math.tan

  assert abs( sin(a_x)    - math.sin(x) )   < delta
  assert abs( sinh(a_x)   - math.sinh(x) )  < delta
  assert abs( sqrt(a_x)   - math.sqrt(x) )  < delta
  assert abs( tan(a_x)    - math.tan(x) )   < delta
  assert abs( tanh(a_x)   - math.tanh(x) )  < delta
 
  # example array and derivative calculation

  assert abs( sin(a2x)    - math.sin(x) )   < delta
  assert abs( sinh(a2x)   - math.sinh(x) )  < delta
  assert abs( sqrt(a2x)   - math.sqrt(x) )  < delta
  assert abs( tan(a2x)    - math.tan(x) )   < delta
  assert abs( tanh(a2x)   - math.tanh(x) )  < delta
 
  # example array and derivative calculation

## example10_6
from fletcherReeves import *
from numarray import array,zeros,Float64
from math import cos,tan,pi
 
def F(x):
    return  8.0/x[0] - x[0]*(tan(x[1]) - 2.0/cos(x[1]))
 
def gradF(x):
    g = zeros((2),type=Float64)
    g[0] = -8.0/(x[0]**2) - tan(x[1]) + 2.0/cos(x[1])
    g[1] = x[0]*(-1.0/cos(x[1]) + 2.0*tan(x[1]))/cos(x[1])

 
x = array([2.0, 0.0])
x,nIter = optimize(F,gradF,x)
b = 8.0/x[0] - x[0]*tan(x[1])
print "h =",x[0],"m"
print "b =",b,"m"
print "theta =",x[1]*180.0/pi,"deg"

    def __init__(self,prog):
         self.prog = prog
 
         self.functions = {           # Built-in function table
             'SIN' : lambda z: math.sin(self.eval(z)),
             'COS' : lambda z: math.cos(self.eval(z)),
             'TAN' : lambda z: math.tan(self.eval(z)),

    def __init__(self,prog):
         self.prog = prog
 
         self.functions = {           # Built-in function table
             'SIN' : lambda z: math.sin(self.eval(z)),
             'COS' : lambda z: math.cos(self.eval(z)),
             'TAN' : lambda z: math.tan(self.eval(z)),

    def __init__(self,prog):
         self.prog = prog
 
         self.functions = {           # Built-in function table
             'SIN' : lambda z: math.sin(self.eval(z)),
             'COS' : lambda z: math.cos(self.eval(z)),
             'TAN' : lambda z: math.tan(self.eval(z)),

from sympy import var
from math import sin, cos, tan, pi
 
def dependent_qdot(_x, _params):
    """Linear mapping from lean rate, steer rate, front wheel rate to yaw rate, rear
    wheel rate, pitch rate, rear wheel contact point velocity in N[1] and N[2]
    directions.

    s0 = sin(q0)
    s3 = sin(q3)
    s4 = sin(q4)
    t1 = tan(q1)
 
    # Calculate return values
    q0d = c3*u2/c1 - s3*u0/c1

    s1 = sin(q1)
    s3 = sin(q3)
    s4 = sin(q4)
    t1 = tan(q1)
 
    # Nested terms
    e1c_s = rf*(s1*s4 - c1*c4*s3)/(1 - (c4*s1 + c1*s3*s4)**2)**(1/2)

from __future__ import division
from math import sin, cos, tan, pi
 
def dependent_qdot(_x, _params):
    """Linear mapping from lean rate, steer rate, front wheel rate to yaw rate, rear
    wheel rate, pitch rate, rear wheel contact point velocity in N[1] and N[2]
    directions.

    s0 = sin(q0)
    s3 = sin(q3)
    s4 = sin(q4)
    t1 = tan(q1)
 
    # Calculate return values
    q0d = c3*u2/c1 - s3*u0/c1

    s1 = sin(q1)
    s3 = sin(q3)
    s4 = sin(q4)
    t1 = tan(q1)
 
    # Nested terms
    e1c_s = rf*(s1*s4 - c1*c4*s3)/(1 - (c4*s1 + c1*s3*s4)**2)**(1/2)

    con=eccent*sinphi
    com=.5*eccent
    con=math.pow(((1.0-con)/(1.0+con)),com)
    ts=math.tan(.5*((math.pi*0.5)-phi))/con
    y=0-r_major*math.log(ts)
    return y
 

from __future__ import division
from math import sin, cos, tan
 
def eoms(_x, t, _params):
    """Rigidy body equations of motion.
 
    _x is an array/list in the following order:

    c2 = cos(q2)
    c3 = cos(q3)
    s3 = sin(q3)
    t2 = tan(q2)
 
    # Calculate return values
    q1d = c3*u3/c2 - s3*u1/c2

           curve5 = []
           for val in curve1:
               curve3.append( math.cos(val) );
               curve4.append( math.tan(val) );
               curve5.append( math.fabs(val) );
 
           node.AddChild(Baum.Table("5 Column Table", [curve1,curve2, curve3,curve4,curve5], ["Curve 1", "Curve 2","Curve 3", "Curve 4","Curve 5",] ))