Playing around with generating 1D polynomials using numpy

numpy_polynomials.py

#For full reference, see http://docs.scipy.org/doc/numpy-1.10.1/reference/routines.polynomials.poly1d.html

def generate_poly(points, n): 
	'''
	Generates an n degree polynomial from 2d array of discrete Points.
	parameter points is a 2D numpy array: e.g. np.array([(1, 1), (2, 4), (3, 1), (9, 3)])
	'''
	return np.poly1d(np.polyfit(points[:,0], points[:,1], n))

points = np.array([(1050, 1.0), (900, 0.775), (800, 0.5)])
poly_from_points = generate_poly(points, 3)

new_x_values = [1,2,3,4,5] #...

#Extrapolation / Interpolation
#Generate an array of y values for this polynomial; each value corresponds to its respective new_x_value
poly_from_roots_arr = np.polyval(poly_from_points, new_x_values)

############################################################################

'''
Generate a polynomial from array of coefficients

Parameter in poly1d is a list of polynomal roots

polynomial can be shifted up or down by "vertical_shift"
'''

x_values = [1,2,3,4,5] #...

vertical_shift = 0
poly_from_roots = np.poly1d([-1,0,1]) 

#Generate an array of y values for this polynomial; each value corresponds to its respective x_value
poly_from_roots_arr = np.polyval(poly_from_roots, x_values) + vertical_shift

############################################################################

'''
Generate a polynomial from list of coefficents, in decresing powers
'''

x_values = [1,2,3,4,5] #...

#e.g. 2x^2 + 1*x + 0
poly_from_coefficents = np.poly1d(c_or_r=[2,1,0], r=False)

#Generate an array of y values for this polynomial; each value corresponds to its respective x_value
poly_from_coefficents_arr = np.polyval(poly_from_coefficents, x_values)