;MATH OF FINANCE;[{'MATH3A'}];4;{1, 4};False 149 | MATH;180A;NUMBER THEORY I;[{'MATH3A'}, {'MATH13'}];4;{1, 4};False 150 | MATH;180B;NUMBER THEORY II;[{'MATH180A'}];4;{2, 5};False 151 | MATH;184;HISTORY OF MATH;[{'MATH3D'}, {'MATH120A'}, {'MATH140A'}];4;{2, 5};False 152 | STATS;7;BASIC STATISTICS;[];4;{0, 1, 2, 3, 4, 5};False 153 | STATS;67;INTRO PROB&STAT/CS;[{'MATH2B'}];4;{0, 1, 2, 3, 4, 5};False 154 | STATS;110;STATS METH DATA I;[{'STATS7', 'STATS8', 'MATH131A', 'STATS120A'}, {'STATS7', 'MATH131B', 'STATS8', 'STATS120B'}, {'MATH131C', 'STATS7', 'STATS8', 'STATS120C'}];4;{0, 3};False 155 | STATS;111;STATS METH DATA II;[{'STATS110'}];4;{1, 4};False 156 | STATS;112;STATS METH DATA III;[{'STATS111'}];4;{2, 5};False 157 | STATS;115;BAYES DATA ANALYSIS;[{'STATS120C'}, {'STATS110'}];4;{1, 3};False 158 | STATS;120A;INTRO PROB & STATS;[{'MATH2A'}, {'MATH2B'}, {'MATH2D', 'MATH4'}];4;{0, 3};False 159 | STATS;120B;INTRO PROB & STATS;[{'STATS120A'}];4;{1, 4};False 160 | STATS;120C;INTRO PROB & STATS;[{'STATS120B'}, {'MATH6G', 'MATH3A', 'I&CSCI6N'}];4;{2, 5};False 161 | STATS;140;MULTIVAR STAT METH;[{'STATS120C'}, {'MATH3A', 'I&CSCI6N'}];4;{0};False 162 | STATS;200A;INT PROB & STAT THY;[{'STATS120C'}];4;{0, 3};False 163 | STATS;200B;INT PROB & STAT THY;[{'STATS200A'}];4;{1, 4};False 164 | STATS;200C;INT PROB & STAT THY;[{'STATS200B'}];4;{2, 5};False 165 | STATS;201;STATS METH DATA I;[{'STATS7', 'STATS8'}];4;{0, 3};False 166 | STATS;202;STATS METH DATA II;[{'STATS210', 'STATS201'}];4;{1, 4};False 167 | STATS;203;STATS METH DATA III;[{'STATS202'}];4;{2, 5};False 168 | STATS;205;BAYES DATA ANALYSIS;[{'STATS120C'}, {'STATS201'}, {'STATS210'}];4;{1, 3};False 169 | STATS;211;STATS METHOD II;[{'STATS210'}];4;{1, 4};False 170 | STATS;212;STATS METHODS III;[{'STATS211'}];4;{2, 5};False 171 | STATS;220A;ADV PROB&STATTOPICS;[{'STATS200C'}];4;{1, 3};False 172 | STATS;220B;ADV PROB&STATTOPICS;[{'STATS220A'}, {'MATH140B'}];4;{2, 5};False 173 | STATS;225;BAYESIAN STATISTICS;[{'STATS205'}, {'STATS230'}];4;{2, 5};False 174 | STATS;235;MOD DATA ANLYS METH;[{'STATS120C'}, {'STATS205'}, {'STATS210', 'STATS201'}];4;{5};False 175 | STATS;240;MULTIVAR STAT METH;[{'STATS120C'}, {'MATH3A', 'I&CSCI6N'}];4;{0};False 176 | STATS;245;TIME SERIES ANALYS;[{'STATS200C'}, {'STATS210', 'STATS201'}];4;{1};False 177 | STATS;250;BIOSTATISTICS;[{'STATS210'}];4;{2};False 178 | STATS;275;STAT CONSULTING;[{'STATS212', 'STATS203'}];4;{0, 4};False 179 | IN4MATX;43;INTRO SOFTWARE ENGR;[{'CSE42', 'I&CSCI32'}];4;{0, 2, 3, 5};False 180 | IN4MATX;101;CONCEPT PGMG LANG I;[{'CSE31', 'I&CSCI51', 'EECS31'}, {'I&CSCI46', 'CSE46'}];4;{0, 3};False 181 | IN4MATX;102;CONCPT PGMG LANG II;[{'CSE141', 'IN4MATX101', 'COMPSCI141'}];4;{5};False 182 | IN4MATX;113;REQT ANALYSIS & ENG;[{'I&CSCI33', 'I&CSCI22', 'IN4MATX42', 'CSE22', 'CSE43'}, {'I&CSCI52', 'IN4MATX43'}];4;{1, 4};False 183 | IN4MATX;115;SW TEST&QUAL ASSUR;[{'I&CSCI45J', 'CSE45C', 'CSE23', 'I&CSCI45C', 'I&CSCI65', 'CSE46', 'I&CSCI46', 'I&CSCI23', 'IN4MATX45'}, {'I&CSCI52', 'IN4MATX43'}];4;{0, 3};False 184 | IN4MATX;117;PROJ IN SFT SYS DES;[{'I&CSCI52', 'IN4MATX43'}, {'I&CSCI33', 'I&CSCI22', 'IN4MATX42', 'CSE22', 'CSE43'}];4;{2, 5};True 185 | IN4MATX;121;SOFTWARE DESIGN I;[{'CSE23', 'I&CSCI23', 'CSE43', 'CSE46', 'I&CSCI46', 'I&CSCIH23', 'I&CSCI33', 'IN4MATX45'}];4;{0, 3};True 186 | IN4MATX;122;SFT DSN:STRC & IMPL;[{'I&CSCI45J', 'IN4MATX45', 'I&CSCI46'}, {'CSE141', 'IN4MATX101', 'COMPSCI141'}];4;{1, 4};False 187 | IN4MATX;124;INTERNET APPS ENGR;[{'COMPSCI132', 'EECS148'}, {'I&CSCI45J'}];4;{2, 5};False 188 | IN4MATX;125;CMPTR GAME DEVLPMNT;[{'COMPSCI171', 'IN4MATX121', 'I&CSCI166', 'ART106B', 'I&CSCI163', 'COMPSCI112'}];4;{0, 3};False 189 | IN4MATX;131;HUMAN CMPTR INTRACT;[{'I&CSCIH21', 'EECS10', 'I&CSCI10', 'I&CSCI21', 'ENGR10', 'I&CSCI31', 'IN4MATX41', 'CSE21', 'ENGRMAE10', 'CSE41'}];4;{0, 1, 4};False 190 | IN4MATX;132;PROJ IN HCI & EVAL;[{'IN4MATX131'}];4;{2, 5};False 191 | IN4MATX;133;USER INTERACTION SW;[{'I&CSCI45J'}];4;{0, 3};False 192 | IN4MATX;134;PROJ IN USER INT SW;[{'IN4MATX131'}, {'IN4MATX133'}];4;{1};False 193 | IN4MATX;141;INFRMTION RETRIEVAL;[{'I&CSCI45J', 'I&CSCI45C'}, {'STATS7', 'STATS67'}];4;{1, 2, 4};False 194 | IN4MATX;143;INFRMTION VISUALZTN;[{'IN4MATX131', 'I&CSCI52', 'IN4MATX43'}, {'I&CSCI31', 'IN4MATX41', 'CSE21', 'I&CSCI21', 'CSE41'}];4;{2, 5};False 195 | IN4MATX;148;PROJ UBIQUITOUS CMP;[{'I&CSCI10', 'I&CSCI21', 'I&CSCI31', 'IN4MATX41', 'CSE21', 'CSE41'}];4;{2, 5};True 196 | IN4MATX;151;PROJECT MANAGEMENT;[{'I&CSCI52', 'IN4MATX43'}];4;{0, 1, 4};True 197 | IN4MATX;153;CMPTR SUP COOP WORK;[{'I&CSCI52', 'IN4MATX43', 'IN4MATX161'}, {'I&CSCI31', 'IN4MATX41', 'CSE21', 'I&CSCI21', 'CSE41'}];4;{2, 5};False 198 | IN4MATX;161;SOC ANLYS OF COMPTR;[{'I&CSCIH21', 'EECS10', 'I&CSCI10', 'I&CSCI21', 'ENGR10', 'I&CSCI31', 'IN4MATX41', 'CSE21', 'ENGRMAE10', 'CSE41'}];4;{0, 1, 3};False 199 | IN4MATX;162W;ORGNIZATNL INFO SYS;[{'IN4MATX161'}];4;{1, 4};False 200 | IN4MATX;163;PRJ IN SOC IMP COMP;[{'IN4MATX162', 'IN4MATX162W'}];4;{2, 5};False 201 | IN4MATX;171;MEDICAL INFORMATICS;[];4;{0, 3};True 202 | IN4MATX;172;PROJ HEALTH IN4MATX;[{'IN4MATX171', 'PUBHLTH105'}];4;{1, 4};False 203 | IN4MATX;191A;SENIOR DESIGN PROJ;[{'IN4MATX113'}, {'IN4MATX121'}, {'IN4MATX131'}, {'IN4MATX151'}, {'IN4MATX161'}, {'ST', 'SENI'}];4;{0, 1, 3, 4};False 204 | IN4MATX;191B;SENIOR DESIGN PROJ;[{'IN4MATX191A'}];4;{1, 2, 4, 5};False 205 | IN4MATX;203;QUAL RSCH MTHDS;[{'IN4MATX251', 'IN4MATX261'}];4;{1, 4};False 206 | IN4MATX;205;QUAN RSCH MTHDS-IS;[{'IN4MATX251', 'IN4MATX261'}];4;{2, 5};False 207 | IN4MATX;225;INFO RETRIEVAL;[{'COMPSCI161'}, {'COMPSCI171'}, {'MATH6G', 'MATH3A', 'I&CSCI6N'}];4;{1, 4};False 208 | IN4MATX;233;INTELL USER INTERFC;[{'COMPSCI171'}];4;{0};False 209 | IN4MATX;242;UBIQ COMP INTERACTN;[{'IN4MATX231'}, {'IN4MATX241'}];4;{1};False 210 | IN4MATX;244;INTR EMBED UBIQ SYS;[{'I&CSCI51'}, {'COMPSCI152'}, {'COMPSCI161'}, {'MATH6G', 'I&CSCI6D', 'MATH3A', 'I&CSCI6N'}];4;{0, 3};False 211 | IN4MATX;263;CMPTRZN,WORK, ORGS;[{'IN4MATX251', 'IN4MATX261'}];4;{5};False 212 | IN4MATX;265;CMPUTRZATN INFO/SYS;[{'IN4MATX251', 'IN4MATX261'}];4;{1};False 213 | IN4MATX;267;DIGITIAL MEDIA &SOC;[{'IN4MATX251', 'IN4MATX261'}];4;{5};False 214 | IN4MATX;283;USER EXPERIENCE;[{'IN4MATX280'}];4;{1};False 215 | IN4MATX;284;ADVANCED DESIGN;[{'IN4MATX282'}];4;{1};False 216 | IN4MATX;285;INTERACTIVE TECH;[{'IN4MATX280'}];4;{2};False 217 | IN4MATX;287;CAPSTONE PROJECT;[{'IN4MATX283'}, {'IN4MATX284'}];4;{2};False 218 | WRITING;LOW1;LOWER-DIVISION WRITING 1;[];4;{0,1,2,3,4,5};False 219 | WRITING;LOW2;LOWER-DIVISION WRITING 2;[{'WRITINGLOW1'}];4;{0,1,2,3,4,5};False 220 | GEII;-1;GEII.1;[];4;{0,1,2,3,4,5};False 221 | GEII;-2;GEII.2;[];4;{0,1,2,3,4,5};False 222 | GEII;-3;GEII.3;[];4;{0,1,2,3,4,5};False 223 | GEIII;-1;GEIII.1;[];4;{0,1,2,3,4,5};False 224 | GEIII;-2;GEIII.2;[];4;{0,1,2,3,4,5};False 225 | GEIII;-3;GEIII.2;[];4;{0,1,2,3,4,5};False 226 | GEIV;-1;GEIV.1;[];4;{0,1,2,3,4,5};False 227 | GEIV;-2;GEIV.2;[];4;{0,1,2,3,4,5};False 228 | GEIV;-3;GEIV.3;[];4;{0,1,2,3,4,5};False 229 | GEV;a;GEV.a;[];4;{0,1,2,3,4,5};False 230 | GEV;b;GEV.b;[];4;{0,1,2,3,4,5};False 231 | GEVI;-1;GEVI;[];4;{0,1,2,3,4,5};False 232 | GEVII;-1;GEVII;[];4;{0,1,2,3,4,5};False 233 | GEVIII;-1;GEVIII;[];4;{0,1,2,3,4,5};False 234 | HISTORY;40A;COL AM:NEW WORLDS;[];4;{0,3};False 235 | HISTORY;40B;19C US:CRISIS&EXPAN;[];4;{1,4};False 236 | HISTORY;40C;MOD AM CLTR&POWER;[];4;{2,5};False 237 | POLSCI;21A;INTRO AMERICAN GOVT;[];4;{0,1,2,3,4,5};False -------------------------------------------------------------------------------- /info/fullcourses_new.txt: -------------------------------------------------------------------------------- 1 | I&CSCI;90;NEW STUDENTS SEMINR;[];1;{0,3};False 2 | I&CSCI;6B;BOOLEAN ALG & LOGIC;[];4;{0, 1, 2, 3, 4, 5};False 3 | I&CSCI;6D;DISCRET MATH FOR CS;[{'I&CSCI6B'}];4;{0, 1, 2, 3, 4, 5};False 4 | I&CSCI;6N;COMP LINEAR ALGEBRA;[];4;{0, 1, 3, 4, 5};False 5 | I&CSCI;10;HOW COMPUTERS WORK;[];4;{2, 3};False 6 | I&CSCI;31;INTRO TO PROGRMMING;[];4;{0, 1, 2, 3, 4, 5};False 7 | I&CSCI;32;PROG SOFTWARE LIBR;[{'CSE41', 'I&CSCI31'}];4;{0, 1, 2, 3, 4, 5};False 8 | I&CSCI;33;INTERMEDIATE PRGRMG;[{'I&CSCI32', 'CSE42'}];4;{0, 1, 2, 3, 4, 5};False 9 | I&CSCI;45C;PROGRAM IN C/C++;[{'EECS40', 'IN4MATX42', 'CSE22', 'I&CSCI33', 'I&CSCI22', 'CSE43'}];4;{0, 1, 2, 3, 4, 5};False 10 | I&CSCI;45J;PROGRAMMING IN JAVA;[{'CSE43', 'I&CSCI33'}];4;{0, 2, 3, 4};False 11 | I&CSCI;46;DATA STRC IMPL&ANLS;[{'I&CSCI45C', 'CSE45C'}];4;{0, 1, 2, 3, 4, 5};False 12 | I&CSCI;51;INTRO COMPUTER ORG;[{'IN4MATX42', 'I&CSCI21', 'CSE21', 'I&CSCI31', 'CSE41', 'I&CSCIH21'}, {'I&CSCI6B'}];6;{0, 1, 2, 3, 4, 5};False 13 | I&CSCI;53+53L;PRINCP IN SYS DESGN;[{'I&CSCI51'}];6;{1, 2, 4, 5};False 14 | I&CSCI;60;CMP GAMES & SOCIETY;[];4;{0, 3,5};False 15 | I&CSCI;62;GAME TECH&INT MEDIA;[{'IN4MATX42', 'I&CSCI21', 'CSE21', 'I&CSCI31', 'CSE41', 'I&CSCIH21'}];4;{2,5};False 16 | I&CSCI;139W;CRITICAL WRITING;[{'WRITINGLOW2'}];4;{0, 1, 2, 3, 4, 5};True 17 | I&CSCI;161;DES&ANALYS OF ALGOR;[{'I&CSCI45C', 'I&CSCI65'}];4;{1, 4};False 18 | I&CSCI;162;MODELNG &WORLD BLDG;[{'COMPSCI112'}];4;{1, 4};False 19 | I&CSCI;163;MOBILE & UBI GAMES;[{'I&CSCI61'}, {'I&CSCI21', 'CSE21', 'I&CSCI31', 'I&CSCI10', 'CSE41', 'I&CSCIH21', 'IN4MATX41'}];4;{0, 2, 3};False 20 | I&CSCI;166;GAME DESIGN;[{'I&CSCI61'}, {'I&CSCI52', 'IN4MATX43'}];4;{1,2,5};False 21 | I&CSCI;167;MULTIPLAYER SYSTEMS;[{'I&CSCI51'}];4;{1, 4, 5};False 22 | I&CSCI;168;COMP&NETWRK SECURTY;[{'I&CSCI52', 'IN4MATX43'}, {'I&CSCI167'}];4;{2,5};False 23 | I&CSCI;169A;CAPSTONE GAME I;[{'I&CSCI168'}];4;{0, 3};False 24 | I&CSCI;169B;CAPSTONE GAME II;[{'I&CSCI169A'}];4;{1, 4};False 25 | COMPSCI;111;DIGITAL IMAGE PROC;[{'CSE46', 'I&CSCI23', 'CSE23', 'I&CSCI46', 'I&CSCIH23'}, {'I&CSCI6D'}, {'I&CSCI6N', 'MATH6G', 'MATH3A'}];4;{2,3};False 26 | COMPSCI;112;COMPUTER GRAPHICS;[{'CSE22', 'I&CSCI33', 'I&CSCI22', 'I&CSCIH22', 'CSE43'}, {'I&CSCI45C', 'CSE45C'}, {'I&CSCI6N', 'MATH6G', 'MATH3A'}];4;{1, 3};False 27 | COMPSCI;113;CMPTR GAME DEVLPMNT;[{'IN4MATX121', 'COMPSCI112', 'I&CSCI163', 'I&CSCI166', 'COMPSCI171', 'ART106B'}];4;{0, 3};False 28 | COMPSCI;114;PROJ IN ADV GRAPHIC;[{'COMPSCI112'}, {'I&CSCI45C', 'CSE45C'}, {'COMPSCI161'}, {'CSE161'}, {'COMPSCI164'}, {'COMPSCI165'}];4;{2,5};False 29 | COMPSCI;115;COMPUTER SIMULATION;[{'I&CSCI6B'}, {'I&CSCI6N', 'MATH6G'}, {'STATS7', 'STATS67'}, {'STATS67', 'STATS120A'}, {'I&CSCI51'}, {'IN4MATX43'}];4;{0, 4};True 30 | COMPSCI;116;COMP PHOTO & VISION;[{'I&CSCI6D'}, {'I&CSCI6N', 'MATH6G', 'MATH3A'}, {'MATH2B'}, {'CSE46', 'I&CSCI23', 'CSE23', 'I&CSCI46', 'I&CSCIH23'}];4;{1, 4};False 31 | COMPSCI;117;PROJ IN COMP VISION;[{'I&CSCI6D'}, {'MATH3A', 'MATH6G', 'I&CSCI6N'}, {'MATH2B'}, {'CSE46', 'I&CSCI23', 'CSE23', 'I&CSCI46', 'I&CSCIH23'}, {'COMPSCI116', 'COMPSCI171', 'COMPSCI178', 'COMPSCI112'}];4;{2,5};False 32 | COMPSCI;121;INFRMTION RETRIEVAL;[{'I&CSCI45C', 'I&CSCI45J', 'CSE45C'}, {'STATS7', 'STATS67'}];4;{1,2, 4, 5};False 33 | COMPSCI;122A;INTRO TO DATA MGMT;[{'EECS114', 'CSE43', 'I&CSCI33'}];4;{0, 1, 2, 3, 4, 5};False 34 | COMPSCI;122B;PROJ DATA&WEB APPS;[{'EECS116', 'COMPSCI122A'}, {'I&CSCI45J'}];4;{1, 2, 4, 5};False 35 | COMPSCI;122C;PRINCIPLS DATA MGMT;[{'COMPSCI122A'}, {'COMPSCI143A'}, {'COMPSCI152'}];4;{0, 3};False 36 | COMPSCI;125;NXT GEN SRCH SYSTMS;[{'I&CSCI21', 'CSE21', 'I&CSCI31', 'CSE41', 'IN4MATX41'}];4;{1, 4};True 37 | COMPSCI;131;PARLLEL DIST CMPTNG;[{'COMPSCI143A', 'I&CSCI53+53L'}, {'I&CSCI53L', 'COMPSCI143A'}];4;{2, 4};False 38 | COMPSCI;132;COMPUTER NETWORKS;[{'EECS55', 'STATS67'}];4;{0,1, 2, 3,5};False 39 | COMPSCI;133;ADV COMPUTER NETWKS;[{'COMPSCI132'}];4;{1, 4};False 40 | COMPSCI;134;COMP&NETWRK SECURTY;[{'I&CSCI6D'}, {'IN4MATX42', 'CSE22', 'I&CSCI33', 'I&CSCI22', 'I&CSCIH22', 'CSE43'}, {'COMPSCI143A', 'COMPSCI132', 'COMPSCI122A', 'EECS116', 'CSE104'}];4;{1, 3};False 41 | COMPSCI;137;INTERNET APPS ENGR;[{'EECS148', 'COMPSCI132'}, {'I&CSCI45J'}];4;{2,5};False 42 | COMPSCI;141;CONCEPT PGMG LANG I;[{'I&CSCI51', 'EECS31', 'CSE31'}, {'CSE46', 'I&CSCI46'}];4;{0,1, 3};False 43 | COMPSCI;142A;COMPILERS&INTPRETER;[{'CSE141', 'IN4MATX101', 'COMPSCI141'}];4;{1, 4};False 44 | COMPSCI;142B;LANG PROC CONSTRCTN;[{'COMPSCI142A'},{'CSE142'}];4;{1, 4};False 45 | COMPSCI;143A;PRNCPLS OPERTNG SYS;[{'CSE46', 'I&CSCI23', 'CSE23', 'I&CSCI46', 'I&CSCIH23'}, {'CSE31', 'I&CSCI51', 'EECS31'}];4;{0, 2, 3, 5};False 46 | COMPSCI;143B;PROJ IN OPERTNG SYS;[{'COMPSCI143A', 'CSE104'}];4;{0, 1, 4};False 47 | COMPSCI;145+145L;EMBEDDED SOFTWARE;[{'CSE46', 'I&CSCI46'}, {'CSE132', 'EECS112', 'I&CSCI51'}];4;{1,2,5};False 48 | COMPSCI;146;MULTITASK OPER SYS;[{'CSE46', 'I&CSCI23', 'CSE23', 'I&CSCI46', 'I&CSCIH23'}, {'I&CSCI51'}, {'COMPSCI143A'}];4;{2,5};False 49 | COMPSCI;151;DIGITAL LOG DESIGN;[{'CSE46', 'I&CSCI23', 'CSE23', 'I&CSCI46', 'I&CSCI33', 'I&CSCIH23', 'CSE43'}, {'I&CSCI51'}, {'I&CSCI6B'}, {'I&CSCI6D'}];4;{0, 3};False 50 | COMPSCI;152;COMPUTR SYST ARCHIT;[{'COMPSCI151'}];4;{1, 4};False 51 | COMPSCI;153;LOGIC DESIGN LAB;[{'COMPSCI151'}];4;{1};False 52 | COMPSCI;154;COMPUTER DESIGN LAB;[{'COMPSCI151'}];4;{1,5};False 53 | COMPSCI;161;DES&ANALYS OF ALGOR;[{'CSE46', 'I&CSCI23', 'CSE23', 'I&CSCI46', 'I&CSCIH23'}, {'I&CSCI6B'}, {'I&CSCI6D'}, {'MATH2B'}];4;{0, 1, 2, 3, 4};False 54 | COMPSCI;162;FORMAL LANG & AUTM;[{'CSE46', 'I&CSCI23', 'CSE23', 'I&CSCI46', 'I&CSCIH23'}, {'MATH2A'}, {'MATH2B'}, {'I&CSCI6B'}, {'I&CSCI6D'}];4;{1, 4};False 55 | COMPSCI;163;GRAPH ALGORITHMS;[{'CSE161', 'COMPSCI161'}];4;{2, 5};False 56 | COMPSCI;164;COMPUTATIONAL GEO;[{'CSE46', 'I&CSCI23', 'CSE23', 'I&CSCI46', 'I&CSCIH23'}];4;{1};False 57 | COMPSCI;165;PROJ ALG & DATA STR;[{'CSE161', 'COMPSCI161'}, {'I&CSCI45C'}];4;{2};False 58 | COMPSCI;167;APPLIED CRYPTO;[{'CSE161', 'COMPSCI161'}];4;{1,4};True 59 | COMPSCI;169;INTRO OPTIMIZATION;[{'I&CSCI6N', 'MATH6G', 'MATH3A'}, {'STATS67'}];4;{3};False 60 | COMPSCI;171;INTRO ARTIFCL INTEL;[{'STATS67'}, {'STATS7', 'STATS67'}, {'STATS67', 'STATS120A'}, {'CSE46', 'I&CSCI46'}, {'MATH2B'}];4;{0, 1, 3, 4};False 61 | COMPSCI;172B;NRL NTWKS&DEEP LRNG;[{'MATH121A', 'COMPSCI178', 'COMPSCI273A', 'STATS120A'}, {'STATS120B', 'COMPSCI178', 'COMPSCI273A', 'MATH121A'}];4;{1,4};False 62 | COMPSCI;175;PROJECT IN AI;[{'COMPSCI171'}, {'COMPSCI178'}];4;{1,2, 4, 5};False 63 | COMPSCI;177;APP OF PROB IN CS;[{'MATH2B'}, {'STATS67'}, {'I&CSCI6B'}, {'I&CSCI6D'}, {'I&CSCI6N', 'MATH6G', 'MATH3A'}];4;{0,5};False 64 | COMPSCI;178;MACHINE/DATA MINING;[{'I&CSCI6B'}, {'I&CSCI6D'}, {'I&CSCI6N', 'MATH6G', 'MATH3A'}, {'MATH2B'}, {'STATS7', 'STATS67'}, {'STATS67', 'STATS120A'}];4;{0, 1, 4};False 65 | COMPSCI;183;INTRO COMP BIOLOGY;[{'STATS7', 'STATS8', 'MATH2D', 'MATH2J'}];4;{0};False 66 | COMPSCI;184A;ALGRTHMS FOR MOLBIO;[{'I&CSCI6N', 'MATH6G', 'MATH3A'}];4;{ 3};False 67 | COMPSCI;184C;COMPUTATNL SYS BIO;[{'COMPSCI184A'}];4;{5};False 68 | MATH;1B;PRE-CALCULUS;[];4;{0, 1, 3, 4, 5};False 69 | MATH;2A;CALCULUS;[{'MATH1B'}];4;{0, 1, 2, 3, 4, 5};False 70 | MATH;2B;CALCULUS;[{'MATH2A', 'MATH5A'}];4;{0, 1, 2, 3, 4, 5};False 71 | MATH;2D;MULTIVAR CALCULUS;[{'MATH2B', 'MATH5B'}];4;{0, 1, 2, 3, 4, 5};False 72 | MATH;2E;MULTIVAR CALCULUS;[{'MATHH2D', 'MATH2D'}];4;{0, 1, 2, 3, 4, 5};False 73 | MATH;3A;INTRO LINEAR ALGBRA;[{'MATH2B', 'MATH5B'}];4;{0, 1, 2, 3, 4, 5};False 74 | MATH;3D;ELEM DIFF EQUATIONS;[{'MATH2B'}, {'MATHH2D', 'MATH2D'}, {'MATH3A', 'MATH2J'}];4;{0, 1, 2, 3, 4, 5};False 75 | MATH;4;MATH FOR ECONOMISTS;[{'MATH2B', 'MATH5B'}];4;{0, 1, 2, 3, 4, 5};False 76 | MATH;5A;CALCULUS LIFE SCIEN;[{'MATH1B'}];4;{0, 1, 2, 3, 4, 5};False 77 | MATH;5B;CALCULUS LIFE SCIEN;[{'MATH2A', 'MATH5A'}];4;{0, 1, 2, 3, 4, 5};False 78 | MATH;8;FUNCTIONS &MODELING;[{'MATH2A'}];4;{2};False 79 | MATH;9;INTRO PROG NUM ANLY;[{'MATH2A'}];4;{0, 1, 2, 3, 4, 5};False 80 | MATH;13;INTRO ABSTRACT MATH;[{'MATH2A', 'I&CSCI6D'}];4;{0, 1, 2, 3, 4, 5};False 81 | MATH;105A;NUMERICAL ANALYSIS;[{'MATH6G', 'MATH3A'}];4;{0, 3};False 82 | MATH;105B;NUMERICAL ANALYSIS;[{'MATH105A'}];4;{1, 4, 5};False 83 | MATH;107;NUMERICAL DIFF EQNS;[{'MATH3D'}, {'MATH105B'}];4;{2};False 84 | MATH;112A;INT PARTIAL DIF EQU;[{'MATH2E'}, {'MATH3D'}];4;{0, 3};False 85 | MATH;112B;INT PARTIAL DIF EQU;[{'MATH2E'}, {'MATH112A'}];4;{1, 4, 5};False 86 | MATH;112C;INT PARTIAL DIF EQU;[{'MATH112B'}];4;{2};False 87 | MATH;113A;MATH MODELNG IN BIO;[{'MATH2B', 'MATH5B'}];4;{0, 3};False 88 | MATH;113B;MATH MODELNG IN BIO;[{'MATH113A'}];4;{1, 4, 5};False 89 | MATH;115;MATH MODELING;[{'MATH6C', 'MATH6G', 'MATH3A'}, {'MATH3D'}, {'MATH112A', 'ENGRMAE140'}];4;{2};False 90 | MATH;117;DYNAMICAL SYSTEMS;[{'MATH3D'}, {'MATH140A'}];4;{1, 4, 5};False 91 | MATH;118;THRY DIFF EQUATIONS;[{'MATH3D'}, {'MATH140A'}];4;{0, 3};False 92 | MATH;120A;INTRO GROUP THEORY;[{'MATH6G', 'MATH3A'}, {'MATH13'}];4;{0, 1, 3, 4, 5};False 93 | MATH;120B;INTRO RINGS/FIELDS;[{'MATH120A'}];4;{0, 1, 2, 4, 5};False 94 | MATH;120C;INTRO GALOIS THEORY;[{'MATH120B'}];4;{2};False 95 | MATH;H120A;HON GRAD ALGBR I;[{'MATH3A'}];5;{0, 3};False 96 | MATH;H120B;HON GRAD ALGBR II;[{'MATHH120A'}];5;{1, 4, 5};False 97 | MATH;121A;LINEAR ALGEBRA;[{'MATH6G', 'MATH3A'}, {'MATH13'}];4;{0, 1, 2, 3, 4, 5};False 98 | MATH;121B;LINEAR ALGEBRA;[{'MATH121A'}];4;{1, 2, 4, 5};False 99 | MATH;130A;PROB&STOCHASTC PROC;[{'MATH2A'}, {'MATH2B'}, {'MATH6G', 'MATH3A'}];4;{0, 1, 3, 4, 5};False 100 | MATH;130B;PROB&STOCH PROCESS;[{'MATH130A', 'MATH131A', 'STATS120A'}];4;{0, 1, 4, 5};False 101 | MATH;130C;PROB & STOCH PROC;[{'MATH130B'}];4;{2};False 102 | MATH;133A;STAT MTHD FINANCE I;[{'MATH130A', 'MATH131A', 'STATS120A'}];4;{0, 1, 4, 5};False 103 | MATH;133B;STAT MTHD FNANCE II;[{'MATH133A'}];4;{2};False 104 | MATH;140A;ELEMENTARY ANALYSIS;[{'MATH2D'}, {'MATH3A'}, {'MATH13'}];4;{0, 1, 3, 4, 5};False 105 | MATH;140B;ELEMENTARY ANALYSIS;[{'MATH140A'}];4;{0, 1, 2, 4, 5};False 106 | MATH;140C;ANALY SEV VARIABLES;[{'MATH140B'}];4;{2};False 107 | MATH;H140A;HON GRAD ANLYS I;[{'MATH2E'}, {'MATH3A'}, {'MATH13'}];5;{0, 3};False 108 | MATH;H140B;HON GRAD ANLYS II;[{'MATHH140A'}];5;{1, 4, 5};False 109 | MATH;141;INTRO TOPOLOGY;[{'MATH140A'}];4;{2};False 110 | MATH;147;COMPLEX ANALYSIS;[{'MATH140A'}];4;{1, 4, 5};False 111 | MATH;150;INTRO TO MATH LOGIC;[{'I&CSCI6B', 'MATH13', 'MATH6B'}, {'MATH6D', 'MATH13', 'I&CSCI6D'}];4;{0, 3};False 112 | MATH;161;MODERN GEOMETRY;[{'I&CSCI6B', 'MATH13'}, {'MATH13', 'I&CSCI6D'}];4;{1, 2, 4, 5};False 113 | MATH;162A;INTRO DIFFRNTL GEOM;[{'MATH2E'}, {'MATH3A'}, {'MATH3D'}];4;{1, 4, 5};False 114 | MATH;173A;INTRO CRYPTOLOGY;[{'MATH2B'}, {'MATH6G', 'MATH3A'}, {'I&CSCI6B', 'MATH13'}, {'MATH13', 'I&CSCI6D'}];4;{0, 3};False 115 | MATH;175;COMBINATORICS;[{'MATH2B'}, {'MATH13'}];4;{1, 4, 5};False 116 | MATH;176;MATH OF FINANCE;[{'MATH3A'}];4;{1, 4, 5};False 117 | MATH;180A;NUMBER THEORY I;[{'MATH3A'}, {'MATH13'}];4;{1, 4, 5};False 118 | MATH;180B;NUMBER THEORY II;[{'MATH180A'}];4;{2};False 119 | MATH;184;HISTORY OF MATH;[{'MATH3D'}, {'MATH120A'}, {'MATH140A'}];4;{2};False 120 | STATS;7;BASIC STATISTICS;[];4;{0, 1, 2, 3, 4, 5};False 121 | STATS;8;INTRO TO BIO STATS;[];4;{0, 1, 2, 3, 4, 5};False 122 | STATS;67;INTRO PROB&STAT/CS;[{'MATH2B'}];4;{0, 1, 2, 3, 4, 5};False 123 | STATS;110;STATS METH DATA I;[{'STATS7', 'MATH131A', 'STATS8', 'STATS120A'}, {'STATS120B', 'STATS7', 'STATS8', 'MATH131B'}, {'STATS7', 'MATH131C', 'STATS8', 'STATS120C'}];4;{0, 3};False 124 | STATS;111;STATS METH DATA II;[{'STATS110'}];4;{1, 4, 5};False 125 | STATS;112;STATS METH DATA III;[{'STATS111'}];4;{2};False 126 | STATS;115;BAYES DATA ANALYSIS;[{'STATS120C'}, {'STATS110'}];4;{4, 5};False 127 | STATS;120A;INTRO PROB & STATS;[{'MATH2A'}, {'MATH2B'}, {'MATH4', 'MATH2D'}];4;{0, 3};False 128 | STATS;120B;INTRO PROB & STATS;[{'STATS120A'}];4;{1, 4, 5};False 129 | STATS;120C;INTRO PROB & STATS;[{'STATS120B'}, {'I&CSCI6N', 'MATH6G', 'MATH3A'}];4;{2};False 130 | STATS;140;MULTIVAR STAT METH;[{'STATS120C'}, {'I&CSCI6N', 'MATH3A'}];4;{3};False 131 | STATS;200A;INT PROB & STAT THY;[{'STATS120C'}];4;{0, 3};False 132 | STATS;200B;INT PROB & STAT THY;[{'STATS200A'}];4;{1, 4, 5};False 133 | STATS;200C;INT PROB & STAT THY;[{'STATS200B'}];4;{2};False 134 | STATS;201;STATS METH DATA I;[{'STATS7', 'STATS8'}];4;{0, 3};False 135 | STATS;202;STATS METH DATA II;[{'STATS210', 'STATS201'}];4;{1, 4, 5};False 136 | STATS;203;STATS METH DATA III;[{'STATS202'}];4;{2};False 137 | STATS;205;BAYES DATA ANALYSIS;[{'STATS120C'}, {'STATS201'}, {'STATS210'}];4;{4, 5};False 138 | STATS;211;STATS METHOD II;[{'STATS210'}];4;{1, 4, 5};False 139 | STATS;212;STATS METHODS III;[{'STATS211'}];4;{2};False 140 | STATS;220A;ADV PROB&STATTOPICS;[{'STATS200C'}];4;{4, 5};False 141 | STATS;220B;ADV PROB&STATTOPICS;[{'STATS220A'}, {'MATH140B'}];4;{2};False 142 | STATS;225;BAYESIAN STATISTICS;[{'STATS205'}, {'STATS230'}];4;{2};False 143 | STATS;235;MOD DATA ANLYS METH;[{'STATS120C'}, {'STATS205'}, {'STATS210', 'STATS201'}];4;{0, 2};False 144 | STATS;240;MULTIVAR STAT METH;[{'STATS120C'}, {'I&CSCI6N', 'MATH3A'}];4;{3};False 145 | STATS;245;TIME SERIES ANALYS;[{'STATS200C'}, {'STATS210', 'STATS201'}];4;{4, 5};False 146 | STATS;275;STAT CONSULTING;[{'STATS212', 'STATS203'}];4;{0, 1, 3};False 147 | IN4MATX;43;INTRO SOFTWARE ENGR;[{'I&CSCI32', 'CSE42'}];4;{0, 2, 3};False 148 | IN4MATX;101;CONCEPT PGMG LANG I;[{'I&CSCI51', 'EECS31', 'CSE31'}, {'CSE46', 'I&CSCI46'}];4;{0, 3};False 149 | IN4MATX;102;CONCPT PGMG LANG II;[{'CSE141', 'IN4MATX101', 'COMPSCI141'}];4;{2};False 150 | IN4MATX;113;REQT ANALYSIS & ENG;[{'IN4MATX42', 'CSE22', 'I&CSCI33', 'I&CSCI22', 'CSE43'}, {'I&CSCI52', 'IN4MATX43'}];4;{1, 4, 5};False 151 | IN4MATX;115;SW TEST&QUAL ASSUR;[{'I&CSCI45J', 'I&CSCI65', 'I&CSCI45C', 'I&CSCI23', 'CSE46', 'IN4MATX45', 'CSE23', 'I&CSCI46', 'CSE45C'}, {'I&CSCI52', 'IN4MATX43'}];4;{0, 3};False 152 | IN4MATX;117;PROJ IN SFT SYS DES;[{'I&CSCI52', 'IN4MATX43'}, {'IN4MATX42', 'CSE22', 'I&CSCI33', 'I&CSCI22', 'CSE43'}];4;{0, 2};True 153 | IN4MATX;121;SOFTWARE DESIGN I;[{'IN4MATX45', 'CSE46', 'I&CSCI23', 'CSE23', 'I&CSCI46', 'I&CSCI33', 'I&CSCIH23', 'CSE43'}];4;{0, 3};True 154 | IN4MATX;122;SFT DSN:STRC & IMPL;[{'I&CSCI45J', 'IN4MATX45', 'I&CSCI46'}, {'CSE141', 'IN4MATX101', 'COMPSCI141'}];4;{1, 4, 5};False 155 | IN4MATX;124;INTERNET APPS ENGR;[{'EECS148', 'COMPSCI132'}, {'I&CSCI45J'}];4;{2};False 156 | IN4MATX;125;CMPTR GAME DEVLPMNT;[{'IN4MATX121', 'COMPSCI112', 'I&CSCI163', 'I&CSCI166', 'COMPSCI171', 'ART106B'}];4;{0, 3};False 157 | IN4MATX;131;HUMAN CMPTR INTRACT;[{'ENGR10', 'I&CSCI21', 'CSE21', 'I&CSCI31', 'CSE41', 'I&CSCI10', 'ENGRMAE10', 'IN4MATX41', 'I&CSCIH21', 'EECS10'}];4;{0, 1, 3, 4, 5};False 158 | IN4MATX;132;PROJ IN HCI & EVAL;[{'IN4MATX131'}];4;{2};False 159 | IN4MATX;133;USER INTERACTION SW;[{'I&CSCI45J'}];4;{0, 3};False 160 | IN4MATX;134;PROJ IN USER INT SW;[{'IN4MATX131'}, {'IN4MATX133'}];4;{4, 5};False 161 | IN4MATX;141;INFRMTION RETRIEVAL;[{'I&CSCI45C', 'I&CSCI45J'}, {'STATS7', 'STATS67'}];4;{1, 4, 5};False 162 | IN4MATX;143;INFRMTION VISUALZTN;[{'I&CSCI52', 'IN4MATX43', 'IN4MATX131'}, {'I&CSCI21', 'CSE21', 'I&CSCI31', 'CSE41', 'IN4MATX41'}];4;{2};False 163 | IN4MATX;148;PROJ UBIQUITOUS CMP;[{'I&CSCI21', 'CSE21', 'I&CSCI31', 'I&CSCI10', 'CSE41', 'IN4MATX41'}];4;{2};True 164 | IN4MATX;151;PROJECT MANAGEMENT;[{'I&CSCI52', 'IN4MATX43'}];4;{0, 1, 3, 4, 5};True 165 | IN4MATX;153;CMPTR SUP COOP WORK;[{'I&CSCI52', 'IN4MATX161', 'IN4MATX43'}, {'I&CSCI21', 'CSE21', 'I&CSCI31', 'CSE41', 'IN4MATX41'}];4;{2};False 166 | IN4MATX;161;SOC ANLYS OF COMPTR;[{'ENGR10', 'I&CSCI21', 'CSE21', 'I&CSCI31', 'CSE41', 'I&CSCI10', 'ENGRMAE10', 'IN4MATX41', 'I&CSCIH21', 'EECS10'}];4;{0, 3, 4, 5};False 167 | IN4MATX;162W;ORGNIZATNL INFO SYS;[{'IN4MATX161'}];4;{1, 4, 5};False 168 | IN4MATX;163;PRJ IN SOC IMP COMP;[{'IN4MATX162', 'IN4MATX162W'}];4;{2};False 169 | IN4MATX;171;MEDICAL INFORMATICS;[];4;{3};True 170 | IN4MATX;172;PROJ HEALTH IN4MATX;[{'IN4MATX171', 'PUBHLTH105'}];4;{1, 4, 5};False 171 | IN4MATX;191A;SENIOR DESIGN PROJ;[{'IN4MATX113'}, {'IN4MATX121'}, {'IN4MATX131'}, {'IN4MATX151'}, {'IN4MATX161'}, {'SENI', 'ST'}];4;{0, 1, 3, 4, 5};False 172 | IN4MATX;191B;SENIOR DESIGN PROJ;[{'IN4MATX191A'}];4;{1, 2, 4, 5};False 173 | IN4MATX;203;QUAL RSCH MTHDS;[{'IN4MATX251', 'IN4MATX261'}];4;{1, 4, 5};False 174 | IN4MATX;205;QUAN RSCH MTHDS-IS;[{'IN4MATX251', 'IN4MATX261'}];4;{2};False 175 | IN4MATX;225;INFO RETRIEVAL;[{'COMPSCI161'}, {'COMPSCI171'}, {'I&CSCI6N', 'MATH6G', 'MATH3A'}];4;{1, 4, 5};False 176 | IN4MATX;233;INTELL USER INTERFC;[{'COMPSCI171'}];4;{3};False 177 | IN4MATX;242;UBIQ COMP INTERACTN;[{'IN4MATX231'}, {'IN4MATX241'}];4;{4, 5};False 178 | IN4MATX;244;INTR EMBED UBIQ SYS;[{'I&CSCI51'}, {'COMPSCI152'}, {'COMPSCI161'}, {'I&CSCI6N', 'MATH6G', 'MATH3A', 'I&CSCI6D'}];4;{0, 3};False 179 | IN4MATX;263;CMPTRZN,WORK, ORGS;[{'IN4MATX251', 'IN4MATX261'}];4;{2};False 180 | IN4MATX;265;CMPUTRZATN INFO/SYS;[{'IN4MATX251', 'IN4MATX261'}];4;{4, 5};False 181 | IN4MATX;267;DIGITIAL MEDIA &SOC;[{'IN4MATX251', 'IN4MATX261'}];4;{2};False 182 | IN4MATX;283;USER EXPERIENCE;[{'IN4MATX280'}];4;{4, 5};False 183 | IN4MATX;284;ADVANCED DESIGN;[{'IN4MATX282'}];4;{4, 5};False 184 | WRITING;LOW1;LOWER-DIVISION WRITING 1;[];4;{0,1,2,3,4,5};False 185 | WRITING;LOW2;LOWER-DIVISION WRITING 2;[{'WRITINGLOW1'}];4;{0,1,2,3,4,5};False 186 | GEII;-1;GEII.1;[];4;{0,1,2,3,4,5};False 187 | GEII;-2;GEII.2;[];4;{0,1,2,3,4,5};False 188 | GEII;-3;GEII.3;[];4;{0,1,2,3,4,5};False 189 | GEIII;-1;GEIII.1;[];4;{0,1,2,3,4,5};False 190 | GEIII;-2;GEIII.2;[];4;{0,1,2,3,4,5};False 191 | GEIII;-3;GEIII.2;[];4;{0,1,2,3,4,5};False 192 | GEIV;-1;GEIV.1;[];4;{0,1,2,3,4,5};False 193 | GEIV;-2;GEIV.2;[];4;{0,1,2,3,4,5};False 194 | GEIV;-3;GEIV.3;[];4;{0,1,2,3,4,5};False 195 | GEV;a;GEV.a;[];4;{0,1,2,3,4,5};False 196 | GEV;b;GEV.b;[];4;{0,1,2,3,4,5};False 197 | GEVI;-1;GEVI;[];4;{0,1,2,3,4,5};False 198 | GEVII;-1;GEVII;[];4;{0,1,2,3,4,5};False 199 | GEVIII;-1;GEVIII;[];4;{0,1,2,3,4,5};False 200 | HISTORY;40A;COL AM:NEW WORLDS;[];4;{0,3};False 201 | HISTORY;40B;19C US:CRISIS&EXPAN;[];4;{1,4};False 202 | HISTORY;40C;MOD AM CLTR&POWER;[];4;{2,5};False 203 | POLSCI;21A;INTRO AMERICAN GOVT;[];4;{0,1,2,3,4,5};False -------------------------------------------------------------------------------- /info/specializations.txt: -------------------------------------------------------------------------------- 1 | University 2 | 1 3 | { 4 | HISTORY 40A 5 | HISTORY 40B 6 | HISTORY 40C 7 | } 8 | 1 9 | { 10 | POLSCI 21A 11 | } 12 | ; 13 | GEI 14 | 2 15 | { 16 | WRITING LOW1 17 | WRITING LOW2 18 | } 19 | ; 20 | GEII 21 | 3 22 | { 23 | GEII -1 24 | GEII -2 25 | I&CSCI 31 26 | I&CSCI 51 27 | } 28 | ; 29 | GEIII 30 | 3 31 | { 32 | POLSCI 21A 33 | GEIII -1 34 | GEIII -2 35 | GEIII -3 36 | } 37 | ; 38 | GEIV 39 | 3 40 | { 41 | HISTORY 40A 42 | HISTORY 40B 43 | HISTORY 40C 44 | GEIV -1 45 | GEIV -2 46 | GEIV -3 47 | } 48 | ; 49 | GEV 50 | 2 51 | { 52 | I&CSCI 32 53 | STATS 67 54 | GEV -Va1 55 | } 56 | 1 57 | { 58 | I&CSCI 6B 59 | GEV -Vb1 60 | } 61 | ; 62 | GEVI 63 | 1 64 | { 65 | GEVI -1 66 | } 67 | ; 68 | GEVII 69 | 1 70 | { 71 | GEVII -1 72 | } 73 | ; 74 | GEVIII 75 | 1 76 | { 77 | GEVIII -1 78 | } 79 | ; 80 | CS-Lower-division 81 | all 82 | { 83 | I&CSCI 31 84 | I&CSCI 32 85 | I&CSCI 33 86 | I&CSCI 45C 87 | I&CSCI 46 88 | I&CSCI 51 89 | I&CSCI 53+53L 90 | I&CSCI 90 91 | IN4MATX 43 92 | MATH 2A 93 | MATH 2B 94 | I&CSCI 6B 95 | I&CSCI 6D 96 | STATS 67 97 | GEII -1 98 | GEII -2 99 | } 100 | 1 101 | { 102 | I&CSCI 6N 103 | MATH 3A 104 | } 105 | ; 106 | CS-Upper-division 107 | 2 108 | { 109 | COMPSCI 161 110 | I&CSCI 139W 111 | } 112 | 9 113 | { 114 | COMPSCI 111 115 | COMPSCI 112 116 | COMPSCI 113 117 | COMPSCI 114 118 | COMPSCI 115 119 | COMPSCI 117 120 | COMPSCI 116 121 | COMPSCI 121 122 | COMPSCI 122A 123 | COMPSCI 122B 124 | COMPSCI 122C 125 | COMPSCI 125 126 | COMPSCI 131 127 | COMPSCI 132 128 | COMPSCI 133 129 | COMPSCI 134 130 | COMPSCI 137 131 | COMPSCI 141 132 | COMPSCI 142A 133 | COMPSCI 142B 134 | COMPSCI 143A 135 | COMPSCI 143B 136 | COMPSCI 144 137 | COMPSCI 145+145L 138 | COMPSCI 146 139 | COMPSCI 151 140 | COMPSCI 152 141 | COMPSCI 153 142 | COMPSCI 154 143 | COMPSCI 162 144 | COMPSCI 163 145 | COMPSCI 164 146 | COMPSCI 165 147 | COMPSCI 167 148 | COMPSCI 169 149 | COMPSCI 171 150 | COMPSCI 172B 151 | COMPSCI 174 152 | COMPSCI 175 153 | COMPSCI 177 154 | COMPSCI 178 155 | COMPSCI 179 156 | COMPSCI 183 157 | COMPSCI 184A 158 | COMPSCI 184B 159 | COMPSCI 184C 160 | COMPSCI 189 161 | IN4MATX 102 162 | IN4MATX 113 163 | IN4MATX 115 164 | IN4MATX 121 165 | IN4MATX 122 166 | IN4MATX 124 167 | IN4MATX 131 168 | IN4MATX 133 169 | IN4MATX 134 170 | I&CSCI 160 171 | I&CSCI 161 172 | I&CSCI 162 173 | } 174 | 2 175 | { 176 | COMPSCI 113 177 | COMPSCI 114 178 | COMPSCI 117 179 | COMPSCI 122B 180 | COMPSCI 122C 181 | COMPSCI 133 182 | COMPSCI 142B 183 | COMPSCI 143B 184 | COMPSCI 145+145L 185 | COMPSCI 153 186 | COMPSCI 154 187 | COMPSCI 165 188 | COMPSCI 175 189 | COMPSCI 189 190 | IN4MATX 134 191 | } 192 | ; 193 | Algorithms 194 | 2 195 | { 196 | COMPSCI 178 197 | COMPSCI 171 198 | } 199 | 4 200 | { 201 | COMPSCI 162 202 | COMPSCI 163 203 | COMPSCI 164 204 | COMPSCI 165 205 | COMPSCI 167 206 | COMPSCI 169 207 | COMPSCI 168 208 | COMPSCI 177 209 | COMPSCI 179 210 | } 211 | ; 212 | Architecture and Embedded Systems 213 | 4 214 | { 215 | COMPSCI 144 216 | COMPSCI 145+145L 217 | COMPSCI 151 218 | COMPSCI 152 219 | COMPSCI 153 220 | COMPSCI 154 221 | } 222 | recommend 223 | { 224 | COMPSCI 131 225 | COMPSCI 142A 226 | COMPSCI 143A 227 | } 228 | ; 229 | Bioinformatics 230 | 1 231 | { 232 | COMPSCI 184A 233 | } 234 | 2 235 | { 236 | COMPSCI 172B 237 | COMPSCI 184B 238 | COMPSCI 184C 239 | COMPSCI 189 240 | } 241 | ; 242 | Information 243 | 3 244 | { 245 | COMPSCI 121 246 | COMPSCI 122A 247 | COMPSCI 178 248 | } 249 | 4 250 | { 251 | I&CSCI 45J 252 | COMPSCI 122B 253 | COMPSCI 125 254 | COMPSCI 132 255 | COMPSCI 134 256 | COMPSCI 141 257 | COMPSCI 142A 258 | COMPSCI 143A 259 | COMPSCI 163 260 | COMPSCI 165 261 | COMPSCI 167 262 | COMPSCI 179 263 | } 264 | 1 265 | { 266 | COMPSCI 122B 267 | COMPSCI 125 268 | COMPSCI 179 269 | } 270 | ; 271 | Intelligent Systems 272 | 3 273 | { 274 | COMPSCI 171 275 | COMPSCI 175 276 | COMPSCI 178 277 | } 278 | 1 279 | { 280 | COMPSCI 177 281 | COMPSCI 179 282 | } 283 | 1 284 | { 285 | COMPSCI 162 286 | COMPSCI 163 287 | COMPSCI 164 288 | COMPSCI 169 289 | } 290 | 1 291 | { 292 | COMPSCI 116 293 | COMPSCI 121 294 | COMPSCI 125 295 | COMPSCI 174 296 | COMPSCI 184B 297 | } 298 | ; 299 | Networked Systems 300 | all 301 | { 302 | COMPSCI 132 303 | COMPSCI 133 304 | COMPSCI 134 305 | COMPSCI 143A 306 | } 307 | 1 308 | { 309 | COMPSCI 122B 310 | COMPSCI 143B 311 | } 312 | 2 313 | { 314 | COMPSCI 122A 315 | COMPSCI 131 316 | COMPSCI 137 317 | COMPSCI 167 318 | COMPSCI 145-145L 319 | COMPSCI 163 320 | COMPSCI 169 321 | } 322 | ; 323 | Systems and Software 324 | 3 325 | { 326 | COMPSCI 131 327 | COMPSCI 141 328 | COMPSCI 142A 329 | COMPSCI 142B 330 | COMPSCI 143A 331 | COMPSCI 143B 332 | } 333 | recommend 334 | { 335 | COMPSCI 132 336 | COMPSCI 134 337 | COMPSCI 144 338 | COMPSCI 152 339 | } 340 | ; 341 | Visual Computing 342 | 4 343 | { 344 | COMPSCI 111 345 | COMPSCI 112 346 | COMPSCI 114 347 | COMPSCI 116 348 | COMPSCI 117 349 | I&C SCI 162 350 | } 351 | -------------------------------------------------------------------------------- /info/taken.txt: -------------------------------------------------------------------------------- 1 | 0 2 | 180 3 | WRITINGLOW1 4 | WRITINGLOW2 5 | I&CSCI33 6 | I&CSCI51 7 | GEII-1 8 | GEIII-1 9 | GEIII-2 10 | GEIII-3 11 | GEIV-1 12 | GEVI-1 13 | GEVIII-1 14 | I&CSCI31 15 | I&CSCI32 16 | I&CSCI33 17 | I&CSCI45C 18 | I&CSCI46 19 | I&CSCI90 20 | IN4MATX43 21 | MATH1A 22 | MATH1B 23 | MATH2A 24 | MATH2B 25 | I&CSCI6B 26 | I&CSCI6D 27 | MATH3A 28 | STATS67 29 | COMPSCI161 30 | COMPSCI116 31 | COMPSCI162 32 | COMPSCI171 33 | COMPSCI178 34 | POLSCI21A 35 | COMPSCI165 36 | COMPSCI122A 37 | COMPSCI175 38 | COMPSCI163 -------------------------------------------------------------------------------- /info/taken2.txt: -------------------------------------------------------------------------------- 1 | 0 2 | 0 3 | MATH1B -------------------------------------------------------------------------------- /info/widthFunc.txt: -------------------------------------------------------------------------------- 1 | 0:13 2 | else:16 3 | -------------------------------------------------------------------------------- /reports/report-final.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jennyzeng/CourseScheduling/734e261c48f38f524cbaf7daced2ef1fb1aa2f44/reports/report-final.pdf -------------------------------------------------------------------------------- /reports/research-initial-plan.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jennyzeng/CourseScheduling/734e261c48f38f524cbaf7daced2ef1fb1aa2f44/reports/research-initial-plan.pdf -------------------------------------------------------------------------------- /test/avoid.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jennyzeng/CourseScheduling/734e261c48f38f524cbaf7daced2ef1fb1aa2f44/test/avoid.txt -------------------------------------------------------------------------------- /test/courseTest.py: -------------------------------------------------------------------------------- 1 | import oldCoursesGraph 2 | from scheduling import * 3 | 4 | from DataHelper.loadData import DataLoading 5 | 6 | 7 | def loadData(major, specs, specsFilename, courseFilename, useTaken, takenFilename, useAvoid, avoidFilename, widthFuncFilename): 8 | specsCourse, specsTable = DataLoading().loadSpec( 9 | major=major, specs=specs, filename=specsFilename) 10 | graph = oldCoursesGraph() 11 | DataLoading().loadCourses(graph, courseFilename) 12 | graph.loadSpecs(specsCourse) 13 | graph.updateSatisfies() 14 | widthFuncTable = DataLoading().loadWidthFuncTable(widthFuncFilename) 15 | defaultUnits = 0 16 | startQ = 0 17 | if useTaken: 18 | startQ, defaultUnits = DataLoading().loadTaken(graph, specsTable, takenFilename) 19 | if useAvoid: 20 | DataLoading().loadAvoid(graph, avoidFilename) 21 | 22 | return graph, specsTable, defaultUnits, startQ, widthFuncTable 23 | 24 | 25 | 26 | def printResult(L, bestBound, startQ): 27 | print("start quarter: ", startQ) 28 | # print("Taking %d credits per quarter: " % (creditsPerQuarter)) 29 | for i, level in enumerate(L): 30 | i = i + startQ 31 | print("code %d :"%(level)) 32 | 33 | print("best upper bound:",bestBound ) 34 | 35 | 36 | if __name__ == '__main__': 37 | # creditsPerQuarter = 16 38 | # data loading 39 | ## for Computer Science graph 40 | graph, specsTable, defaultUnits, startQ, widthFuncTable = loadData( 41 | major="test", 42 | specs=["firstReq","secondReq"], 43 | specsFilename="./test_spec.txt", 44 | courseFilename="./testcourses.txt", 45 | useTaken=False, 46 | takenFilename="info/test/taken.txt.txt", 47 | useAvoid=False, 48 | avoidFilename="info/test/avoid.txt", 49 | widthFuncFilename="./test_widthFunc.txt" 50 | ) 51 | # scheduling 52 | L, bestBound = CourseScheduling(graph, specsTable, startQ, 3-defaultUnits, widthFuncTable).findBestSchedule(20) 53 | printResult(L, bestBound, startQ) 54 | 55 | 56 | """ 57 | start quarter: 0 58 | Taking 16 credits per quarter: 59 | year 1 quarter 1: ['I&CSCI31', 'MATH2A', 'I&CSCI6B', 'HISTORY40A'] 60 | year 1 quarter 2: ['I&CSCI32', 'MATH2B', 'I&CSCI51'] 61 | year 1 quarter 3: ['I&CSCI33', 'STATS67', 'I&CSCI6D', 'MATH3A'] 62 | year 2 quarter 1: ['I&CSCI45C', 'COMPSCI169', 'POLSCI21A', 'WRITINGLOW1'] 63 | year 2 quarter 2: ['I&CSCI46', 'COMPSCI178', 'HISTORY40B', 'GEII-1'] 64 | year 2 quarter 3: ['HISTORY40C', 'GEIII-1', 'GEIII-2', 'GEVI-1'] 65 | year 3 quarter 1: ['COMPSCI161', 'COMPSCI171', 'GEVII-1', 'GEVIII-1'] 66 | year 3 quarter 2: ['COMPSCI162', 'COMPSCI116', 'COMPSCI175', 'WRITINGLOW2'] 67 | year 3 quarter 3: ['COMPSCI163', 'COMPSCI177', 'COMPSCI165', 'IN4MATX43'] 68 | year 4 quarter 1: ['COMPSCI179', 'I&CSCI90', 'I&CSCI139W'] 69 | year 4 quarter 2: ['COMPSCI164', 'I&CSCI53+53L'] 70 | best upper bound: year 2 quarter 3 71 | """ 72 | -------------------------------------------------------------------------------- /test/taken.txt: -------------------------------------------------------------------------------- 1 | 0 2 | 0 3 | -------------------------------------------------------------------------------- /test/test_spec.txt: -------------------------------------------------------------------------------- 1 | firstReq 2 | 1 3 | { 4 | A a 5 | } 6 | 4 7 | { 8 | A a 9 | B b 10 | C c 11 | J j 12 | L l 13 | } 14 | 2 15 | { 16 | D d 17 | E e 18 | F f 19 | G g 20 | } 21 | ; 22 | secondReq 23 | 2 24 | { 25 | H h 26 | M m 27 | K k 28 | L l 29 | I i 30 | } 31 | 3 32 | { 33 | F f 34 | G g 35 | D d 36 | E e 37 | } -------------------------------------------------------------------------------- /test/test_widthFunc.txt: -------------------------------------------------------------------------------- 1 | 0:6 2 | 1:8 3 | else:10 -------------------------------------------------------------------------------- /test/testcourses.txt: -------------------------------------------------------------------------------- 1 | A;a;a;[{"Bb"},{"Hh","Ii","Jj"}];4;{0,1,2,3,4,5};False 2 | B;b;b;[{"Cc","Ee"},{"Dd"}];4;{1,3,4,5};False 3 | C;c;c;[{"Gg"}];3;{1,2,4,5};False 4 | D;d;d;[{"Ff"},{"Gg"}];4;{0,1,3};False 5 | E;e;e;[{"Ff","Gg"}];2;{0,1,3,4};False 6 | F;f;f;[];4;{0,3};False 7 | G;g;g;[];2;{0,3};False 8 | H;h;h;[{"Mm"}];2;{3};False 9 | I;i;i;[{"Kk"}];3;{1,2,4,5};True 10 | J;j;j;[{"Ll"}];5;{0,1,2,4};True 11 | K;k;k;[{"Mm"}];3;{0,3};False 12 | L;l;l;[];1;{0,1};False 13 | M;m;m;[];4;{0,1,2,3,4,5};False --------------------------------------------------------------------------------