theilsen.py

"""
Theil-Sen Regression Calculation for Points

This file is Copyright (c) 2020 Lorena Buciu, Rafee Rahman, Kevin Yang, Ricky Yi
"""

import statistics
import csv
from typing import Tuple, List
from scipy.stats import linregress


def process_file() -> Tuple[int, List[float]]:
    """Return the new values to add to the file
    """
    # Predict until 2300
    years = 2301
    x_data = []
    y_data = []
    with open('data_predictions.csv', 'r') as read_obj:
        read_obj.readline()  # skip first line
        reader = csv.reader(read_obj)
        for row in reader:
            x_data.append(int(row[0]))
            y_data.append(float(row[1]))

    m, b = theil_sen_linear_model(x_data, y_data)
    new_values = projected_values(m, b, years)

    return years, new_values


def linear_regression(x_coords: list, y_coords: list) -> list:
    """
    returns slope, intercept, correlation coefficient, [p value]

    Preconditions:
        - x_coords != []
        - y_coords != []

    """

    slope, intercept, correlation, _, _ = linregress(x_coords, y_coords)
    return [slope, intercept, correlation]


def theil_sen_linear_model(x_cords: list, y_cords: list) -> Tuple[float, float]:
    """
    Returns the linear equation once a Theil-Sen analysis is performed on the input
    data. The slope of the model is the median of all slopes between each set of data
    points and calculates the y intercept of the model as well.
    """
    list_of_slopes = []
    for x in range(len(x_cords) - 1):
        slope = (y_cords[x + 1] - y_cords[x]) / (x_cords[x + 1] - x_cords[x])
        list_of_slopes.append(slope)

    m = statistics.median(list_of_slopes)
    b = y_cords[1] - m * x_cords[1]
    return (m, b)


def projected_values(slope: float, y_int: float, years: int) -> List[float]:
    """
    Returns a list of projected ocean levels based on a Theil-Sen linear model
    """
    final_values = []
    for num in range(2021, years + 1):
        projection = slope * num + y_int
        final_values.append(projection)
    return final_values