import numpy as np
import matplotlib
import matplotlib.pyplot as plt

#S3  5610 https://www.futuremark.com/hardware/mobile/Samsung+Galaxy+S3+%28MSM8960%29/review
#S4: 18443 https://www.futuremark.com/hardware/mobile/Samsung+Galaxy+S4+4G_+%28MSM8974AA+v2%29/review
#S5: 18437 https://www.futuremark.com/hardware/mobile/Samsung+Galaxy+S5+LTE-A/review
#S6: 21488 https://www.futuremark.com/hardware/mobile/Samsung+Galaxy+S6+Edge/review
#S7: 28510 https://www.tek.no/artikler/test-samsung-galaxy-s9/431900/2
#S8: 28940 https://www.tek.no/artikler/test-samsung-galaxy-s9/431900/2
#S9: 39365 https://www.tek.no/artikler/test-samsung-galaxy-s9/431900/2
samsung_scores = np.array([5610, 18433, 18437, 21488, 28510, 28940, 39365])

# Release dates of Galaxy S (Rounded down to month)
# (source: google "Samsung Galaxy S<version> release date")
samsung_versions = np.array([2012+5./12, 2013+4/12., 2014+4/12., 2015+4/12.,
                             2016+3./12, 2017+4/12., 2018+3/12.])

p_samsung =  plt.plot(samsung_versions, samsung_scores, '*',
         label='Samsung Galaxy S <Year>')

# Now we fit the data, except the latest, and see how good a prediction it is
samsung_versions_shifted = samsung_versions - samsung_versions[0]*np.ones_like(samsung_versions)
samsung_poly_coeff = np.polyfit(samsung_versions_shifted[:-1],
    np.log(samsung_scores[:-1]), 1)


plt.plot(samsung_versions,
         np.exp(samsung_poly_coeff[1])*np.exp(samsung_versions_shifted*samsung_poly_coeff[0]),
         '--', color=p_samsung[0].get_color(),
         label='$%.2f (%.2f)^{\\mathrm{Year} - %.2f}$'
         % (np.exp(samsung_poly_coeff[1]),
          np.exp(samsung_poly_coeff[0]),
          samsung_versions[0]))


# iphone 4s 2351 https://www.futuremark.com/hardware/mobile/Apple+iPhone+4s/review
# iphone 5 6005 https://www.futuremark.com/hardware/mobile/Apple+iPhone+5/review
# iphone 5s 14819 https://www.futuremark.com/hardware/mobile/Apple+iPhone+5s/review
# iphone 6 17281 https://www.futuremark.com/hardware/mobile/Apple+iPhone+6/review
# iphone 6s 28051 https://www.futuremark.com/hardware/mobile/Apple+iPhone+6s/review
# iphone 7 36132 https://www.tek.no/artikler/test-samsung-galaxy-s9/431900/2
# iphone 8 64785 https://www.tek.no/artikler/test-samsung-galaxy-s9/431900/2

iphone_scores = np.array([2351, 6005, 14819, 17281, 28051, 36132, 64785])

# Release dates of iPhone (Rounded down to month)
# (source: google "Iphone <version> release date")
iphone_versions = np.array(
    [2011. + 10/12., 2012 + 9/12., 2013+ 9/12., 2014+9/12.,
                   2015+9/12., 2016+9/12., 2017+9/12.])


p_apple = plt.plot(iphone_versions, iphone_scores,
                   'o', label='Apple iPhone <Year>')

iphone_versions_shifted = iphone_versions - iphone_versions[0]*np.ones_like(iphone_versions)
apple_poly_coeff = np.polyfit(iphone_versions_shifted[:-1],
    np.log(iphone_scores[:-1]), 1)


plt.plot(iphone_versions,
         np.exp(apple_poly_coeff[1])*np.exp(iphone_versions_shifted*apple_poly_coeff[0]),
         '--', color=p_apple[0].get_color(),
         label='$%.2f (%.2f)^{\\mathrm{Year} - %.2f}$'
         % (np.exp(apple_poly_coeff[1]),
          np.exp(apple_poly_coeff[0]),
          iphone_versions[0]))


plt.xlabel('Year')
plt.ylabel('3D Mark Ice Storm Unlimited')
plt.legend()
plt.grid("on")
plt.show()