config.py

import numpy as np

gship_tuition = {
    "Gship": 539.33,
    "Instate": 1509.01,
    "Non-resident": 1678,
    "Tuition inflation": 0.1,
}

salary = {
    "Step 8": 6514.81,
    "Step 9": 6656.08,
    "Step 10": 7186.58,
    "Fringe 8": 8986.0 / 3.0,
    "Fringe 9": 8986.0 / 3.0,
    "Fringe 10": 9702.0 / 3.0,
    "Inflation": 0.03,
    "SRA": 5336.0,
}

salary_per_department = {
    "ece": {
        "pre-master": np.nan,
        "pre-candidacy": 0.45 * salary["Step 9"] + 0.023 * salary["Fringe 9"],
        "post-candidacy": 0.45 * salary["Step 10"] + 0.023 * salary["Fringe 10"],
        "candidacy year": 4,
    },
    "cs": {
        "pre-master": 0.49 * salary["Step 8"] + 0.023 * salary["Fringe 8"],
        "pre-candidacy": 0.49 * salary["Step 9"] + 0.023 * salary["Fringe 9"],
        "post-candidacy": 0.49 * salary["Step 10"] + 0.023 * salary["Fringe 10"],
        "candidacy year": 3,
    },
    "phys": {
        "pre-master": np.nan,
        "pre-candidacy": 0.4999 * salary["Step 8"] + 0.023 * salary["Fringe 8"],
        "post-candidacy": 0.4999 * salary["Step 9"] + 0.023 * salary["Fringe 9"],
        "candidacy year": 3,
    },
}

name_dict = {
    "Student 1": {
        "department": "",
        "start": "",
        "end": "",
        "instate": "",
        "candidacy": "",
        "overhead": "",
    },
    "Student 2": {
        "department": "",
        "start": "",
        "end": "",
        "instate": "",
        "candidacy": "",
        "overhead": "",
    },
    "Student 3": {
        "department": "",
        "start": "",
        "end": "",
        "instate": "",
        "candidacy": "",
        "overhead": "",
    },
}