import math
import numpy as np


lbstoN = 4.44822

def str2bool(v):
  return v.lower() in ("yes", "true", "t", "1")

def uniqify(seq, idfun=None): 
   # order preserving
   if idfun is None:
       def idfun(x): return x
   seen = {}
   result = []
   for item in seq:
       marker = idfun(item)
       # in old Python versions:
       # if seen.has_key(marker)
       # but in new ones:
       if marker in seen: continue
       seen[marker] = 1
       result.append(item)
   return result

def serialize_list(value,token=','):
    assert(isinstance(value, list) or isinstance(value, tuple) or isinstance(value,np.ndarray))
    return token.join([unicode(s) for s in value])

def deserialize_list(value,token=','):
    if isinstance(value, list):
        return value
    elif isinstance(value, np.ndarray):
        return value
    return value.split(token)

def geo_distance(lat1,lon1,lat2,lon2):
    """ Approximate distance and bearing between two points
    defined by lat1,lon1 and lat2,lon2
    This is a slight underestimate but is close enough for our purposes,
    We're never moving more than 10 meters between trackpoints

    Bearing calculation fails if one of the points is a pole. 
    (Hey, from the North pole you can walk South, East, North and end up 
    on the same spot!)
    
    """
    
    # radius of our earth in km --> should be moved to settings if
    # rowing takes off on other planets
    R = 6373.0

    # pi
    pi = math.pi

    lat1 = math.radians(lat1)
    lat2 = math.radians(lat2)
    lon1 = math.radians(lon1)
    lon2 = math.radians(lon2)

    dlon = lon2 - lon1
    dlat = lat2 - lat1

    a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
    c = 2 * atan2(sqrt(a), sqrt(1 - a))

    distance = R * c

    tc1 = atan2(sin(lon2-lon1)*cos(lat2),
		cos(lat1)*sin(lat2)-sin(lat1)*cos(lat2)*cos(lon2-lon1))

    tc1 = tc1 % (2*pi)

    bearing = math.degrees(tc1)

    return [distance,bearing]