Python numpy.abs() Examples

def _project_im_rois(im_rois, scales):
    """Project image RoIs into the image pyramid built by _get_image_blob.
    Arguments:
        im_rois (ndarray): R x 4 matrix of RoIs in original image coordinates
        scales (list): scale factors as returned by _get_image_blob
    Returns:
        rois (ndarray): R x 4 matrix of projected RoI coordinates
        levels (list): image pyramid levels used by each projected RoI
    """
    im_rois = im_rois.astype(np.float, copy=False)

    if len(scales) > 1:
        widths = im_rois[:, 2] - im_rois[:, 0] + 1
        heights = im_rois[:, 3] - im_rois[:, 1] + 1
        areas = widths * heights
        scaled_areas = areas[:, np.newaxis] * (scales[np.newaxis, :] ** 2)
        diff_areas = np.abs(scaled_areas - 224 * 224)
        levels = diff_areas.argmin(axis=1)[:, np.newaxis]
    else:
        levels = np.zeros((im_rois.shape[0], 1), dtype=np.int)

    rois = im_rois * scales[levels]

    return rois, levels 

def _project_im_rois(im_rois, scales):
    """Project image RoIs into the image pyramid built by _get_image_blob.
    Arguments:
        im_rois (ndarray): R x 4 matrix of RoIs in original image coordinates
        scales (list): scale factors as returned by _get_image_blob
    Returns:
        rois (ndarray): R x 4 matrix of projected RoI coordinates
        levels (list): image pyramid levels used by each projected RoI
    """
    im_rois = im_rois.astype(np.float, copy=False)

    if len(scales) > 1:
        widths = im_rois[:, 2] - im_rois[:, 0] + 1
        heights = im_rois[:, 3] - im_rois[:, 1] + 1
        areas = widths * heights
        scaled_areas = areas[:, np.newaxis] * (scales[np.newaxis, :] ** 2)
        diff_areas = np.abs(scaled_areas - 224 * 224)
        levels = diff_areas.argmin(axis=1)[:, np.newaxis]
    else:
        levels = np.zeros((im_rois.shape[0], 1), dtype=np.int)

    rois = im_rois * scales[levels]

    return rois, levels 

def get_audio_data(self):
        frames = self.rec.get_frames()
        result = [0] * self.bins
        if len(frames) > 0:
            # keeps only the last frame
            current_frame = frames[-1]
            # plots the time signal
            # self.line_top.set_data(self.time_vect, current_frame)
            # computes and plots the fft signal
            fft_frame = np.fft.rfft(current_frame)
            if self.auto_gain:
                fft_frame /= np.abs(fft_frame).max()
            else:
                fft_frame *= (1 + self.gain) / 5000000.

            fft_frame = np.abs(fft_frame)
            if self.log_scale:
                fft_frame = np.log10(np.add(1, np.multiply(10, fft_frame)))

            result = [min(int(max(i, 0.) * 1023), 1023) for i in fft_frame][0:self.bins]

        return result 

def get_audio_data(self):
        frames = self.rec.get_frames()
        result = [0] * self.bins
        if len(frames) > 0:
            # keeps only the last frame
            current_frame = frames[-1]
            # plots the time signal
            # self.line_top.set_data(self.time_vect, current_frame)
            # computes and plots the fft signal
            fft_frame = np.fft.rfft(current_frame)
            if self.auto_gain:
                fft_frame /= np.abs(fft_frame).max()
            else:
                fft_frame *= (1 + self.gain) / 5000000.

            fft_frame = np.abs(fft_frame)
            if self.log_scale:
                fft_frame = np.log10(np.add(1, np.multiply(10, fft_frame)))

            result = [min(int(max(i, 0.) * 1023), 1023) for i in fft_frame][0:self.bins]

        return result 

def main():
    data_foler = "data"
    wavs = [os.path.join(data_foler, file[:-4]) for file in os.listdir(data_foler) if file.endswith(".wav")]
    outputs_lws = [file + ".lws.gen.wav" for file in wavs]
    wavs = [audio.load_wav(wav_path + ".wav", hparams.sample_rate) for wav_path in wavs]

    lws_processor = lws.lws(512, 128, mode="speech")  # 512: window length; 128: window shift
    i = 0
    for x in wavs:
        X = lws_processor.stft(x)  # where x is a single-channel waveform
        X0 = np.abs(X)  # Magnitude spectrogram
        print('{:6}: {:5.2f} dB'.format('Abs(X)', lws_processor.get_consistency(X0)))
        X1 = lws_processor.run_lws(
            X0)  # reconstruction from magnitude (in general, one can reconstruct from an initial complex spectrogram)
        print(X1.shape)
        print('{:6}: {:5.2f} dB'.format('LWS', lws_processor.get_consistency(X1)))
        print(X1.shape)
        wav = lws_processor.istft(X1).astype(np.float32)

        audio.save_wav(wav, outputs_lws[i])
        i += 1 

def _extract(self, intervals, out, **kwargs):

        def find_closest(ldm, interval, use_strand=True):
            """Uses
            """
            # subset the positions to the appropriate strand
            # and extract the positions
            ldm_positions = ldm.loc[interval.chrom]
            if use_strand and interval.strand != ".":
                ldm_positions = ldm_positions.loc[interval.strand]
            ldm_positions = ldm_positions.position.values

            int_midpoint = (interval.end + interval.start) // 2
            dist = (ldm_positions - 1) - int_midpoint  # -1 for 0, 1 indexed positions
            if use_strand and interval.strand == "-":
                dist = - dist

            return dist[np.argmin(np.abs(dist))]

        out[:] = np.array([[find_closest(self.landmarks[ldm_name], interval, self.use_strand)
                            for ldm_name in self.columns]
                           for interval in intervals], dtype=float)

        return out 

def __init__(self, parallel, wave_len=254, wave_dif=64, buffer_size=5, loop_num=5, window=np.hanning(254)):
        self.wave_len = wave_len
        self.wave_dif = wave_dif
        self.buffer_size = buffer_size
        self.loop_num = loop_num
        self.parallel = parallel
        self.window = cp.array([window for _ in range(parallel)])

        self.wave_buf = cp.zeros((parallel, wave_len+wave_dif), dtype=float)
        self.overwrap_buf = cp.zeros((parallel, wave_dif*buffer_size+(wave_len-wave_dif)), dtype=float)
        self.spectrum_buffer = cp.ones((parallel, self.buffer_size, self.wave_len), dtype=complex)
        self.absolute_buffer = cp.ones((parallel, self.buffer_size, self.wave_len), dtype=complex)
        
        self.phase = cp.zeros((parallel, self.wave_len), dtype=complex)
        self.phase += cp.random.random((parallel, self.wave_len))-0.5 + cp.random.random((parallel, self.wave_len))*1j - 0.5j
        self.phase[self.phase == 0] = 1
        self.phase /= cp.abs(self.phase) 

def __init__(self, wave_len=254, wave_dif=64, buffer_size=5, loop_num=5, window=np.hanning(254)):
        self.wave_len = wave_len
        self.wave_dif = wave_dif
        self.buffer_size = buffer_size
        self.loop_num = loop_num
        self.window = window

        self.wave_buf = np.zeros(wave_len+wave_dif, dtype=float)
        self.overwrap_buf = np.zeros(wave_dif*buffer_size+(wave_len-wave_dif), dtype=float)
        self.spectrum_buffer = np.ones((self.buffer_size, self.wave_len), dtype=complex)
        self.absolute_buffer = np.ones((self.buffer_size, self.wave_len), dtype=complex)
        
        self.phase = np.zeros(self.wave_len, dtype=complex)
        self.phase += np.random.random(self.wave_len)-0.5 + np.random.random(self.wave_len)*1j - 0.5j
        self.phase[self.phase == 0] = 1
        self.phase /= np.abs(self.phase) 

def wave2input_image(wave, window, pos=0, pad=0):
    wave_image = np.hstack([wave[pos+i*sride:pos+(i+pad*2)*sride+dif].reshape(height+pad*2, sride) for i in range(256//sride)])[:,:254]
    wave_image *= window
    spectrum_image = np.fft.fft(wave_image, axis=1)
    input_image = np.abs(spectrum_image[:,:128].reshape(1, height+pad*2, 128), dtype=np.float32)

    np.clip(input_image, 1000, None, out=input_image)
    np.log(input_image, out=input_image)
    input_image += bias
    input_image /= scale

    if np.max(input_image) > 0.95:
        print('input image max bigger than 0.95', np.max(input_image))
    if np.min(input_image) < 0.05:
        print('input image min smaller than 0.05', np.min(input_image))

    return input_image 

def stft_magnitude(signal, fft_length,
                   hop_length=None,
                   window_length=None):
  """Calculate the short-time Fourier transform magnitude.

  Args:
    signal: 1D np.array of the input time-domain signal.
    fft_length: Size of the FFT to apply.
    hop_length: Advance (in samples) between each frame passed to FFT.
    window_length: Length of each block of samples to pass to FFT.

  Returns:
    2D np.array where each row contains the magnitudes of the fft_length/2+1
    unique values of the FFT for the corresponding frame of input samples.
  """
  frames = frame(signal, window_length, hop_length)
  # Apply frame window to each frame. We use a periodic Hann (cosine of period
  # window_length) instead of the symmetric Hann of np.hanning (period
  # window_length-1).
  window = periodic_hann(window_length)
  windowed_frames = frames * window
  return np.abs(np.fft.rfft(windowed_frames, int(fft_length)))


# Mel spectrum constants and functions. 

def jacobian(self, p, into=None):
        # transpose to be 3 x 2 x n
        p = np.transpose(np.reshape(p, (-1, 3, 2)), (1,2,0))
        # First, get the two legs...
        (dx_ab, dy_ab) = p[1] - p[0]
        (dx_ac, dy_ac) = p[2] - p[0]
        (dx_bc, dy_bc) = p[2] - p[1]
        # now, the area is half the z-value of the cross-product...
        sarea0 = 0.5 * (dx_ab*dy_ac - dx_ac*dy_ab)
        # but we want to abs it
        dsarea0 = np.sign(sarea0)
        z = np.transpose([[-dy_bc,dx_bc], [dy_ac,-dx_ac], [-dy_ab,dx_ab]], (2,0,1))
        z = times(0.5*dsarea0, z)
        m = numel(p)
        n = p.shape[2]
        ii = (np.arange(n) * np.ones([6, n])).T.flatten()
        z = sps.csr_matrix((z.flatten(), (ii, np.arange(len(ii)))), shape=(n, m))
        return safe_into(into, z) 

def is_integer(number, precision=10e-10):
    """
    Check if number is convertible to integer.

    :Parameters:
        #. number (str, number): Input number.
        #. precision (number): To avoid floating errors,
           a precision should be given.

    :Returns:
        #. result (bool): True if convertible, False otherwise.
    """
    if isinstance(number, (int, long)):
        return True
    try:
        number = float(number)
    except:
        return False
    else:
        if np.abs(number-int(number)) < precision:
            return True
        else:
            return False 

def update(self, labels, preds):
        """Updates the internal evaluation result.

        Parameters
        ----------
        labels : list of `NDArray`
            The labels of the data.

        preds : list of `NDArray`
            Predicted values.
        """
        labels, preds = check_label_shapes(labels, preds, True)

        for label, pred in zip(labels, preds):
            label = label.asnumpy()
            pred = pred.asnumpy()

            if len(label.shape) == 1:
                label = label.reshape(label.shape[0], 1)
            if len(pred.shape) == 1:
                pred = pred.reshape(pred.shape[0], 1)

            self.sum_metric += numpy.abs(label - pred).mean()
            self.num_inst += 1 # numpy.prod(label.shape) 

def test_quantize_float32_to_int8():
    shape = rand_shape_nd(4)
    data = rand_ndarray(shape, 'default', dtype='float32')
    min_range = mx.nd.min(data)
    max_range = mx.nd.max(data)
    qdata, min_val, max_val = mx.nd.contrib.quantize(data, min_range, max_range, out_type='int8')
    data_np = data.asnumpy()
    min_range = min_range.asscalar()
    max_range = max_range.asscalar()
    real_range = np.maximum(np.abs(min_range), np.abs(max_range))
    quantized_range = 127.0
    scale = quantized_range / real_range
    assert qdata.dtype == np.int8
    assert min_val.dtype == np.float32
    assert max_val.dtype == np.float32
    assert same(min_val.asscalar(), -real_range)
    assert same(max_val.asscalar(), real_range)
    qdata_np = (np.sign(data_np) * np.minimum(np.abs(data_np) * scale + 0.5, quantized_range)).astype(np.int8)
    assert_almost_equal(qdata.asnumpy(), qdata_np, atol = 1) 

def heuristic_fn_vec(n1, n2, n_ori, step_size):
  # n1 is a vector and n2 is a single point.
  dx = (n1[:,0] - n2[0,0])/step_size
  dy = (n1[:,1] - n2[0,1])/step_size
  dt = n1[:,2] - n2[0,2]
  dt = np.mod(dt, n_ori)
  dt = np.minimum(dt, n_ori-dt)

  if n_ori == 6:
    if dx*dy > 0:
      d = np.maximum(np.abs(dx), np.abs(dy))
    else:
      d = np.abs(dy-dx)
  elif n_ori == 4:
    d = np.abs(dx) + np.abs(dy)

  return (d + dt).reshape((-1,1)) 

def _reward(self):
    current_base_position = self.minitaur.GetBasePosition()
    forward_reward = current_base_position[0] - self._last_base_position[0]
    # Cap the forward reward if a cap is set.
    forward_reward = min(forward_reward, self._forward_reward_cap)
    # Penalty for sideways translation.
    drift_reward = -abs(current_base_position[1] - self._last_base_position[1])
    # Penalty for sideways rotation of the body.
    orientation = self.minitaur.GetBaseOrientation()
    rot_matrix = pybullet.getMatrixFromQuaternion(orientation)
    local_up_vec = rot_matrix[6:]
    shake_reward = -abs(np.dot(np.asarray([1, 1, 0]), np.asarray(local_up_vec)))
    energy_reward = -np.abs(
        np.dot(self.minitaur.GetMotorTorques(),
               self.minitaur.GetMotorVelocities())) * self._time_step
    objectives = [forward_reward, energy_reward, drift_reward, shake_reward]
    weighted_objectives = [
        o * w for o, w in zip(objectives, self._objective_weights)
    ]
    reward = sum(weighted_objectives)
    self._objectives.append(objectives)
    return reward 

def _reward(self):
    current_base_position = self.minitaur.GetBasePosition()
    forward_reward = current_base_position[0] - self._last_base_position[0]
    drift_reward = -abs(current_base_position[1] - self._last_base_position[1])
    shake_reward = -abs(current_base_position[2] - self._last_base_position[2])
    self._last_base_position = current_base_position
    energy_reward = np.abs(
        np.dot(self.minitaur.GetMotorTorques(),
               self.minitaur.GetMotorVelocities())) * self._time_step
    reward = (
        self._distance_weight * forward_reward -
        self._energy_weight * energy_reward + self._drift_weight * drift_reward
        + self._shake_weight * shake_reward)
    self._objectives.append(
        [forward_reward, energy_reward, drift_reward, shake_reward])
    return reward 

def _reward(self):
    current_base_position = self.minitaur.GetBasePosition()
    forward_reward = current_base_position[0] - self._last_base_position[0]
    drift_reward = -abs(current_base_position[1] - self._last_base_position[1])
    shake_reward = -abs(current_base_position[2] - self._last_base_position[2])
    self._last_base_position = current_base_position
    energy_reward = np.abs(
        np.dot(self.minitaur.GetMotorTorques(),
               self.minitaur.GetMotorVelocities())) * self._time_step
    reward = (
        self._distance_weight * forward_reward -
        self._energy_weight * energy_reward + self._drift_weight * drift_reward
        + self._shake_weight * shake_reward)
    self._objectives.append(
        [forward_reward, energy_reward, drift_reward, shake_reward])
    return reward 

def _step(self, a):
		assert (not self.scene.multiplayer)
		self.robot.apply_action(a)
		self.scene.global_step()

		state = self.robot.calc_state()  # sets self.to_target_vec

		potential_old = self.potential
		self.potential = self.robot.calc_potential()

		electricity_cost = (
			-0.10 * (np.abs(a[0] * self.robot.theta_dot) + np.abs(a[1] * self.robot.gamma_dot))  # work torque*angular_velocity
			- 0.01 * (np.abs(a[0]) + np.abs(a[1]))  # stall torque require some energy
		)
		stuck_joint_cost = -0.1 if np.abs(np.abs(self.robot.gamma) - 1) < 0.01 else 0.0
		self.rewards = [float(self.potential - potential_old), float(electricity_cost), float(stuck_joint_cost)]
		self.HUD(state, a, False)
		return state, sum(self.rewards), False, {} 

def db(audio):
    if len(audio.shape) > 1:
        maxx = np.max(np.abs(audio), axis=1)
        return 20 * np.log10(maxx) if np.any(maxx != 0) else np.array([0])
    maxx = np.max(np.abs(audio))
    return 20 * np.log10(maxx) if maxx != 0 else np.array([0]) 

def removeOutliers(data, m = 10.):
    """ a method of trimming outliers from a list/array using 
    outlier-safe methods of calculating the center and variance;
    only removes the upper tail, not the lower tail """
    if len(data) < 2:
        return data
        
    data = numpy.array(data)
    d_abs = numpy.abs(data - numpy.median(data))
    d = data - numpy.median(data)
    mdev = numpy.median(d_abs)
    s = d/mdev if mdev else 0.
    return data[s<m] 

def scoreInsertSize(self, isize):
        if not self.hasInsertSizeDistribution():
            return 0

        if self._insertSizeKDE is None:
            self._insertSizeKDE = gaussian_kde(self.insertSizes)

        # the gaussian kde call is pretty slow with ~50,000 data points in it, so we'll cache the result for a bit of a speed-up
        isize = abs(isize)
        if not isize in self._insertSizeScores:
            self._insertSizeScores[isize] = self._insertSizeKDE(isize)

        return self._insertSizeScores[isize] 

def _grid_sfc_area(lon, lat, lon_bounds=None, lat_bounds=None):
    """Calculate surface area of each grid cell in a lon-lat grid."""
    # Compute the bounds if not given.
    if lon_bounds is None:
        lon_bounds = _bounds_from_array(
            lon, internal_names.LON_STR, internal_names.LON_BOUNDS_STR)
    if lat_bounds is None:
        lat_bounds = _bounds_from_array(
            lat, internal_names.LAT_STR, internal_names.LAT_BOUNDS_STR)
    # Compute the surface area.
    dlon = _diff_bounds(utils.vertcoord.to_radians(lon_bounds, is_delta=True),
                        lon)
    sinlat_bounds = np.sin(utils.vertcoord.to_radians(lat_bounds,
                                                      is_delta=True))
    dsinlat = np.abs(_diff_bounds(sinlat_bounds, lat))
    sfc_area = dlon*dsinlat*(RADIUS_EARTH**2)
    # Rename the coordinates such that they match the actual lat / lon.
    try:
        sfc_area = sfc_area.rename(
            {internal_names.LAT_BOUNDS_STR: internal_names.LAT_STR,
             internal_names.LON_BOUNDS_STR: internal_names.LON_STR})
    except ValueError:
        pass
    # Clean up: correct names and dimension order.
    sfc_area = sfc_area.rename(internal_names.SFC_AREA_STR)
    sfc_area[internal_names.LAT_STR] = lat
    sfc_area[internal_names.LON_STR] = lon
    return sfc_area.transpose() 

def polar_distance(x1, x2):
    """
    Given two arrays of numbers x1 and x2, pairs the cells that are the
    closest and provides the pairing matrix index: x1(index(1,:)) should be as
    close as possible to x2(index(2,:)). The function outputs the average of 
    the absolute value of the differences abs(x1(index(1,:))-x2(index(2,:))).
    :param x1: vector 1
    :param x2: vector 2
    :return: d: minimum distance between d
             index: the permutation matrix
    """
    x1 = np.reshape(x1, (1, -1), order='F')
    x2 = np.reshape(x2, (1, -1), order='F')
    N1 = x1.size
    N2 = x2.size
    diffmat = np.arccos(np.cos(x1 - np.reshape(x2, (-1, 1), order='F')))
    min_N1_N2 = np.min([N1, N2])
    index = np.zeros((min_N1_N2, 2), dtype=int)
    if min_N1_N2 > 1:
        for k in range(min_N1_N2):
            d2 = np.min(diffmat, axis=0)
            index2 = np.argmin(diffmat, axis=0)
            index1 = np.argmin(d2)
            index2 = index2[index1]
            index[k, :] = [index1, index2]
            diffmat[index2, :] = float('inf')
            diffmat[:, index1] = float('inf')
        d = np.mean(np.arccos(np.cos(x1[:, index[:, 0]] - x2[:, index[:, 1]])))
    else:
        d = np.min(diffmat)
        index = np.argmin(diffmat)
        if N1 == 1:
            index = np.array([1, index])
        else:
            index = np.array([index, 1])
    return d, index 

def _lb2RN_mid(self, l, b):
        R = (np.abs(b) - 10.) / 0.6
        N = (np.mod(l, 360.) + 0.15) / 0.3 - 1
        return np.round(R).astype('i4'), np.round(N).astype('i4') 

def _clip(self, complex_iq, limit=1.0):
        # Clips amplitude to level
        clipped_samples = np.abs(complex_iq) > limit
        if np.any(clipped_samples):
            clipped = complex_iq
            clipped[clipped_samples] = complex_iq[clipped_samples] / np.abs(complex_iq[clipped_samples])
            warn('Some samples were clipped')
        else:
            clipped = complex_iq
        return clipped 

def griffin_lim(spectrogram):
    '''Applies Griffin-Lim's raw.'''
    X_best = copy.deepcopy(spectrogram)
    for i in range(hp.n_iter):
        X_t = invert_spectrogram(X_best)
        est = librosa.stft(X_t, hp.n_fft, hp.hop_length, win_length=hp.win_length)
        phase = est / np.maximum(1e-8, np.abs(est))
        X_best = spectrogram * phase
    X_t = invert_spectrogram(X_best)
    y = np.real(X_t)

    return y 

def _polygon_area(self, x, y):
        """Compute the area of a component of a polygon.

        Using the shoelace formula:
        https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates

        Args:
            x (ndarray): x coordinates of the component
            y (ndarray): y coordinates of the component

        Return:
            float: the are of the component
        """  # noqa: 501
        return 0.5 * np.abs(
            np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1))) 

def main(argv):
    # Set TF random seed to improve reproducibility
    tf.set_random_seed(1234)

    input_shape = [FLAGS.batch_size, 224, 224, 3]
    x_src = tf.abs(tf.random_uniform(input_shape, 0., 1.))
    x_guide = tf.abs(tf.random_uniform(input_shape, 0., 1.))
    print("Input shape:")
    print(input_shape)

    model = make_imagenet_cnn(input_shape)
    attack = FastFeatureAdversaries(model)
    attack_params = {'eps': 0.3, 'clip_min': 0., 'clip_max': 1.,
                     'nb_iter': FLAGS.nb_iter, 'eps_iter': 0.01,
                     'layer': FLAGS.layer}
    x_adv = attack.generate(x_src, x_guide, **attack_params)
    h_adv = model.fprop(x_adv)[FLAGS.layer]
    h_src = model.fprop(x_src)[FLAGS.layer]
    h_guide = model.fprop(x_guide)[FLAGS.layer]

    with tf.Session() as sess:
        init = tf.global_variables_initializer()
        sess.run(init)
        ha, hs, hg, xa, xs, xg = sess.run(
            [h_adv, h_src, h_guide, x_adv, x_src, x_guide])

        print("L2 distance between source and adversarial example `%s`: %.4f" %
              (FLAGS.layer, ((hs-ha)*(hs-ha)).sum()))
        print("L2 distance between guide and adversarial example `%s`: %.4f" %
              (FLAGS.layer, ((hg-ha)*(hg-ha)).sum()))
        print("L2 distance between source and guide `%s`: %.4f" %
              (FLAGS.layer, ((hg-hs)*(hg-hs)).sum()))
        print("Maximum perturbation: %.4f" % np.abs((xa-xs)).max())
        print("Original features: ")
        print(hs[:10, :10])
        print("Adversarial features: ")
        print(ha[:10, :10]) 

def save_wav(wav, path):
    wav *= 32767 / max(0.01, np.max(np.abs(wav)))
    wavfile.write(path, hparams.sample_rate, wav.astype(np.int16))