Python numpy.piecewise方法代码示例

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import piecewise [as 别名]
def quintic_spline(dx, dtype=np.float64):
    def inner(x):
        return 1 + x ** 3 / 12 * (-95 + 138 * x - 55 * x ** 2)

    def middle(x):
        return (x - 1) * (x - 2) / 24 * (-138 + 348 * x - 249 * x ** 2 + 55 * x ** 3)

    def outer(x):
        return (x - 2) * (x - 3) ** 2 / 24 * (-54 + 50 * x - 11 * x ** 2)

    window = np.arange(-3, 4)
    x = np.abs(dx - window)
    result = np.piecewise(
        x,
        [x <= 1, (x > 1) & (x <= 2), (x > 2) & (x <= 3)],
        [lambda x: inner(x), lambda x: middle(x), lambda x: outer(x)],
    )
    return result, window 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import piecewise [as 别名]
def B_0123(x, der=0):
    """A quadratic B-spline function B(x | 0, 1, 2, 3)."""
    x = np.atleast_1d(x)
    conds = [x < 1, (x > 1) & (x < 2), x > 2]
    if der == 0:
        funcs = [lambda x: x*x/2.,
                 lambda x: 3./4 - (x-3./2)**2,
                 lambda x: (3.-x)**2 / 2]
    elif der == 2:
        funcs = [lambda x: 1.,
                 lambda x: -2.,
                 lambda x: 1.]
    else:
        raise ValueError('never be here: der=%s' % der)
    pieces = np.piecewise(x, conds, funcs)
    return pieces 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import piecewise [as 别名]
def minimum(self, ndim):
        return -2.903534 * np.ones(ndim)


# class Simionescu(OptimizationTestFunction):
#
#     def __init__(self):
#         OptimizationTestFunction.__init__(self, mindim=2, maxdim=2, domain=np.array([-1.25, 1.25]))
#
#     @staticmethod
#     def function(x):
#         rt = 1
#         rs = 0.2
#         n = 8
#         return np.piecewise(x,
#                      [x[0]**2 + x[1]**2 <= (rt + rs*np.cos(n*np.arctan(x[0]/x[1])))**2,
#                       x[0]**2 + x[1]**2 > (rt + rs*np.cos(n*np.arctan(x[0]/x[1])))**2], [0.1*x[0]*x[1], 1])
#
#
#     def minimum(self, ndim):
#         assert ndim == 2
#         return -0.84852813*np.ones(ndim) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import piecewise [as 别名]
def get_random(self, size=None):
        """Draw a random number from the distribution.

        If size is not None but an integer N, return an array of N numbers.

        For the MultivariateNumericalDistribution, the PDF from which the
        random numbers are drawn is approximated to be piecewise constant in
        hypercubes around the points of the lattice spanned by the `xi`. A finer
        lattice spacing will lead to a smoother distribution of random numbers
        (but will also be slower).
        """

        if size is None:
            return self._get_random()
        else:
            return np.array([self._get_random() for i in range(size)]) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import piecewise [as 别名]
def get_frame(self,frame_data):

		# print(frame_data.size)
		frame = np.rec.array(None, dtype=[('value', np.float16),('valid', np.bool_)], shape=(self.height, self.width))
		frame.valid.fill(False)
		frame.value.fill(0.)
		# print(frame.size)

		for datum in np.nditer(frame_data, flags=['zerosize_ok']):
			# print(datum['y'])
			ts_val = datum['ts']
			f_data = frame[datum['y'], datum['x']]
			f_data.value += 1

		img = frame.value/20*255
		img = img.astype('uint8')
		# img = np.piecewise(img, [img <= 0, (img > 0) & (img < 255), img >= 255], [0, lambda x: x, 255])
		# cv2.normalize(img,img,0,255,cv2.NORM_L1)
		cv2.normalize(img,img,0,255,cv2.NORM_MINMAX)
		img = cv2.flip(img, 1)
		img = np.rot90(img)
		# cv2.imshow('img_f', img)
		# cv2.waitKey(0)
		return img 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import piecewise [as 别名]
def get_frame_negative(self,frame_data):

		# print(frame_data.size)
		frame = np.rec.array(None, dtype=[('value', np.float16),('valid', np.bool_)], shape=(self.height, self.width))
		frame.valid.fill(False)
		frame.value.fill(0.)
		# print(frame.size)

		for datum in np.nditer(frame_data):
			# print(datum['y'])
			ts_val = datum['ts']
			f_data = frame[datum['y'], datum['x']]
			if(datum['p']== False):
				f_data.value += 1

		img = frame.value/20*255
		img = img.astype('uint8')
		# img = np.piecewise(img, [img <= 0, (img > 0) & (img < 255), img >= 255], [0, lambda x: x, 255])
		cv2.normalize(img,img,0,255,cv2.NORM_MINMAX)
		img = cv2.flip(img, 1)
		img = np.rot90(img)
		# cv2.imshow('img_neg', img)
		# cv2.waitKey(0)
		return img 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import piecewise [as 别名]
def _SetSize(self):
    """Set minimum size (GB) necessary to achieve _iops level.

    Rating performance levels as of May 2017, sources found below.
    GCP: ratings from https://cloud.google.com/compute/docs/disks/. Storage can
          go as high as 64TB per disk but IOPS maxes out at 30,000 iops/disk
          or (1000GB).
    AWS: ratings from
          http://docs.aws.amazon.com/AWSEC2/latest/
          UserGuide/EBSVolumeTypes.html#EBSVolumeTypes_gp2. Storage can go as
          high as 16TiB per disk but IOPS maxes out at 10000 IOPS/volume size.
          Reference gives volume size in GiB so converted to GB below.
    """
    if self._provider == GCP:
      self._size = min(int(math.ceil(self._iops / 30.0)), 30000 / 30)
    elif self._provider == AWS:
      value = self._iops
      value = numpy.array(value)
      self._size = int(
          numpy.piecewise([value], [[value <= 100], [(value > 100) & (
              value <= 9999)], [value > 9999]], [
                  lambda x: int(math.ceil(1.07374)),
                  lambda x: int(math.ceil(3 * value)),
                  lambda x: int(math.ceil(3579.855))])) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import piecewise [as 别名]
def _SetCPUCount(self):
    """Set cpu count.

    GCP: ratings from
    https://cloud.google.com/compute/docs/disks/performance#ssd-pd-performance
    AWS: to achieve good performance on EBS, one needs to use an
    EBS-optimized VM instance, and the smallest VM instance that can be EBS
    optimized is *.large VM types (e.g., c4.large), those comes with 2 cores.
    ratings from
    http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/EBSOptimized.html

    """
    if self._provider == GCP:
      value = self._iops
      self._cpu_count = int(
          numpy.piecewise([value], [[value <= 15000], [
              (value > 15000) & (value <= 25000)
          ], [value > 25000]], [lambda x: 1, lambda x: 16, lambda x: 32]))
    elif self._provider == AWS:
      self._cpu_count = 2 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import piecewise [as 别名]
def B_012(x):
    """ A linear B-spline function B(x | 0, 1, 2)."""
    x = np.atleast_1d(x)
    return np.piecewise(x, [(x < 0) | (x > 2),
                            (x >= 0) & (x < 1),
                            (x >= 1) & (x <= 2)],
                           [lambda x: 0., lambda x: x, lambda x: 2.-x]) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import piecewise [as 别名]
def gaussian_square(times: np.ndarray, amp: complex, center: float, square_width: float,
                    sigma: float, zeroed_width: Optional[float] = None) -> np.ndarray:
    r"""Continuous gaussian square pulse.

    Args:
        times: Times to output pulse for.
        amp: Pulse amplitude.
        center: Center of the square pulse component.
        square_width: Width of the square pulse component.
        sigma: Standard deviation of Gaussian rise/fall portion of the pulse.
        zeroed_width: Subtract baseline of gaussian square pulse
            to enforce $\OmegaSquare(center \pm zeroed_width/2)=0$.

    Raises:
        PulseError: if zeroed_width is not compatible with square_width.
    """
    square_start = center-square_width/2
    square_stop = center+square_width/2
    if zeroed_width:
        if zeroed_width < square_width:
            raise PulseError("zeroed_width cannot be smaller than square_width.")
        gaussian_zeroed_width = zeroed_width-square_width
    else:
        gaussian_zeroed_width = None

    funclist = [functools.partial(gaussian, amp=amp, center=square_start, sigma=sigma,
                                  zeroed_width=gaussian_zeroed_width, rescale_amp=True),
                functools.partial(gaussian, amp=amp, center=square_stop, sigma=sigma,
                                  zeroed_width=gaussian_zeroed_width, rescale_amp=True),
                functools.partial(constant, amp=amp)]
    condlist = [times <= square_start, times >= square_stop]
    return np.piecewise(times.astype(np.complex_), condlist, funclist) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import piecewise [as 别名]
def backward_var(self, grad, index, **kwargs):
        a = self.variables[index]
        return grad * np.piecewise(
            a.data, [a.data < 0, a.data == 0, a.data > 0], [-1, np.nan, 1]
        ) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import piecewise [as 别名]
def backward_var(self, grad, index, **kwargs):
        a = self.variables[index]
        x = a.data
        return np.pi * grad * np.piecewise(x, [x == 0, x != 0], [np.zeros_like, _dsinc]) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import piecewise [as 别名]
def __call__(self, a):
        self.variables = (a,)
        return np.piecewise(
            a.data, [a.data == 0, a.data != 0], [np.pi / 2, lambda x: np.arctan(1 / x)]
        ) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import piecewise [as 别名]
def get_generator(self):
        return lambda x: numpy.piecewise(x, self.domain(x), [self._generators[j] for j in range(len(self._generators))] + [self.finals[-1]]) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import piecewise [as 别名]
def cdf(self, x):
        cdf0 = self.scipy_dist.cdf(0)
        cdf = (self.scipy_dist.cdf(x) - cdf0)/(1-cdf0)
        return np.piecewise(
                    np.asarray(x, dtype=float),
                    [x<0, x>=0],
                    [0., cdf]) # return 0 for negative x