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- # -*- coding: UTF-8 -*-
- import numpy as np
- import matplotlib as mpl
- import matplotlib.pyplot as plt
- from numpy import *
- import sys
- from scipy.interpolate import spline
- plt.clf()
- ffile = sys.argv[1]
- data = genfromtxt(ffile, unpack=True)
- N = data[0][:]
- mod = data[1][:]
- fi = data[2][:]
- i1 = data[3][:]
- X = N
- Y_01 = mod
- Y_02 = i1
- #Magnitude spline
- coefficients_mod = polyfit(X, Y_01, 6)
- polynomial_mod = poly1d(coefficients_mod)
- xs_mod = arange(0, max(N), 100)
- ys_mod = polynomial_mod(xs_mod)
- #i1 spline
- coefficients_i1 = polyfit(X, Y_02, 6)
- polynomial_i1 = poly1d(coefficients_i1)
- xs_i1 = arange(0, max(N), 100)
- ys_i1 = polynomial_i1(xs_i1)
- mpl.rcParams['figure.figsize'] = (8.0, 6.0)
- line_mod, = plt.plot(X, Y_01, 'ro--', markevery = (0, 5), label ='Magnitude')
- line_spline_mod = plt.plot(xs_mod, ys_mod, 'r-', linewidth=3.0)
- line_i1 = plt.plot(X, Y_02, 'bo--', markevery = (0, 5), label = 'Total variability(I1)')
- line_spline_i1 = plt.plot(xs_i1, ys_i1, 'b-', linewidth=3.0)
- plt.title(u'Magnitude and variability (1979-2006)')
- plt.legend(loc = 'best')
- ax_01 = plt.axes()
- ax_01.grid(color = 'black')
- ax_01.set_xlabel(u'days')
- ax_01.set_ylabel(u'[cm/s]')
- plt.axis((min(N)-50, max(N)+50, min(mod)-1, max(mod)+5))
- plt.savefig('I1_mod.png', dpi=300, format = 'png')
- print 'Correlation (magnitude,i1): %2.1f' % (corrcoef(mod, i1)[0,1])
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