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skaitiniai-metodai-labs/Lab3/part_3.py
2023-11-27 23:05:39 +02:00

76 lines
2.2 KiB
Python

import csv
import numpy as np
import matplotlib.pyplot as plt
def remove_empty_years(emissions):
empty_emissions = []
for (year, emission) in emissions:
if emission == None:
empty_emissions.append((year, emission))
for empty_entry in empty_emissions:
emissions.remove(empty_entry)
def get_country_emissions(country: str):
emissions_path = "API_EN.ATM.GHGT.KT.CE_DS2_en_csv_v2_5995567.csv"
with open(emissions_path, "r", newline="", encoding="utf-8") as f:
# Skip first 4 lines
for _ in range(4):
f.readline()
reader = csv.reader(f, delimiter=",", quotechar='"')
header_row = next(reader)
years = list(int(header) for header in header_row[4:] if header != '')
for row in reader:
if row[0] == country:
emissions_str = row[4:4+len(years)]
emissions = list(float(emission) if emission != "" else None for emission in emissions_str)
emission_points = list(zip(years, emissions))
remove_empty_years(emission_points)
years = list(row[0] for row in emission_points)
emissions = list(row[1] for row in emission_points)
return years, emissions
def approximate(X, Y, degree, scalar = 1):
degree += 1
G = np.zeros((len(X), degree), dtype=float)
for i in range(degree):
G[:, i] = np.power(X, i)
coefficients = np.linalg.solve(
np.dot(np.transpose(G), G),
np.dot(np.transpose(G), Y)
)
approx_x = np.linspace(X[0], X[-1], len(X) * scalar);
approx_y = np.zeros(approx_x.size, dtype=float)
for i in range(degree):
approx_y += np.power(approx_x, i) * coefficients[i];
return approx_x, approx_y
def main(country, degrees, scale):
years, emissions = get_country_emissions(country)
X = np.array(years, dtype=float)
Y = np.array(emissions)
for degree in degrees:
approx_x, approx_y = approximate(X, Y, degree, scale)
plt.plot(approx_x, approx_y, 'b')
for i in range(len(X)):
plt.plot(X[i], Y[i], 'ro')
plt.title(f"{degree} laipsnio")
plt.show()
main(
country = "Zambia",
degrees = [1, 2, 3, 5],
scale = 10
)