Python numpy.log2方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log2 [as 別名]
def fit_loglog(x, y):
    """
    Fit a line to isotropic spectra in log-log space

    Parameters
    ----------
    x : `numpy.array`
        Coordinate of the data
    y : `numpy.array`
        data

    Returns
    -------
    y_fit : `numpy.array`
        The linear fit
    a : float64
        Slope of the fit
    b : float64
        Intercept of the fit
    """
    # fig log vs log
    p = np.polyfit(np.log2(x), np.log2(y), 1)
    y_fit = 2**(np.log2(x)*p[0] + p[1])

    return y_fit, p[0], p[1] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log2 [as 別名]
def add_image(self, img):
        if self.print_progress and self.cur_images % self.progress_interval == 0:
            print('%d / %d\r' % (self.cur_images, self.expected_images), end='', flush=True)
            sys.stdout.flush()
        if self.shape is None:
            self.shape = img.shape
            self.resolution_log2 = int(np.log2(self.shape[1]))
            assert self.shape[0] in [1, 3]
            assert self.shape[1] == self.shape[2]
            assert self.shape[1] == 2**self.resolution_log2
            tfr_opt = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.NONE)
            for lod in range(self.resolution_log2 - 1):
                tfr_file = self.tfr_prefix + '-r%02d.tfrecords' % (self.resolution_log2 - lod)
                self.tfr_writers.append(tf.python_io.TFRecordWriter(tfr_file, tfr_opt))
        assert img.shape == self.shape
        for lod, tfr_writer in enumerate(self.tfr_writers):
            if lod:
                img = img.astype(np.float32)
                img = (img[:, 0::2, 0::2] + img[:, 0::2, 1::2] + img[:, 1::2, 0::2] + img[:, 1::2, 1::2]) * 0.25
            quant = np.rint(img).clip(0, 255).astype(np.uint8)
            ex = tf.train.Example(features=tf.train.Features(feature={
                'shape': tf.train.Feature(int64_list=tf.train.Int64List(value=quant.shape)),
                'data': tf.train.Feature(bytes_list=tf.train.BytesList(value=[quant.tostring()]))}))
            tfr_writer.write(ex.SerializeToString())
        self.cur_images += 1 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log2 [as 別名]
def __init__(self, resolution=1024, num_channels=3, dtype='uint8', dynamic_range=[0,255], label_size=0, label_dtype='float32'):
        self.resolution         = resolution
        self.resolution_log2    = int(np.log2(resolution))
        self.shape              = [num_channels, resolution, resolution]
        self.dtype              = dtype
        self.dynamic_range      = dynamic_range
        self.label_size         = label_size
        self.label_dtype        = label_dtype
        self._tf_minibatch_var  = None
        self._tf_lod_var        = None
        self._tf_minibatch_np   = None
        self._tf_labels_np      = None

        assert self.resolution == 2 ** self.resolution_log2
        with tf.name_scope('Dataset'):
            self._tf_minibatch_var = tf.Variable(np.int32(0), name='minibatch_var')
            self._tf_lod_var = tf.Variable(np.int32(0), name='lod_var') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log2 [as 別名]
def predict_on_batch(self, inputs):
            if inputs.shape == (2,):
                inputs = inputs[np.newaxis, :]
            # Encode
            max_len = len(max(inputs, key=len))
            one_hot_ref =  self.encode(inputs[:,0])
            one_hot_alt = self.encode(inputs[:,1])
            # Construct dummy library indicator
            indicator = np.zeros((inputs.shape[0],2))
            indicator[:,1] = 1
            # Compute fold change for all three frames
            fc_changes = []
            for shift in range(3):
                if shift > 0:
                    shifter = np.zeros((one_hot_ref.shape[0],1,4))
                    one_hot_ref = np.concatenate([one_hot_ref, shifter], axis=1)
                    one_hot_alt = np.concatenate([one_hot_alt, shifter], axis=1)
                pred_ref = self.model.predict_on_batch([one_hot_ref, indicator]).reshape(-1)
                pred_variant = self.model.predict_on_batch([one_hot_alt, indicator]).reshape(-1)
                fc_changes.append(np.log2(pred_variant/pred_ref))
            # Return
            return {"mrl_fold_change":fc_changes[0], 
                    "shift_1":fc_changes[1],
                    "shift_2":fc_changes[2]} 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log2 [as 別名]
def multinomLog2(selectors):
    """
    Function calculates logarithm 2 of a kind of multinom.

    selectors: list of integers
    """

    ln2 = 0.69314718055994528622
    noAll = sum(selectors)
    lgNf = math.lgamma(noAll + 1.0) / ln2  # log2(N!)

    lgnFac = []
    for selector in selectors:
        if selector == 0 or selector == 1:
            lgnFac.append(0.0)
        elif selector == 2:
            lgnFac.append(1.0)
        elif selector == noAll:
            lgnFac.append(lgNf)
        else:
            lgnFac.append(math.lgamma(selector + 1.0) / ln2)
    return lgNf - sum(lgnFac) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log2 [as 別名]
def _check_beta_prior(beta_prior, nchoices, for_ucb=False):
    if beta_prior == 'auto':
        if not for_ucb:
            out = ( (2.0 / np.log2(nchoices), 4.0), 2 )
        else:
            out = ( (3.0 / np.log2(nchoices), 4.0), 2 )
    elif beta_prior is None:
        out = ((1.0,1.0), 0)
    else:
        assert len(beta_prior) == 2
        assert len(beta_prior[0]) == 2
        assert isinstance(beta_prior[1], int)
        assert isinstance(beta_prior[0][0], int) or isinstance(beta_prior[0][0], float)
        assert isinstance(beta_prior[0][1], int) or isinstance(beta_prior[0][1], float)
        assert (beta_prior[0][0] > 0.) and (beta_prior[0][1] > 0.)
        out = beta_prior
    return out 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log2 [as 別名]
def pp_labels(matrix_dim):
    def _is_integer(x):
        return bool(abs(x - round(x)) < 1e-6)
    if matrix_dim == 0: return []
    if matrix_dim == 1: return ['']  # special case - use empty label instead of "I"

    nQubits = _np.log2(matrix_dim)
    if not _is_integer(nQubits):
        raise ValueError("Dimension for Pauli tensor product matrices must be an integer *power of 2*")
    nQubits = int(round(nQubits))

    lblList = []
    basisLblList = [['I', 'X', 'Y', 'Z']] * nQubits
    for sigmaLbls in _itertools.product(*basisLblList):
        lblList.append(''.join(sigmaLbls))
    return lblList 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log2 [as 別名]
def get_rpe_experiment_design(max_max_length, qubit_labels=None, req_counts=None):
    max_log_lengths = _np.log2(max_max_length)
    if not (int(max_log_lengths) - max_log_lengths == 0):
        raise ValueError('Only integer powers of two accepted for max_max_length.')

    assert(qubit_labels is None or qubit_labels == (0,)), "Only qubit_labels=(0,) is supported so far"
    return _rpe.RobustPhaseEstimationDesign(
        _obj.Circuit([('Gxpi2', 0)], line_labels=(0,)),
        [2**i for i in range(int(max_log_lengths) + 1)],
        _obj.Circuit([], line_labels=(0,)),
        _obj.Circuit([('Gxpi2', 0)], line_labels=(0,)),
        ['1'],
        ['0'],
        _obj.Circuit([], line_labels=(0,)),
        _obj.Circuit([], line_labels=(0,)),
        ['0'],
        ['1'],
        qubit_labels=qubit_labels,
        req_counts=req_counts) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log2 [as 別名]
def get_rpe_experiment_design(max_max_length, qubit_labels=None, req_counts=None):
    max_log_lengths = _np.log2(max_max_length)
    if not (int(max_log_lengths) - max_log_lengths == 0):
        raise ValueError('Only integer powers of two accepted for max_max_length.')

    assert(qubit_labels is None or qubit_labels == (0,)), "Only qubit_labels=(0,) is supported so far"
    return _rpe.RobustPhaseEstimationDesign(
        _obj.Circuit([('Gypi2', 0)], line_labels=(0,)),
        [2**i for i in range(int(max_log_lengths) + 1)],
        _obj.Circuit([], line_labels=(0,)),
        _obj.Circuit([('Gypi2', 0)], line_labels=(0,)),
        ['1'],
        ['0'],
        _obj.Circuit([], line_labels=(0,)),
        _obj.Circuit([], line_labels=(0,)),
        ['0'],
        ['1'],
        qubit_labels=qubit_labels,
        req_counts=req_counts) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log2 [as 別名]
def up_scaling(self, x, f, scale_factor, name):
        """
        :param x: image
        :param f: conv2d filter
        :param scale_factor: scale factor
        :param name: scope name
        :return:
        """
        with tf.variable_scope(name):
            if scale_factor == 3:
                x = tfu.conv2d(x, f * 9, k=1, name='conv2d-image_scaling-0')
                x = tfu.pixel_shuffle(x, 3)
            elif scale_factor & (scale_factor - 1) == 0:  # is it 2^n?
                log_scale_factor = int(np.log2(scale_factor))
                for i in range(log_scale_factor):
                    x = tfu.conv2d(x, f * 4, k=1, name='conv2d-image_scaling-%d' % i)
                    x = tfu.pixel_shuffle(x, 2)
            else:
                raise NotImplementedError("[-] Not supported scaling factor (%d)" % scale_factor)
            return x 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log2 [as 別名]
def nside_to_level(nside):
    """
    Find the HEALPix level for a given nside.

    This is given by ``level = log2(nside)``.

    This function is the inverse of `level_to_nside`.

    Parameters
    ----------
    nside : int
        The number of pixels on the side of one of the 12 'top-level' HEALPix tiles.
        Must be a power of two.

    Returns
    -------
    level : int
        The level of the HEALPix cells
    """
    nside = np.asarray(nside, dtype=np.int64)

    _validate_nside(nside)
    return np.log2(nside).astype(np.int64) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log2 [as 別名]
def _hist_bin_sturges(x, range):
    """
    Sturges histogram bin estimator.

    A very simplistic estimator based on the assumption of normality of
    the data. This estimator has poor performance for non-normal data,
    which becomes especially obvious for large data sets. The estimate
    depends only on size of the data.

    Parameters
    ----------
    x : array_like
        Input data that is to be histogrammed, trimmed to range. May not
        be empty.

    Returns
    -------
    h : An estimate of the optimal bin width for the given data.
    """
    del range  # unused
    return x.ptp() / (np.log2(x.size) + 1.0) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log2 [as 別名]
def test_branch_cuts(self):
        # check branch cuts and continuity on them
        _check_branch_cut(np.log,   -0.5, 1j, 1, -1, True)
        _check_branch_cut(np.log2,  -0.5, 1j, 1, -1, True)
        _check_branch_cut(np.log10, -0.5, 1j, 1, -1, True)
        _check_branch_cut(np.log1p, -1.5, 1j, 1, -1, True)
        _check_branch_cut(np.sqrt,  -0.5, 1j, 1, -1, True)

        _check_branch_cut(np.arcsin, [ -2, 2],   [1j, 1j], 1, -1, True)
        _check_branch_cut(np.arccos, [ -2, 2],   [1j, 1j], 1, -1, True)
        _check_branch_cut(np.arctan, [0-2j, 2j],  [1,  1], -1, 1, True)

        _check_branch_cut(np.arcsinh, [0-2j,  2j], [1,   1], -1, 1, True)
        _check_branch_cut(np.arccosh, [ -1, 0.5], [1j,  1j], 1, -1, True)
        _check_branch_cut(np.arctanh, [ -2,   2], [1j, 1j], 1, -1, True)

        # check against bogus branch cuts: assert continuity between quadrants
        _check_branch_cut(np.arcsin, [0-2j, 2j], [ 1,  1], 1, 1)
        _check_branch_cut(np.arccos, [0-2j, 2j], [ 1,  1], 1, 1)
        _check_branch_cut(np.arctan, [ -2,  2], [1j, 1j], 1, 1)

        _check_branch_cut(np.arcsinh, [ -2,  2, 0], [1j, 1j, 1], 1, 1)
        _check_branch_cut(np.arccosh, [0-2j, 2j, 2], [1,  1,  1j], 1, 1)
        _check_branch_cut(np.arctanh, [0-2j, 2j, 0], [1,  1,  1j], 1, 1) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log2 [as 別名]
def test_branch_cuts_complex64(self):
        # check branch cuts and continuity on them
        _check_branch_cut(np.log,   -0.5, 1j, 1, -1, True, np.complex64)
        _check_branch_cut(np.log2,  -0.5, 1j, 1, -1, True, np.complex64)
        _check_branch_cut(np.log10, -0.5, 1j, 1, -1, True, np.complex64)
        _check_branch_cut(np.log1p, -1.5, 1j, 1, -1, True, np.complex64)
        _check_branch_cut(np.sqrt,  -0.5, 1j, 1, -1, True, np.complex64)

        _check_branch_cut(np.arcsin, [ -2, 2],   [1j, 1j], 1, -1, True, np.complex64)
        _check_branch_cut(np.arccos, [ -2, 2],   [1j, 1j], 1, -1, True, np.complex64)
        _check_branch_cut(np.arctan, [0-2j, 2j],  [1,  1], -1, 1, True, np.complex64)

        _check_branch_cut(np.arcsinh, [0-2j,  2j], [1,   1], -1, 1, True, np.complex64)
        _check_branch_cut(np.arccosh, [ -1, 0.5], [1j,  1j], 1, -1, True, np.complex64)
        _check_branch_cut(np.arctanh, [ -2,   2], [1j, 1j], 1, -1, True, np.complex64)

        # check against bogus branch cuts: assert continuity between quadrants
        _check_branch_cut(np.arcsin, [0-2j, 2j], [ 1,  1], 1, 1, False, np.complex64)
        _check_branch_cut(np.arccos, [0-2j, 2j], [ 1,  1], 1, 1, False, np.complex64)
        _check_branch_cut(np.arctan, [ -2,  2], [1j, 1j], 1, 1, False, np.complex64)

        _check_branch_cut(np.arcsinh, [ -2,  2, 0], [1j, 1j, 1], 1, 1, False, np.complex64)
        _check_branch_cut(np.arccosh, [0-2j, 2j, 2], [1,  1,  1j], 1, 1, False, np.complex64)
        _check_branch_cut(np.arctanh, [0-2j, 2j, 0], [1,  1,  1j], 1, 1, False, np.complex64) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log2 [as 別名]
def p2o(psf, shape):
    '''
    # psf: NxCxhxw
    # shape: [H,W]
    # otf: NxCxHxWx2
    '''
    otf = torch.zeros(psf.shape[:-2] + shape).type_as(psf)
    otf[...,:psf.shape[2],:psf.shape[3]].copy_(psf)
    for axis, axis_size in enumerate(psf.shape[2:]):
        otf = torch.roll(otf, -int(axis_size / 2), dims=axis+2)
    otf = torch.rfft(otf, 2, onesided=False)
    n_ops = torch.sum(torch.tensor(psf.shape).type_as(psf) * torch.log2(torch.tensor(psf.shape).type_as(psf)))
    otf[...,1][torch.abs(otf[...,1])<n_ops*2.22e-16] = torch.tensor(0).type_as(psf)
    return otf



# otf2psf: not sure where I got this one from. Maybe translated from Octave source code or whatever. It's just math.