#openttdcoop

#/usr/bin/env python3
import datetime as dt
import pandas as pd
import math

class Calendar(object):
    # :(
    modulecodemap = (
        ("COM00001C-A", "ICAR"),  # Introduction to Computer Architectures 15
        ("COM00001H-A", "ARTS"),  # Analysable Real-Time Systems 20
        ("COM00001I-A", "ARIN"),  # Artificial Intelligence 20
        ("COM00002H-A", "CVIS"),  # Computer Vision 20
        ("COM00002I-A", "COCO"),  # Computability & Complexity 10
        ("COM00003C-A", "HACS"),  # Human Aspects of Computer Science 20
        ("COM00003H-A", "EMBS"),  # Embedded Systems Design & Implementation 20
        ("COM00004H-A", "ENAR"),  # Enterprise Architecture 20
        ("COM00005C-A", "MFCS"),  # Mathematical Foundations of Computer Science 20
        ("COM00005H-A", "GRAT"),  # Computing by Graph Transformation 20
        ("COM00005I-A", "POPL"),  # Principles of Programming Languages 20
        ("COM00006C-A", "NUMA"),  # Numerical Analysis 10
        ("COM00006H-A", "ICOT"),  # Information & Coding Theory 20
        ("COM00007C-A", "TPOP"),  # Theory and Practice of Programming 20
        ("COM00007H-A", "INCA"),  # Introduction to Neural Computing & Applications 20
        ("COM00007I-A", "SYAC"),  # Systems Software & Compilers 30
        ("COM00008C-A", "SKIL"),  # Skills, Knowledge & Independent Learning 5
        ("COM00008I-A", "SEPR"),  # Software Engineering Project 30
        ("COM00009C-A", "FESC"),  # Foundations in Electronics, Signals and Circuits 20
        ("COM00009H-A", "MAIG"),  # Multi-Agent Interaction & Games 20
        ("COM00009I-A", "VIGR"),  # Vision & Graphics 10
        ("COM00010C-A", "PROM"),  # Programming of Micro-controllers 10
        ("COM00010H-A", "MLAP"),  # Machine Learning & Applications 20
        ("COM00012H-A", "PCOC"),  # Programming: Correctness by Construction 20
        ("COM00012I-A", "EMPR"),  # Embedded Systems Project 30
        ("COM00013H-A", "PRBE"),  # Project: Computer Science with Embedded Systems 40
        ("COM00014H-A", "PRBM"),  # Project: Mathematics and Computer Science 40
        ("COM00015H-A", "PRBX"),  # Project: Computer Science 40
        ("COM00016H-A", "IAPT"),  # Interactive Application Programming Techniques 20
        ("COM00042M-A", "QIPR"),  # Quantum Information Processing 10
        ("COM00045M-A", "QUCO"),  # Quantum Computation 10
        ("COM00063M-A", "SAVE"),  # Static Analysis & Verification 10
        ("COM00066M-A", "ALAS"),  # Adaptive & Learning Agents 10
        ("COM00068M-A", "COPR"),  # Constraint Programming 10
        ("COM00069M-A", "CRSY"),  # Critical Systems 10
        ("COM00071M-A", "EVCO"),  # Evolutionary Computation 10
        ("COM00073M-A", "GPIG"),  # Group Project (Integrated Masters) 20
        ("COM00080M-A", "PRIS"),  # Individual Project - Computer Science with Business Enterprise Systems 50
        ("COM00081M-A", "PRIA"),  # Individual Project - Computer Science with Artificial Intelligence 50
        ("COM00081M-A", "PRIY"),  # Individual Project - Computer Systems & Software Engineering 50
        ("COM00082M-A", "PSEC"),  # Topics in Privacy & Security 10
        ("COM00085M-A", "SMAT"),  # Software Measurement & Testing 10
        ("COM00087M-A", "SYAR"),  # Systems Architecture 10
        ("COM00104M-A", "AVIS"),  # Advanced Computer Vision 10
        ("COM00106M-A", "NLPR"),  # Natural Language Processing 10
        ("COM00111M-A", "MODE"),  # Model-Driven Engineering 10
        ("ELE00061M-A", "EVHW"),  # Evolvable Hardware 10
        ("ELE00068M-A", "SWIN"),  # Swarm Intelligence 10
    )

def __init__(self, filename):
        cols = ["start-date", "start-time", "end-date", "end-time",
                "activity-type", "activity-name", "taught-by", "location",
                "directions", "module", "activity-notes", "contact-details"]
        self.df = pd.read_csv(filename, skipinitialspace=True, index_col=False,
                              skiprows=1, dayfirst=True, names=cols)

# Merge the date/time fields
        self.df["start-date"] = pd.to_datetime(self.df["start-date"] + ' ' + self.df["start-time"], dayfirst=True)
        self.df["end-date"] = pd.to_datetime(self.df["end-date"] + ' ' + self.df["end-time"], dayfirst=True)
        self.df = self.df.drop("start-time", 1).drop("end-time", 1)
        self.df.index = self.df["start-date"]

# Repurpose activity-name into something useful
        self.df = self.df.rename(columns={"activity-name": "group-num"})
        self.df["group-num"] = self.df["group-num"].str.split("/").str[-1].str.split().str[0]

# Get rid of useless fields
        self.df = self.df.drop(["directions", "activity-notes",
                                "contact-details"], 1)

# Get rid of rows that are in the past
        self.df = self.df[dt.datetime.now().strftime("%Y-%m-%d %H:%M") <= self.df["end-date"]]

self.df = self.df.sort()

def get_long_code(self, code):
        """raises StopIteration"""
        return next((x[0] for x in self.modulecodemap if x[1] == code))

def get_next_lecture(self, code):
        code = code.upper()
        try:
            longcode = self.get_long_code(code)
        except StopIteration:
            # Fine, we'll try with the existing code
            longcode = code
        module = self.df[self.df["module"] == longcode]
        try:
            first = module.iloc[0]
        except:
            return "Untimetabled module"
        timestr = ""
        if first["start-date"] is not dt.datetime.today():
            timestr += first["start-date"].strftime("%Y-%m-%d")
        timestr += first["start-date"].strftime(" %H:%M")

group = ""
        if (module[(module["activity-type"].apply(str) == first["activity-type"]) &
                   (module["group-num"].apply(str) != first["group-num"])].shape[0] != 0):
            group += "Group{} ".format(first["group-num"])

lecturers = first["taught-by"].split('  ')  # double space
        lecturerstr = lecturers[0]
        for lecturer in lecturers[1:]:
            if len(lecturer) + len(lecturerstr) + 3 <= 60:
                lecturerstr += ", " + lecturer
            else:
                lecturerstr += " & others"
                break

location = first["location"]
        if math.isnan(location):
            location = "unspecified"
        return "{} {activity-type} {}in {} at {} with {}".format(code, group, location, timestr, lecturer, **first)

cal = Calendar("cs-calendar.csv")
print(cal.get_next_lecture("skil"))

#for _, row in cal.df.iterrows():
#    if "Runciman" not in str(row["taught-by"]): continue
#    print(row)
#    input()

Mark as private