Python numpy.fabs() Examples

def absdiff(self, constant_value, separate_re_im=False):
        """
        Returns a ReportableQty that is the (element-wise in the vector case)
        difference between `constant_value` and this one given by:

        `abs(self - constant_value)`.
        """
        if separate_re_im:
            re_v = _np.fabs(_np.real(self.value) - _np.real(constant_value))
            im_v = _np.fabs(_np.imag(self.value) - _np.imag(constant_value))
            if self.has_eb():
                return (ReportableQty(re_v, _np.fabs(_np.real(self.errbar)), self.nonMarkovianEBs),
                        ReportableQty(im_v, _np.fabs(_np.imag(self.errbar)), self.nonMarkovianEBs))
            else:
                return ReportableQty(re_v), ReportableQty(im_v)

        else:
            v = _np.absolute(self.value - constant_value)
            if self.has_eb():
                return ReportableQty(v, _np.absolute(self.errbar), self.nonMarkovianEBs)
            else:
                return ReportableQty(v) 

def test_irreg_hf(self):
        idx = date_range('2012-6-22 21:59:51', freq='S', periods=100)
        df = DataFrame(np.random.randn(len(idx), 2), idx)

        irreg = df.iloc[[0, 1, 3, 4]]
        _, ax = self.plt.subplots()
        irreg.plot(ax=ax)
        diffs = Series(ax.get_lines()[0].get_xydata()[:, 0]).diff()

        sec = 1. / 24 / 60 / 60
        assert (np.fabs(diffs[1:] - [sec, sec * 2, sec]) < 1e-8).all()

        _, ax = self.plt.subplots()
        df2 = df.copy()
        df2.index = df.index.astype(object)
        df2.plot(ax=ax)
        diffs = Series(ax.get_lines()[0].get_xydata()[:, 0]).diff()
        assert (np.fabs(diffs[1:] - sec) < 1e-8).all() 

def rotipp(acceleration_x, time_step_x, acceleration_y, time_step_y, periods,
        percentile, damping=0.05, units="cm/s/s", method="Nigam-Jennings"):
    """
    Returns the rotationally independent spectrum RotIpp as defined by
    Boore (2010)
    """
    if np.fabs(time_step_x - time_step_y) > 1E-10:
        raise ValueError("Record pair must have the same time-step!")
    acceleration_x, acceleration_y = equalise_series(acceleration_x,
                                                     acceleration_y)
    target, rota, rotv, rotd, angles = rotdpp(acceleration_x, time_step_x,
                                              acceleration_y, time_step_y,
                                              periods, percentile, damping,
                                              units, method)
    locn, penalty = _get_gmrotd_penalty(
        np.hstack([target["PGA"],target["Pseudo-Acceleration"]]),
        rota)
    target_theta = np.radians(angles[locn])
    arotpp = acceleration_x * np.cos(target_theta) +\
        acceleration_y * np.sin(target_theta)
    spec = get_response_spectrum(arotpp, time_step_x, periods, damping, units,
        method)[0]
    spec["GMRot{:2.0f}".format(percentile)] = target
    return spec 

def nextPos(self, x, x_b, r1, r2, r3, r4, task):
		r"""Move individual to new position in search space.

		Args:
			x (numpy.ndarray): Individual represented with components.
			x_b (nmppy.ndarray): Best individual represented with components.
			r1 (float): Number dependent on algorithm iteration/generations.
			r2 (float): Random number in range of 0 and 2 * PI.
			r3 (float): Random number in range [Rmin, Rmax].
			r4 (float): Random number in range [0, 1].
			task (Task): Optimization task.

		Returns:
			numpy.ndarray: New individual that is moved based on individual ``x``.
		"""
		return task.repair(x + r1 * (sin(r2) if r4 < 0.5 else cos(r2)) * fabs(r3 * x_b - x), self.Rand) 

def rho(self, z):
        r"""
        The robust criterion function for Huber's t.

        Parameters
        ----------
        z : array-like
            1d array

        Returns
        -------
        rho : array
            rho(z) = .5*z**2            for \|z\| <= t

            rho(z) = \|z\|*t - .5*t**2    for \|z\| > t
        """
        z = np.asarray(z)
        test = self._subset(z)
        return (test * 0.5 * z**2 +
                (1 - test) * (np.fabs(z) * self.t - 0.5 * self.t**2)) 

def weights(self, z):
        """
        Huber's t weighting function for the IRLS algorithm

        The psi function scaled by z

        Parameters
        ----------
        z : array-like
            1d array

        Returns
        -------
        weights : array
            weights(z) = 1          for \|z\| <= t

            weights(z) = t/\|z\|      for \|z\| > t
        """
        z = np.asarray(z)
        test = self._subset(z)
        absz = np.fabs(z)
        absz[test] = 1.0
        return test + (1 - test) * self.t / absz 

def rho(self, z):
        r"""
        The robust criterion function for Ramsay's Ea.

        Parameters
        ----------
        z : array-like
            1d array

        Returns
        -------
        rho : array
            rho(z) = a**-2 * (1 - exp(-a*\|z\|)*(1 + a*\|z\|))
        """
        z = np.asarray(z)
        return (1 - np.exp(-self.a * np.fabs(z)) *
                (1 + self.a * np.fabs(z))) / self.a**2 

def psi(self, z):
        """
        The psi function for Ramsay's Ea estimator

        The analytic derivative of rho

        Parameters
        ----------
        z : array-like
            1d array

        Returns
        -------
        psi : array
            psi(z) = z*exp(-a*\|z\|)
        """
        z = np.asarray(z)
        return z * np.exp(-self.a * np.fabs(z)) 

def weights(self, z):
        """
        Ramsay's Ea weighting function for the IRLS algorithm

        The psi function scaled by z

        Parameters
        ----------
        z : array-like
            1d array

        Returns
        -------
        weights : array
            weights(z) = exp(-a*\|z\|)
        """

        z = np.asarray(z)
        return np.exp(-self.a * np.fabs(z)) 

def __call__(self, df_resid, nobs, resid):
        h = (df_resid)/nobs*(self.d**2 + (1-self.d**2)*\
                    Gaussian.cdf(self.d)-.5 - self.d/(np.sqrt(2*np.pi))*\
                    np.exp(-.5*self.d**2))
        s = mad(resid)
        subset = lambda x: np.less(np.fabs(resid/x),self.d)
        chi = lambda s: subset(s)*(resid/s)**2/2+(1-subset(s))*(self.d**2/2)
        scalehist = [np.inf,s]
        niter = 1
        while (np.abs(scalehist[niter-1] - scalehist[niter])>self.tol \
                and niter < self.maxiter):
            nscale = np.sqrt(1/(nobs*h)*np.sum(chi(scalehist[-1]))*\
                    scalehist[-1]**2)
            scalehist.append(nscale)
            niter += 1
            #if niter == self.maxiter:
            #    raise ValueError("Huber's scale failed to converge")
        return scalehist[-1] 

def cont(self, ML=False, rtol=1.0e-05, params_rtol=1e-5, params_atol=1e-4):
        '''convergence check for iterative estimation

        '''

        self.dev, old = self.deviance(ML=ML), self.dev

        #self.history.append(np.hstack((self.dev, self.a)))
        self.history['llf'].append(self.dev)
        self.history['params'].append(self.a.copy())
        self.history['D'].append(self.D.copy())

        if np.fabs((self.dev - old) / self.dev) < rtol:   #why is there times `*`?
            #print np.fabs((self.dev - old)), self.dev, old
            self.termination = 'llf'
            return False

        #break if parameters converged
        #TODO: check termination conditions, OR or AND
        if np.all(np.abs(self.a - self._a_old) < (params_rtol * self.a + params_atol)):
            self.termination = 'params'
            return False

        self._a_old =  self.a.copy()
        return True 

def test_irreg_hf(self):
        idx = date_range('2012-6-22 21:59:51', freq='S', periods=100)
        df = DataFrame(np.random.randn(len(idx), 2), idx)

        irreg = df.iloc[[0, 1, 3, 4]]
        _, ax = self.plt.subplots()
        irreg.plot(ax=ax)
        diffs = Series(ax.get_lines()[0].get_xydata()[:, 0]).diff()

        sec = 1. / 24 / 60 / 60
        assert (np.fabs(diffs[1:] - [sec, sec * 2, sec]) < 1e-8).all()

        _, ax = self.plt.subplots()
        df2 = df.copy()
        df2.index = df.index.astype(object)
        df2.plot(ax=ax)
        diffs = Series(ax.get_lines()[0].get_xydata()[:, 0]).diff()
        assert (np.fabs(diffs[1:] - sec) < 1e-8).all() 

def _wrap_results(result, dtype):
    """ wrap our results if needed """

    if is_datetime64_dtype(dtype):
        if not isinstance(result, np.ndarray):
            result = tslib.Timestamp(result)
        else:
            result = result.view(dtype)
    elif is_timedelta64_dtype(dtype):
        if not isinstance(result, np.ndarray):

            # raise if we have a timedelta64[ns] which is too large
            if np.fabs(result) > _int64_max:
                raise ValueError("overflow in timedelta operation")

            result = tslib.Timedelta(result, unit='ns')
        else:
            result = result.astype('i8').view(dtype)

    return result 

def lidar_to_img(points, img_size):
    # pdb.set_trace()
    lidar_data = np.array(points[:, :2])
    lidar_data *= 9.9999
    lidar_data -= (0.5 * img_size, 0.5 * img_size)
    lidar_data = np.fabs(lidar_data)
    lidar_data = lidar_data.astype(np.int32)
    lidar_data = np.reshape(lidar_data, (-1, 2))
    lidar_img = np.zeros((img_size, img_size))
    lidar_img[tuple(lidar_data.T)] = 255
    return torch.tensor(lidar_img).cuda()


# def lidar_to_img(points, img_size):
#     # pdb.set_trace()
#     lidar_data = points[:, :2]
#     lidar_data *= 9.9999
#     lidar_data -= torch.tensor((0.5 * img_size, 0.5 * img_size)).cuda()
#     lidar_data = torch.abs(lidar_data)
#     lidar_data = torch.floor(lidar_data).long()
#     lidar_data = lidar_data.view(-1, 2)
#     lidar_img = torch.zeros((img_size, img_size)).cuda()
#     lidar_img[lidar_data.permute(1,0)] = 255
#     return lidar_img 

def lidar_to_heightmap(points, img_size):
    # pdb.set_trace()
    lidar_data = np.array(points[:, :2])
    height_data = np.array(points[:,2])
    height_data *= 255/2
    height_data[height_data < 0] = 0
    height_data[height_data > 255] = 255
    height_data = np.fabs(height_data)
    height_data = height_data.astype(np.int32)


    lidar_data *= 9.9999
    lidar_data -= (0.5 * img_size, 0.5 * img_size)
    lidar_data = np.fabs(lidar_data)
    lidar_data = lidar_data.astype(np.int32)
    lidar_data = np.reshape(lidar_data, (-1, 2))
    lidar_img = np.zeros((img_size, img_size))
    lidar_img[tuple(lidar_data.T)] = height_data # TODO: sort the point wrt height first lex sort
    return lidar_img 

def _max_error(arrays1, arrays2):
  """Computes maximum elementwise gap between two lists of ndarrays.

  Computes the maximum elementwise gap between two lists with the same length,
  of arrays with the same shape.

  Args:
    arrays1: a lists of np.ndarrays.
    arrays2: a lists of np.ndarrays of the same shape as arrays1.

  Returns:
    The maximum elementwise absolute difference between the two lists of arrays.
  """
  error = 0
  for array1, array2 in zip(arrays1, arrays2):
    if array1.size or array2.size:  # Handle zero size ndarrays correctly
      error = np.maximum(error, np.fabs(array1 - array2).max())
  return error 

def test_irreg_hf(self):
        import matplotlib.pyplot as plt
        fig = plt.gcf()
        plt.clf()
        fig.add_subplot(111)

        idx = date_range('2012-6-22 21:59:51', freq='S', periods=100)
        df = DataFrame(np.random.randn(len(idx), 2), idx)

        irreg = df.ix[[0, 1, 3, 4]]
        ax = irreg.plot()
        diffs = Series(ax.get_lines()[0].get_xydata()[:, 0]).diff()

        sec = 1. / 24 / 60 / 60
        self.assert_((np.fabs(diffs[1:] - [sec, sec * 2, sec]) < 1e-8).all())

        plt.clf()
        fig.add_subplot(111)
        df2 = df.copy()
        df2.index = df.index.asobject
        ax = df2.plot()
        diffs = Series(ax.get_lines()[0].get_xydata()[:, 0]).diff()
        self.assert_((np.fabs(diffs[1:] - sec) < 1e-8).all()) 

def find_right_intersect(vec, target_val, start_index=0):
        nearest_index = start_index
        next_index = start_index

        size = len(vec) - 1
        if next_index == size:
            return size

        next_val = vec[next_index]
        best_distance = np.abs(next_val - target_val)
        while (next_index < size):
            next_index += 1
            next_val = vec[next_index]
            dist = np.fabs(next_val - target_val)  # pylint: disable=assignment-from-no-return
            if dist < best_distance:
                best_distance = dist
                nearest_index = next_index
            if next_index == size or next_val < target_val:
                break
        return nearest_index 

def __init__(self, interval, level=logging.DEBUG, stat=None):
        self.interval = interval
        self.level = level
        if stat is None:
            def mean_abs(x):
                return np.fabs(x).mean()
            self.stat = mean_abs
        else:
            self.stat = stat 

def __init__(self, interval, level=logging.DEBUG, stat=None):
        self.interval = interval
        self.level = level
        if stat is None:
            def mean_abs(x):
                return np.fabs(x).mean()
            self.stat = mean_abs
        else:
            self.stat = stat 

def evaluate_params(evaluateFunc, params, objectiveParams, urdfRoot='', timeStep=0.01, maxNumSteps=10000, sleepTime=0):
  print('start evaluation')
  beforeTime = time.time()
  p.resetSimulation()

  p.setTimeStep(timeStep)
  p.loadURDF("%s/plane.urdf" % urdfRoot)
  p.setGravity(0,0,-10)

  global minitaur
  minitaur = Minitaur(urdfRoot)
  start_position = current_position()
  last_position = None  # for tracing line
  total_energy = 0

  for i in range(maxNumSteps):
    torques = minitaur.getMotorTorques()
    velocities = minitaur.getMotorVelocities()
    total_energy += np.dot(np.fabs(torques), np.fabs(velocities)) * timeStep

    joint_values = evaluate_func_map[evaluateFunc](i, params)
    minitaur.applyAction(joint_values)
    p.stepSimulation()
    if (is_fallen()):
      break

    if i % 100 == 0:
      sys.stdout.write('.')
      sys.stdout.flush()
    time.sleep(sleepTime)

  print(' ')

  alpha = objectiveParams[0]
  final_distance = np.linalg.norm(start_position - current_position())
  finalReturn = final_distance - alpha * total_energy
  elapsedTime = time.time() - beforeTime
  print ("trial for ", params, " final_distance", final_distance, "total_energy", total_energy, "finalReturn", finalReturn, "elapsed_time", elapsedTime)
  return finalReturn 

def _transformation_shift_linear(value, shift=0.35):
    return correct_to_01(np.fabs(value - shift) / np.fabs(np.floor(shift - value) + shift)) 

def _transformation_shift_deceptive(y, A=0.35, B=0.005, C=0.05):
    tmp1 = np.floor(y - A + B) * (1.0 - C + (A - B) / B) / (A - B)
    tmp2 = np.floor(A + B - y) * (1.0 - C + (1.0 - A - B) / B) / (1.0 - A - B)
    ret = 1.0 + (np.fabs(y - A) - B) * (tmp1 + tmp2 + 1.0 / B)
    return correct_to_01(ret) 

def _transformation_shift_multi_modal(y, A, B, C):
    tmp1 = np.fabs(y - C) / (2.0 * (np.floor(C - y) + C))
    tmp2 = (4.0 * A + 2.0) * np.pi * (0.5 - tmp1)
    ret = (1.0 + np.cos(tmp2) + 4.0 * B * np.power(tmp1, 2.0)) / (B + 2.0)
    return correct_to_01(ret) 

def _transformation_param_dependent(y, y_deg, A=0.98 / 49.98, B=0.02, C=50.0):
    aux = A - (1.0 - 2.0 * y_deg) * np.fabs(np.floor(0.5 - y_deg) + A)
    ret = np.power(y, B + (C - B) * aux)
    return correct_to_01(ret) 

def _transformation_param_deceptive(y, A=0.35, B=0.001, C=0.05):
    tmp1 = np.floor(y - A + B) * (1.0 - C + (A - B) / B) / (A - B)
    tmp2 = np.floor(A + B - y) * (1.0 - C + (1.0 - A - B) / B) / (1.0 - A - B)
    ret = 1.0 + (np.fabs(y - A) - B) * (tmp1 + tmp2 + 1.0 / B)
    return correct_to_01(ret)


# ---------------------------------------------------------------------------------------------------------
# REDUCTION
# --------------------------------------------------------------------------------------------------------- 

def test_replace2(self):
        N = 100
        ser = pd.Series(np.fabs(np.random.randn(N)), tm.makeDateIndex(N),
                        dtype=object)
        ser[:5] = np.nan
        ser[6:10] = 'foo'
        ser[20:30] = 'bar'

        # replace list with a single value
        rs = ser.replace([np.nan, 'foo', 'bar'], -1)

        assert (rs[:5] == -1).all()
        assert (rs[6:10] == -1).all()
        assert (rs[20:30] == -1).all()
        assert (pd.isna(ser[:5])).all()

        # replace with different values
        rs = ser.replace({np.nan: -1, 'foo': -2, 'bar': -3})

        assert (rs[:5] == -1).all()
        assert (rs[6:10] == -2).all()
        assert (rs[20:30] == -3).all()
        assert (pd.isna(ser[:5])).all()

        # replace with different values with 2 lists
        rs2 = ser.replace([np.nan, 'foo', 'bar'], [-1, -2, -3])
        tm.assert_series_equal(rs, rs2)

        # replace inplace
        ser.replace([np.nan, 'foo', 'bar'], -1, inplace=True)
        assert (ser[:5] == -1).all()
        assert (ser[6:10] == -1).all()
        assert (ser[20:30] == -1).all() 

def _wrap_results(result, dtype, fill_value=None):
    """ wrap our results if needed """

    if is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype):
        if fill_value is None:
            # GH#24293
            fill_value = iNaT
        if not isinstance(result, np.ndarray):
            tz = getattr(dtype, 'tz', None)
            assert not isna(fill_value), "Expected non-null fill_value"
            if result == fill_value:
                result = np.nan
            result = tslibs.Timestamp(result, tz=tz)
        else:
            result = result.view(dtype)
    elif is_timedelta64_dtype(dtype):
        if not isinstance(result, np.ndarray):
            if result == fill_value:
                result = np.nan

            # raise if we have a timedelta64[ns] which is too large
            if np.fabs(result) > _int64_max:
                raise ValueError("overflow in timedelta operation")

            result = tslibs.Timedelta(result, unit='ns')
        else:
            result = result.astype('i8').view(dtype)

    return result 

def wideTurn(self, orig_seq, modif_seq):
        '''
        We have to select three points in order to form a triangle
        These three points should be far enough from each other to have
        a reliable estimation of the orientation of the current turn
        '''
        dmin_tri = self.line_width / 2.0
        ibeg = 0
        iend = 2
        for i in range(1, len(orig_seq) - 1):
            dist_from_point = ((orig_seq[i] - orig_seq[i+1:]) ** 2).sum(1)
            if np.amax(dist_from_point) < dmin_tri * dmin_tri:
                continue
            iend = i + 1 + np.argmax(dist_from_point >= dmin_tri * dmin_tri)
            dist_from_point = ((orig_seq[i] - orig_seq[i-1::-1]) ** 2).sum(1)
            if np.amax(dist_from_point) < dmin_tri * dmin_tri:
                continue
            ibeg = i - 1 - np.argmax(dist_from_point >= dmin_tri * dmin_tri)
            length_base = ((orig_seq[iend] - orig_seq[ibeg]) ** 2).sum(0)
            relpos = ((orig_seq[i] - orig_seq[ibeg]) * (orig_seq[iend] - orig_seq[ibeg])).sum(0)
            if np.fabs(relpos) < 1000.0 * np.fabs(length_base):
                relpos /= length_base
            else:
                relpos = 0.5
            projection = orig_seq[ibeg] + relpos * (orig_seq[iend] - orig_seq[ibeg])
            dist_from_proj = np.sqrt(((projection - orig_seq[i]) ** 2).sum(0))
            if dist_from_proj > 0.001:
                modif_seq[i] = (orig_seq[i] - (self.wc_stretch / dist_from_proj)
                                * (projection - orig_seq[i]))
        return 

def __init__(self, magnitude, dip, aspect,
                 tectonic_region='Active Shallow Crust', rake=0., ztor=0.,
                 strike=0., msr=WC1994(), initial_point=DEFAULT_POINT,
                 hypocentre_location=None):
        """
        Instantiate the rupture - requires a minimum of a magnitude, dip
        and aspect ratio
        """
        self.magnitude = magnitude
        self.dip = dip
        self.aspect = aspect
        self.rake = rake
        self.strike = strike
        self.location = initial_point
        self.ztor = ztor
        self.trt = tectonic_region
        self.hypo_loc = hypocentre_location
        # If the top of rupture depth in the initial
        if fabs(self.location.depth - self.ztor) > 1E-9:
            self.location.depth = ztor
        self.msr = msr
        self.area = self.msr.get_median_area(self.magnitude, self.rake)
        self.surface = create_planar_surface(self.location,
                                             self.strike,
                                             self.dip,
                                             self.area,
                                             self.aspect)
        self.hypocentre = get_hypocentre_on_planar_surface(self.surface,
                                                           self.hypo_loc)
        self.rupture = self.get_rupture()
        self.target_sites_config = None
        self.target_sites = None