├── 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 |
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/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 |
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/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 |
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/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('*b>|
|\\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:
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https://raw.githubusercontent.com/jennyzeng/CourseScheduling/734e261c48f38f524cbaf7daced2ef1fb1aa2f44/DataHelper/__init__.py
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/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 |
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/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 |
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/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
--------------------------------------------------------------------------------