import numpy as np
import scipy.stats as spst
import scipy.optimize as spop
import scipy.interpolate as spint

year, aus, can = np.loadtxt('temperatures.txt', delimiter=',', 
                            skiprows=1, unpack=True)

plot(year, aus)
title('Australia')
xlabel('Year')
ylabel('Temperature (C)')

plot(year, can)
title('Canada')
xlabel('Year')
ylabel('Temperature (C)')

aus_line = spst.linregress(year, aus)

aus_line_pts = aus_line[0] * year + aus_line[1]

plot(year, aus)
plot(year, aus_line_pts)
title('Australia')
xlabel('Year')
ylabel('Temperature (C)')

def linear_func(year, slope, intercept):
    return slope * year + intercept

can_line, can_cov = spop.curve_fit(linear_func, year, can)

can_line_pts = linear_func(year, can_line[0], can_line[1])

plot(year, can)
plot(year, can_line_pts)
title('Canada')
xlabel('Year')
ylabel('Temperature (C)')

spst.pearsonr(year, can)

spline = spint.UnivariateSpline(year, aus, k=5)

x = np.linspace(year.min(), year.max(), 1000)

y = spline(x)

plot(year, aus)
plot(x, y)
title('Australia')
xlabel('Year')
ylabel('Temperature (C)')

plot(aus, can, 'bo')
xlabel('Australia Temp (C)')
ylabel('Canada Temp (C)')

new_line = spst.linregress(aus, can)
new_line_pts = new_line[0] * aus + new_line[1]
plot(aus, can, 'bo')
plot(aus, new_line_pts, 'r-')
xlabel('Australia Temp (C)')
ylabel('Canada Temp (C)')

new_line

aus_sub = aus - aus_line_pts

can_sub = can - can_line_pts

plot(year, aus_sub, label='Aus')
plot(year, can_sub, label='Can')
xlabel('Year')
ylabel('Temperature Variation (C)')
legend(loc='lower left')