Python math.sin() Examples

def step(self, amt=1):
        for i in range(self._size):
            y = math.sin(
                math.pi *
                float(self.cycles) *
                float(self._step * i) /
                float(self._size))

            if y >= 0.0:
                # Peaks of sine wave are white
                y = 1.0 - y  # Translate Y to 0.0 (top) to 1.0 (center)
                r, g, b = self.palette(0)
                c2 = (int(255 - float(255 - r) * y),
                      int(255 - float(255 - g) * y),
                      int(255 - float(255 - b) * y))
            else:
                # Troughs of sine wave are black
                y += 1.0  # Translate Y to 0.0 (bottom) to 1.0 (center)
                r, g, b = self.palette(0)
                c2 = (int(float(r) * y),
                      int(float(g) * y),
                      int(float(b) * y))
            self.layout.set(self._start + i, c2)

        self._step += amt 

def step(self, amt=1):
        for i in range(self._size):
            y = math.sin((math.pi * float(self.cycles) * float(i) / float(self._size)) + self._moveStep)

            if y >= 0.0:
                # Peaks of sine wave are white
                y = 1.0 - y  # Translate Y to 0.0 (top) to 1.0 (center)
                r, g, b = self.palette(0)
                c2 = (int(255 - float(255 - r) * y), int(255 - float(255 - g) * y), int(255 - float(255 - b) * y))
            else:
                # Troughs of sine wave are black
                y += 1.0  # Translate Y to 0.0 (bottom) to 1.0 (center)
                r, g, b = self.palette(0)
                c2 = (int(float(r) * y),
                      int(float(g) * y),
                      int(float(b) * y))
            self.layout.set(self._start + i, c2)

        self._moveStep += amt
        self._moveStep += 1
        if(self._moveStep >= self._size):
            self._moveStep = 0 

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) 

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 

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 

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 

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 

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]]) 

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,
    ) 

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 

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 

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 

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) 

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() 

def latlngup_to_ecef(l):
    "Converts L from WGS84 lat/long/up to ECEF"
    
    lat = dtor(l[0])
    lng = dtor(l[1])
    alt = l[2]

    slat = math.sin(lat)
    slng = math.sin(lng)
    clat = math.cos(lat)
    clng = math.cos(lng)

    d = math.sqrt(1 - (slat * slat * WGS84_ECC_SQ))
    rn = WGS84_A / d

    x = (rn + alt) * clat * clng
    y = (rn + alt) * clat * slng
    z = (rn * (1 - WGS84_ECC_SQ) + alt) * slat

    return (x,y,z) 

def latlngup_to_ecef(l):
    "Converts L from WGS84 lat/long/up to ECEF"
    
    lat = dtor(l[0])
    lng = dtor(l[1])
    alt = l[2]

    slat = math.sin(lat)
    slng = math.sin(lng)
    clat = math.cos(lat)
    clng = math.cos(lng)

    d = math.sqrt(1 - (slat * slat * WGS84_ECC_SQ))
    rn = WGS84_A / d

    x = (rn + alt) * clat * clng
    y = (rn + alt) * clat * slng
    z = (rn * (1 - WGS84_ECC_SQ) + alt) * slat

    return (x,y,z) 

def bandreject_biquad(
        waveform: Tensor,
        sample_rate: int,
        central_freq: float,
        Q: float = 0.707
) -> Tensor:
    r"""Design two-pole band-reject filter.  Similar to SoX implementation.

    Args:
        waveform (Tensor): audio waveform of dimension of `(..., time)`
        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
        central_freq (float): central frequency (in Hz)
        Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)

    Returns:
        Tensor: Waveform of dimension of `(..., time)`

    References:
        http://sox.sourceforge.net/sox.html
        https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
    """
    w0 = 2 * math.pi * central_freq / sample_rate
    alpha = math.sin(w0) / 2 / Q

    b0 = 1.
    b1 = -2 * math.cos(w0)
    b2 = 1.
    a0 = 1 + alpha
    a1 = -2 * math.cos(w0)
    a2 = 1 - alpha
    return biquad(waveform, b0, b1, b2, a0, a1, a2) 

def bandpass_biquad(
        waveform: Tensor,
        sample_rate: int,
        central_freq: float,
        Q: float = 0.707,
        const_skirt_gain: bool = False
) -> Tensor:
    r"""Design two-pole band-pass filter.  Similar to SoX implementation.

    Args:
        waveform (Tensor): audio waveform of dimension of `(..., time)`
        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
        central_freq (float): central frequency (in Hz)
        Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)
        const_skirt_gain (bool, optional) : If ``True``, uses a constant skirt gain (peak gain = Q).
            If ``False``, uses a constant 0dB peak gain. (Default: ``False``)

    Returns:
        Tensor: Waveform of dimension of `(..., time)`

    References:
        http://sox.sourceforge.net/sox.html
        https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
    """
    w0 = 2 * math.pi * central_freq / sample_rate
    alpha = math.sin(w0) / 2 / Q

    temp = math.sin(w0) / 2 if const_skirt_gain else alpha
    b0 = temp
    b1 = 0.
    b2 = -temp
    a0 = 1 + alpha
    a1 = -2 * math.cos(w0)
    a2 = 1 - alpha
    return biquad(waveform, b0, b1, b2, a0, a1, a2) 

def treble_biquad(
        waveform: Tensor,
        sample_rate: int,
        gain: float,
        central_freq: float = 3000,
        Q: float = 0.707
) -> Tensor:
    r"""Design a treble tone-control effect.  Similar to SoX implementation.

    Args:
        waveform (Tensor): audio waveform of dimension of `(..., time)`
        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
        gain (float): desired gain at the boost (or attenuation) in dB.
        central_freq (float, optional): central frequency (in Hz). (Default: ``3000``)
        Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``).

    Returns:
        Tensor: Waveform of dimension of `(..., time)`

    References:
        http://sox.sourceforge.net/sox.html
        https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
    """
    w0 = 2 * math.pi * central_freq / sample_rate
    alpha = math.sin(w0) / 2 / Q
    A = math.exp(gain / 40 * math.log(10))

    temp1 = 2 * math.sqrt(A) * alpha
    temp2 = (A - 1) * math.cos(w0)
    temp3 = (A + 1) * math.cos(w0)

    b0 = A * ((A + 1) + temp2 + temp1)
    b1 = -2 * A * ((A - 1) + temp3)
    b2 = A * ((A + 1) + temp2 - temp1)
    a0 = (A + 1) - temp2 + temp1
    a1 = 2 * ((A - 1) - temp3)
    a2 = (A + 1) - temp2 - temp1

    return biquad(waveform, b0, b1, b2, a0, a1, a2) 

def lowpass_biquad(
        waveform: Tensor,
        sample_rate: int,
        cutoff_freq: float,
        Q: float = 0.707
) -> Tensor:
    r"""Design biquad lowpass filter and perform filtering.  Similar to SoX implementation.

    Args:
        waveform (torch.Tensor): audio waveform of dimension of `(..., time)`
        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
        cutoff_freq (float): filter cutoff frequency
        Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)

    Returns:
        Tensor: Waveform of dimension of `(..., time)`
    """
    w0 = 2 * math.pi * cutoff_freq / sample_rate
    alpha = math.sin(w0) / 2 / Q

    b0 = (1 - math.cos(w0)) / 2
    b1 = 1 - math.cos(w0)
    b2 = b0
    a0 = 1 + alpha
    a1 = -2 * math.cos(w0)
    a2 = 1 - alpha
    return biquad(waveform, b0, b1, b2, a0, a1, a2) 

def _compute_initial_pos(self, graph):
        _radius = 1
        _offset = 0
        n = len(graph)
        pos = {id: np.array([_radius * math.cos(theta - math.pi / 2) + _offset,
                             _radius * math.sin(theta - math.pi / 2) + _offset]
                            )
               for id, theta in enumerate(
            np.linspace(0, 2 * math.pi * (1 - 1 / float(n)), num=n))}
        return pos 

def haversine_distance(self, lat1, lon1, lat2, lon2):
        R = 6371.0088
        lat1 = math.radians(lat1)
        lon1 = math.radians(lon1)
        lat2 = math.radians(lat2)
        lon2 = math.radians(lon2)
        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.atan2(math.sqrt(a), math.sqrt(1 - a))
        distance = R * c
        return distance * 1000 

def allpass_biquad(
        waveform: Tensor,
        sample_rate: int,
        central_freq: float,
        Q: float = 0.707
) -> Tensor:
    r"""Design two-pole all-pass filter.  Similar to SoX implementation.

    Args:
        waveform(torch.Tensor): audio waveform of dimension of `(..., time)`
        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
        central_freq (float): central frequency (in Hz)
        Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)

    Returns:
        Tensor: Waveform of dimension of `(..., time)`

    References:
        http://sox.sourceforge.net/sox.html
        https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
    """
    w0 = 2 * math.pi * central_freq / sample_rate
    alpha = math.sin(w0) / 2 / Q

    b0 = 1 - alpha
    b1 = -2 * math.cos(w0)
    b2 = 1 + alpha
    a0 = 1 + alpha
    a1 = -2 * math.cos(w0)
    a2 = 1 - alpha
    return biquad(waveform, b0, b1, b2, a0, a1, a2) 

def bass_biquad(
        waveform: Tensor,
        sample_rate: int,
        gain: float,
        central_freq: float = 100,
        Q: float = 0.707
) -> Tensor:
    r"""Design a bass tone-control effect.  Similar to SoX implementation.

    Args:
        waveform (Tensor): audio waveform of dimension of `(..., time)`
        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
        gain (float): desired gain at the boost (or attenuation) in dB.
        central_freq (float, optional): central frequency (in Hz). (Default: ``100``)
        Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``).

    Returns:
        Tensor: Waveform of dimension of `(..., time)`

    References:
        http://sox.sourceforge.net/sox.html
        https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
    """
    w0 = 2 * math.pi * central_freq / sample_rate
    alpha = math.sin(w0) / 2 / Q
    A = math.exp(gain / 40 * math.log(10))

    temp1 = 2 * math.sqrt(A) * alpha
    temp2 = (A - 1) * math.cos(w0)
    temp3 = (A + 1) * math.cos(w0)

    b0 = A * ((A + 1) - temp2 + temp1)
    b1 = 2 * A * ((A - 1) - temp3)
    b2 = A * ((A + 1) - temp2 - temp1)
    a0 = (A + 1) + temp2 + temp1
    a1 = -2 * ((A - 1) + temp3)
    a2 = (A + 1) + temp2 - temp1

    return biquad(waveform, b0 / a0, b1 / a0, b2 / a0, a0 / a0, a1 / a0, a2 / a0) 

def contrast(
        waveform: Tensor,
        enhancement_amount: float = 75.
) -> Tensor:
    r"""Apply contrast effect.  Similar to SoX implementation.
    Comparable with compression, this effect modifies an audio signal to make it sound louder

    Args:
        waveform (Tensor): audio waveform of dimension of `(..., time)`
        enhancement_amount (float): controls the amount of the enhancement
            Allowed range of values for enhancement_amount : 0-100
            Note that enhancement_amount = 0 still gives a significant contrast enhancement

    Returns:
        Tensor: Waveform of dimension of `(..., time)`

    References:
        http://sox.sourceforge.net/sox.html
    """

    if not 0 <= enhancement_amount <= 100:
        raise ValueError("Allowed range of values for enhancement_amount : 0-100")

    contrast = enhancement_amount / 750.

    temp1 = waveform * (math.pi / 2)
    temp2 = contrast * torch.sin(temp1 * 4)
    output_waveform = torch.sin(temp1 + temp2)

    return output_waveform 

def hex1():
    result = QPolygonF()
    l = 0.5/cos30
    for i in range(6):
        a = i*tau/6 - tau/12
        result.append(QPointF(l*math.sin(a), -l*math.cos(a)))
    return result 

def _cell_polys():
    poly = QPolygonF()
    l = 0.46/cos30
    inner_poly = QPolygonF()
    il = 0.75*l
    for i in range(6):
        a = i*tau/6 - tau/12
        poly.append(QPointF(l*math.sin(a), -l*math.cos(a)))
        inner_poly.append(QPointF(il*math.sin(a), -il*math.cos(a)))
    return poly, inner_poly 

def rotate_x(self, deg):
        """Rotate mesh around x-axis

        :param float deg: Rotation angle (degree)
        :return:
        """
        rad = math.radians(deg)
        mat = numpy.array([
            [1, 0, 0, 0],
            [0, math.cos(rad), math.sin(rad), 0],
            [0, -math.sin(rad), math.cos(rad), 0],
            [0, 0, 0, 1]
        ])
        self.vectors = self.vectors.dot(mat)
        return self 

def rotate(self,pt,midpt,angle):
        import math
        cosTheta = math.cos(angle*math.pi/180.0)
        sinTheta = math.sin(angle*math.pi/180.0)
        px = pt[0] - midpt[0]
        py = pt[1] - midpt[1]
        px_ = px*cosTheta - py*sinTheta
        py_ = px*sinTheta + py*cosTheta
        return (px_+midpt[0],py_+midpt[1]) 

def rotate_y(self, deg):
        """Rotate mesh around y-axis

        :param float deg: Rotation angle (degree)
        """
        rad = math.radians(deg)
        mat = numpy.array([
            [math.cos(rad), 0, -math.sin(rad), 0],
            [0, 1, 0, 0],
            [math.sin(rad), 0, math.cos(rad), 0],
            [0, 0, 0, 1]
        ])
        self.vectors = self.vectors.dot(mat)
        return self