Python math.cos方法代码示例

# 需要导入模块: import math [as 别名]
# 或者: from math import cos [as 别名]
def swirl(x, y, step):
    x -= (u_width / 2)
    y -= (u_height / 2)
    dist = math.sqrt(pow(x, 2) + pow(y, 2)) / 2.0
    angle = (step / 10.0) + (dist * 1.5)
    s = math.sin(angle)
    c = math.cos(angle)
    xs = x * c - y * s
    ys = x * s + y * c
    r = abs(xs + ys)
    r = r * 12.0
    r -= 20
    return (r, r + (s * 130), r + (c * 130))


# roto-zooming checker board 

# 需要导入模块: import math [as 别名]
# 或者: from math import cos [as 别名]
def checker(x, y, step):
    x -= (u_width / 2)
    y -= (u_height / 2)
    angle = (step / 10.0)
    s = math.sin(angle)
    c = math.cos(angle)
    xs = x * c - y * s
    ys = x * s + y * c
    xs -= math.sin(step / 200.0) * 40.0
    ys -= math.cos(step / 200.0) * 40.0
    scale = step % 20
    scale /= 20
    scale = (math.sin(step / 50.0) / 8.0) + 0.25
    xs *= scale
    ys *= scale
    xo = abs(xs) - int(abs(xs))
    yo = abs(ys) - int(abs(ys))
    v = 0 if (math.floor(xs) + math.floor(ys)) % 2 else 1 if xo > .1 and yo > .1 else .5
    r, g, b = hue_to_rgb[step % 255]
    return (r * (v * 255), g * (v * 255), b * (v * 255))


# weeee waaaah 

# 需要导入模块: import math [as 别名]
# 或者: from math import cos [as 别名]
def stance_pct_to_pre(pct, x_or_y):
    """
    pct is our % of the way to the y-axis from
    the x-axis around the unit circle. (If x_or_y == Y, it is the opposite.)
    It will return the x, y coordinates of the point that % of the way.
    I.e., .5 returns NEUT_VEC, 0 returns X_VEC.
    """
    if x_or_y == Y:
        pct = 1 - pct
    if pct == 0:
        return VectorSpace.X_PRE
    elif pct == .5:
        return VectorSpace.NEUT_PRE
    elif pct == 1:
        return VectorSpace.Y_PRE
    else:
        angle = 90 * pct
        x = math.cos(math.radians(angle))
        y = math.sin(math.radians(angle))
        return VectorSpace(x, y) 

# 需要导入模块: import math [as 别名]
# 或者: from math import cos [as 别名]
def distance(pointA, pointB):
	"""
	Calculate the great circle distance between two points 
	on the earth (specified in decimal degrees)

	http://stackoverflow.com/questions/15736995/how-can-i-quickly-estimate-the-distance-between-two-latitude-longitude-points
	"""
	# convert decimal degrees to radians 
	lon1, lat1, lon2, lat2 = map(math.radians, [pointA[1], pointA[0], pointB[1], pointB[0]])

	# haversine formula 
	dlon = lon2 - lon1 
	dlat = lat2 - lat1 
	a = math.sin(dlat/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2)**2
	c = 2 * math.asin(math.sqrt(a)) 
	r = 3956  # Radius of earth in miles. Use 6371 for kilometers
	return c * r 

# 需要导入模块: import math [as 别名]
# 或者: from math import cos [as 别名]
def distance(origin, destination):
	"""Determine distance between 2 sets of [lat,lon] in km"""

	lat1, lon1 = origin
	lat2, lon2 = destination
	radius = 6371  # km

	dlat = math.radians(lat2 - lat1)
	dlon = math.radians(lon2 - lon1)
	a = (math.sin(dlat / 2) * math.sin(dlat / 2) +
		 math.cos(math.radians(lat1)) *
		 math.cos(math.radians(lat2)) * math.sin(dlon / 2) *
		 math.sin(dlon / 2))
	c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
	d = radius * c

	return d 

# 需要导入模块: import math [as 别名]
# 或者: from math import cos [as 别名]
def rotation_matrix_3D(u, th):
    """
    rotation_matrix_3D(u, t) yields a 3D numpy matrix that rotates any vector about the axis u
    t radians counter-clockwise.
    """
    # normalize the axis:
    u = normalize(u)
    # We use the Euler-Rodrigues formula;
    # see https://en.wikipedia.org/wiki/Euler-Rodrigues_formula
    a = math.cos(0.5 * th)
    s = math.sin(0.5 * th)
    (b, c, d) = -s * u
    (a2, b2, c2, d2) = (a*a, b*b, c*c, d*d)
    (bc, ad, ac, ab, bd, cd) = (b*c, a*d, a*c, a*b, b*d, c*d)
    return np.array([[a2 + b2 - c2 - d2, 2*(bc + ad),         2*(bd - ac)],
                     [2*(bc - ad),       a2 + c2 - b2 - d2,   2*(cd + ab)],
                     [2*(bd + ac),       2*(cd - ab),         a2 + d2 - b2 - c2]]) 

# 需要导入模块: import math [as 别名]
# 或者: from math import cos [as 别名]
def _gen_signal(self, t, phase):
    """Generates a sinusoidal reference leg trajectory.

    The foot (leg tip) will move in a ellipse specified by extension and swing
    amplitude.

    Args:
      t: Current time in simulation.
      phase: The phase offset for the periodic trajectory.

    Returns:
      The desired leg extension and swing angle at the current time.
    """
    period = 1 / self._step_frequency
    extension = self._extension_amplitude * math.cos(
        2 * math.pi / period * t + phase)
    swing = self._swing_amplitude * math.sin(2 * math.pi / period * t + phase)
    return extension, swing 

# 需要导入模块: import math [as 别名]
# 或者: from math import cos [as 别名]
def _signal(self, t):
    initial_pose = np.array([
        INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS,
        INIT_EXTENSION_POS, INIT_EXTENSION_POS, INIT_EXTENSION_POS,
        INIT_EXTENSION_POS
    ])
    amplitude = STEP_AMPLITUDE
    period = STEP_PERIOD
    extension = amplitude * (-1.0 + math.cos(2 * math.pi / period * t))
    ith_leg = int(t / period) % 2
    first_leg = np.array([0, 0, 0, 0, 0, extension, extension, 0])
    second_leg = np.array([0, 0, 0, 0, extension, 0, 0, extension])
    if ith_leg:
      signal = initial_pose + second_leg
    else:
      signal = initial_pose + first_leg
    return signal 

# 需要导入模块: import math [as 别名]
# 或者: from math import cos [as 别名]
def random_quat(rand=None):
    """Return uniform random unit quaternion.
    rand: array like or None
        Three independent random variables that are uniformly distributed
        between 0 and 1.
    >>> q = random_quat()
    >>> np.allclose(1.0, vector_norm(q))
    True
    >>> q = random_quat(np.random.random(3))
    >>> q.shape
    (4,)
    """
    if rand is None:
        rand = np.random.rand(3)
    else:
        assert len(rand) == 3
    r1 = np.sqrt(1.0 - rand[0])
    r2 = np.sqrt(rand[0])
    pi2 = math.pi * 2.0
    t1 = pi2 * rand[1]
    t2 = pi2 * rand[2]
    return np.array(
        (np.sin(t1) * r1, np.cos(t1) * r1, np.sin(t2) * r2, np.cos(t2) * r2),
        dtype=np.float32,
    ) 

# 需要导入模块: import math [as 别名]
# 或者: from math import cos [as 别名]
def rbbox_to_corners(corners, rbbox):
    # generate clockwise corners and rotate it clockwise
    angle = rbbox[4]
    a_cos = math.cos(angle)
    a_sin = math.sin(angle)
    center_x = rbbox[0]
    center_y = rbbox[1]
    x_d = rbbox[2]
    y_d = rbbox[3]
    corners_x = cuda.local.array((4, ), dtype=numba.float32)
    corners_y = cuda.local.array((4, ), dtype=numba.float32)
    corners_x[0] = -x_d / 2
    corners_x[1] = -x_d / 2
    corners_x[2] = x_d / 2
    corners_x[3] = x_d / 2
    corners_y[0] = -y_d / 2
    corners_y[1] = y_d / 2
    corners_y[2] = y_d / 2
    corners_y[3] = -y_d / 2
    for i in range(4):
        corners[2 *
                i] = a_cos * corners_x[i] + a_sin * corners_y[i] + center_x
        corners[2 * i
                + 1] = -a_sin * corners_x[i] + a_cos * corners_y[i] + center_y 

# 需要导入模块: import math [as 别名]
# 或者: from math import cos [as 别名]
def __init__(
            self,
            classes,
            m=0.5,
            s=64,
            easy_margin=True,
            weight=None,
            size_average=None,
            ignore_index=-100,
            reduce=None,
            reduction='mean'):
        super(ArcLoss, self).__init__(weight, size_average, reduce, reduction)
        self.ignore_index = ignore_index
        assert s > 0.
        assert 0 <= m <= (math.pi / 2)
        self.s = s
        self.m = m
        self.cos_m = math.cos(m)
        self.sin_m = math.sin(m)
        self.mm = math.sin(math.pi - m) * m
        self.threshold = math.cos(math.pi - m)
        self.classes = classes
        self.easy_margin = easy_margin 

# 需要导入模块: import math [as 别名]
# 或者: from math import cos [as 别名]
def _get_body(self, x, target):
        cos_t = torch.gather(x, 1, target.unsqueeze(1))  # cos(theta_yi)
        if self.easy_margin:
            cond = torch.relu(cos_t)
        else:
            cond_v = cos_t - self.threshold
            cond = torch.relu(cond_v)
        cond = cond.bool()
        # Apex would convert FP16 to FP32 here
        # cos(theta_yi + m)
        new_zy = torch.cos(torch.acos(cos_t) + self.m).type(cos_t.dtype)
        if self.easy_margin:
            zy_keep = cos_t
        else:
            zy_keep = cos_t - self.mm  # (cos(theta_yi) - sin(pi - m)*m)
        new_zy = torch.where(cond, new_zy, zy_keep)
        diff = new_zy - cos_t  # cos(theta_yi + m) - cos(theta_yi)
        gt_one_hot = F.one_hot(target, num_classes=self.classes)
        body = gt_one_hot * diff
        return body 

# 需要导入模块: import math [as 别名]
# 或者: from math import cos [as 别名]
def rotate_bbox(box, a):
  '''Rotate a bounding box 4-tuple by an angle in degrees'''
  corners = ( (box[0], box[1]), (box[0], box[3]), (box[2], box[3]), (box[2], box[1]) )
  a = -math.radians(a)
  sa = math.sin(a)
  ca = math.cos(a)
  
  rot = []
  for p in corners:
    rx = p[0]*ca + p[1]*sa
    ry = -p[0]*sa + p[1]*ca
    rot.append((rx,ry))
  
  # Find the extrema of the rotated points
  rot = list(zip(*rot))
  rx0 = min(rot[0])
  rx1 = max(rot[0])
  ry0 = min(rot[1])
  ry1 = max(rot[1])

  #print('## RBB:', box, rot)
    
  return (rx0, ry0, rx1, ry1) 

# 需要导入模块: import math [as 别名]
# 或者: from math import cos [as 别名]
def update(self):
        radians = math.radians(self.direction)

        self.x += self.speed * math.sin(radians)
        self.y -= self.speed * math.cos(radians)

        # Update ball position
        self.rect.x = self.x
        self.rect.y = self.y

        if self.y <= self.top_edge:
            self.direction = (180-self.direction) % 360
            self.sound.edge_sound.play()
        if self.y > self.bottom_edge - 1*self.height:
            self.direction = (180-self.direction) % 360
            self.sound.edge_sound.play() 

# 需要导入模块: import math [as 别名]
# 或者: from math import cos [as 别名]
def _gaussian_noise_factor():
    """Return Gaussian noise of mean 0, std dev 1.

    Returns
    --------
    Float samples from Gaussian distribution.

    Examples
    --------
    >>> gaussian_noise_factor()
    1.43412557975
    >>> gaussian_noise_factor()
    -0.0410900866765

    """
    return math.sqrt(-2.0 * math.log(random())) * math.cos(2.0 * math.pi * random())


# Mutations

# @TODO: Implement all the common literal mutations.