Let's assume we have the following situation:

• Multiple project topics exist, each corresponding to a natural number n,


• students can sign in and choose two projects and give them either priority_1 or priority_2,


• The name and the time when they sign in is tracked as well.

With all of the information above assume you receive data in the from of a txt-file looking like this

Martin 16:46:32 8 19
Alice 15:22:56 8 12
Alex 17:23:11 19 1
John 19:02:11 11 13
Phillip 19:03:11 11 13
Diego 15:23:57 14 5
Jack 16:46:45 8 3

where the columns represent the name, time, priority 1 and priority 2 in this order. You can assume that all of them signed in on the same day and students which signed in first have a higher priority in comparison to the others.

I wanted to write a program that takes this txt-file as input and returns a txt-file with output

Name Assigned Project
Alice 8
Diego 14
Martin 19
Jack 3
Alex 1
John 11
Phillip 13

The solution I came up with looks like this:

import numpy as np

dat = np.genfromtxt("data.txt", dtype="str")

class person:
 def __init__(self, name, time, prio1, prio2):
 self.name = name
 sp = time.split(":")
 t = sp[0]*3600 + sp[1]*60 + sp[2] 
 self.time = t
 self.prio1 = prio1
 self.prio2 = prio2

names = dat[:, 0]
time = dat[:, 1]
prio1 = dat[:, 2]
prio2 = dat[:, 3]

people = 
for i,j,k,l in zip(names, time, prio1, prio2):
 people.append(person(i,j,k,l))

people_sorted_time = sorted(people, key=lambda x: x.time) 

for k in range(len(people_sorted_time)):
 for j in range(k + 1, len(people_sorted_time)):
 if people_sorted_time[k].prio1 == people_sorted_time[j].prio1:
 people_sorted_time[j].prio1 = people_sorted_time[j].prio2

res = open("results","w")
res.write("Name"+"t"+"Assigned Project"+"n")
for k in range(len(people_sorted_time)):
 res.write(people_sorted_time[k].name + "t" 
 + people_sorted_time[k].prio1 + "n")

The code seems to work fine, but I'm not sure if I actually was able to take care of all edge-cases. I'm also not sure if this is really a efficient way to solve the problem, I rarely code stuff like that (mostly computational physics stuff), and would appreciate any kind of suggestions on how one could improve the code in general.

EDIT: What I realized after some thinking is that it would probably be quite hard do implement further priority variables (like prio 3, prio 4, etc.). If someone could suggest a better way of deciding how to assign the projects in terms of priority, I'd be glad to see it.

There are two issues I personally dislike very much.

Messing with date/time using homebrew algorithm and math

While looking easy to handle time/date are by far more complcated to handle than you ever can think of. Timezones, locals, leap years and so on. When doing time/date math and/or serialisation/deserialisation always(!) go for a library. For python this is datetime

When the code is lying to me when I'm reading it as natural language

With lying I mean your line

people_sorted_time[j].prio1 = people_sorted_time[j].prio2

This is not true. The person clearly stated a first priority project. When you change that value as an algorithmic side effect you immediatly break data integrity. Your person suddenly has both priorities on the same project. You even got yourself tricked. What happens, when a person got the first prio project taken away and later the second one as well?

Other issues

There is no need to have people_sorted_time, as you never refer to the read order again. Just do

people = sorted(people, key=lambda x: x.time) 

Never loop over range(len(something), always try to loop over the elements directly. Your output loop rewrites (still lying about prio1) to

for p in people:
 res.write(p.name + "t" + p.prio1 + "n")

You use numpy only for reading a file, then convert back to standard python. This is a fail. Read with python directly.

with open("data.txt") as infile:
 lines = infile.readlines()
people = [Person(*line.split()) for line in lines]

You need time for comparison only. There is no need to mess with it, string comparison will do.

self.time = time

Do not modify the people data but maintain a set of available projects

prio1_projects = set(p.prio1 for p in people)
prio2_projects = set(p.prio2 for p in people)
projects_available = prio1_projects | prio2_projects

When assigning projects we do it like

people = sorted(people, key = lambda p: p.time)
assignments = 
for p in people:
 if p.prio1 in projects_available:
 proj = p.prio1
 elif p.prio2 in projects_available:
 proj = p.prio2
 else:
 proj = None
 assignments.append(proj)
 if proj is not None:
 projects_available.remove(proj)

Note the new None case.

The output code

with open("results","w") as res:
 res.write("Name"+"t"+"Assigned Project"+"n")
 for p, a in zip(people, assignments):
 res.write(p.name + "t" + str(a) + "n")