The question is broad in scope, but here are some simple ideas (and code!) that might serve as a starting point for computing arctan. First, the good old Taylor series. For simplicity, we use a fixed number of terms; in practice, you might want to decide the number of terms to use dynamically based on the size of x, or introduce some kind of convergence criterion. With a fixed number of terms, we can evaluate efficiently using something akin to Horner's scheme.
def arctan_taylor(x, terms=9):
"""
Compute arctan for small x via Taylor polynomials.
Uses a fixed number of terms. The default of 9 should give good results for
abs(x) < 0.1. Results will become poorer as abs(x) increases, becoming
unusable as abs(x) approaches 1.0 (the radius of convergence of the
series).
"""
# Uses Horner's method for evaluation.
t = 0.0
for n in range(2*terms-1, 0, -2):
t = 1.0/n - x*x*t
return x * t
The above code gives good results for small x (say smaller than 0.1 in absolute value), but the accuracy drops off as x becomes larger, and for abs(x) > 1.0, the series never converges, no matter how many terms (or how much extra precision) we throw at it. So we need a better way to compute for larger x. One solution is to use argument reduction, via the identity arctan(x) = 2 * arctan(x / (1 + sqrt(1 + x^2))). This gives the following code, which builds on arctan_taylor to give reasonable results for a wide range of x (but beware possible overflow and underflow when computing x*x).
import math
def arctan_taylor_with_reduction(x, terms=9, threshold=0.1):
"""
Compute arctan via argument reduction and Taylor series.
Applies reduction steps until x is below `threshold`,
then uses Taylor series.
"""
reductions = 0
while abs(x) > threshold:
x = x / (1 + math.sqrt(1 + x*x))
reductions += 1
return arctan_taylor(x, terms=terms) * 2**reductions
Alternatively, given an existing implementation for tan, you could simply find a solution y to the equation tan(y) = x using traditional root-finding methods. Since arctan is already naturally bounded to lie in the interval (-pi/2, pi/2), bisection search works well:
def arctan_from_tan(x, tolerance=1e-15):
"""
Compute arctan as the inverse of tan, via bisection search. This assumes
that you already have a high quality tan function.
"""
low, high = -0.5 * math.pi, 0.5 * math.pi
while high - low > tolerance:
mid = 0.5 * (low + high)
if math.tan(mid) < x:
low = mid
else:
high = mid
return 0.5 * (low + high)
Finally, just for fun, here's a CORDIC-like implementation, which is really more appropriate for a low-level implementation than for Python. The idea here is that you precompute, once and for all, a table of arctan values for 1, 1/2, 1/4, etc., and then use those to compute general arctan values, essentially by computing successive approximations to the true angle. The remarkable part is that, after the precomputation step, the arctan computation involves only additions, subtractions, and multiplications by by powers of 2. (Of course, those multiplications aren't any more efficient than any other multiplication at the level of Python, but closer to the hardware, this could potentially make a big difference.)
cordic_table_size = 60
cordic_table = [(2**-i, math.atan(2**-i))
for i in range(cordic_table_size)]
def arctan_cordic(y, x=1.0):
"""
Compute arctan(y/x), assuming x positive, via CORDIC-like method.
"""
r = 0.0
for t, a in cordic_table:
if y < 0:
r, x, y = r - a, x - t*y, y + t*x
else:
r, x, y = r + a, x + t*y, y - t*x
return r
Each of the above methods has its strengths and weaknesses, and all of the above code can be improved in a myriad of ways. I encourage you to experiment and explore.
To wrap it all up, here are the results of calling the above functions on a small number of not-very-carefully-chosen test values, comparing with the output of the standard library math.atan function:
test_values = [2.314, 0.0123, -0.56, 168.9]
for value in test_values:
print("{:20.15g} {:20.15g} {:20.15g} {:20.15g}".format(
math.atan(value),
arctan_taylor_with_reduction(value),
arctan_from_tan(value),
arctan_cordic(value),
))
Output on my machine:
1.16288340166519 1.16288340166519 1.16288340166519 1.16288340166519
0.0122993797673 0.0122993797673 0.0122993797673002 0.0122993797672999
-0.510488321916776 -0.510488321916776 -0.510488321916776 -0.510488321916776
1.56487573286064 1.56487573286064 1.56487573286064 1.56487573286064
