Python numpy.logical_and方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logical_and [as 別名]
def dataframe_select(df, *cols, **filters):
    '''
    dataframe_select(df, k1=v1, k2=v2...) yields df after selecting all the columns in which the
      given keys (k1, k2, etc.) have been selected such that the associated columns in the dataframe
      contain only the rows whose cells match the given values.
    dataframe_select(df, col1, col2...) selects the given columns.
    dataframe_select(df, col1, col2..., k1=v1, k2=v2...) selects both.
    
    If a value is a tuple/list of 2 elements, then it is considered a range where cells must fall
    between the values. If value is a tuple/list of more than 2 elements or is a set of any length
    then it is a list of values, any one of which can match the cell.
    '''
    ii = np.ones(len(df), dtype='bool')
    for (k,v) in six.iteritems(filters):
        vals = df[k].values
        if   pimms.is_set(v):                    jj = np.isin(vals, list(v))
        elif pimms.is_vector(v) and len(v) == 2: jj = (v[0] <= vals) & (vals < v[1])
        elif pimms.is_vector(v):                 jj = np.isin(vals, list(v))
        else:                                    jj = (vals == v)
        ii = np.logical_and(ii, jj)
    if len(ii) != np.sum(ii): df = df.loc[ii]
    if len(cols) > 0: df = df[list(cols)]
    return df 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logical_and [as 別名]
def _update_labels(self, label, crop_box, height, width):
        """Convert labels according to crop box"""
        xmin = float(crop_box[0]) / width
        ymin = float(crop_box[1]) / height
        w = float(crop_box[2]) / width
        h = float(crop_box[3]) / height
        out = label.copy()
        out[:, (1, 3)] -= xmin
        out[:, (2, 4)] -= ymin
        out[:, (1, 3)] /= w
        out[:, (2, 4)] /= h
        out[:, 1:5] = np.maximum(0, out[:, 1:5])
        out[:, 1:5] = np.minimum(1, out[:, 1:5])
        coverage = self._calculate_areas(out[:, 1:]) * w * h / self._calculate_areas(label[:, 1:])
        valid = np.logical_and(out[:, 3] > out[:, 1], out[:, 4] > out[:, 2])
        valid = np.logical_and(valid, coverage > self.min_eject_coverage)
        valid = np.where(valid)[0]
        if valid.size < 1:
            return None
        out = out[valid, :]
        return out 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logical_and [as 別名]
def _parse_label(self, label):
        """Helper function to parse object detection label.

        Format for raw label:
        n \t k \t ... \t [id \t xmin\t ymin \t xmax \t ymax \t ...] \t [repeat]
        where n is the width of header, 2 or larger
        k is the width of each object annotation, can be arbitrary, at least 5
        """
        if isinstance(label, nd.NDArray):
            label = label.asnumpy()
        raw = label.ravel()
        if raw.size < 7:
            raise RuntimeError("Label shape is invalid: " + str(raw.shape))
        header_width = int(raw[0])
        obj_width = int(raw[1])
        if (raw.size - header_width) % obj_width != 0:
            msg = "Label shape %s inconsistent with annotation width %d." \
                %(str(raw.shape), obj_width)
            raise RuntimeError(msg)
        out = np.reshape(raw[header_width:], (-1, obj_width))
        # remove bad ground-truths
        valid = np.where(np.logical_and(out[:, 3] > out[:, 1], out[:, 4] > out[:, 2]))[0]
        if valid.size < 1:
            raise RuntimeError('Encounter sample with no valid label.')
        return out[valid, :] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logical_and [as 別名]
def get_hardness_distribution(gtG, max_dist, min_dist, rng, trials, bins, nodes,
                              n_ori, step_size):
  heuristic_fn = lambda node_ids, node_id: \
    heuristic_fn_vec(nodes[node_ids, :], nodes[[node_id], :], n_ori, step_size)
  num_nodes = gtG.num_vertices()
  gt_dists = []; h_dists = [];
  for i in range(trials):
    end_node_id = rng.choice(num_nodes)
    gt_dist = gt.topology.shortest_distance(gt.GraphView(gtG, reversed=True),
                                            source=gtG.vertex(end_node_id),
                                            target=None, max_dist=max_dist)
    gt_dist = np.array(gt_dist.get_array())
    ind = np.where(np.logical_and(gt_dist <= max_dist, gt_dist >= min_dist))[0]
    gt_dist = gt_dist[ind]
    h_dist = heuristic_fn(ind, end_node_id)[:,0]
    gt_dists.append(gt_dist)
    h_dists.append(h_dist)
  gt_dists = np.concatenate(gt_dists)
  h_dists = np.concatenate(h_dists)
  hardness = 1. - h_dists*1./gt_dists
  hist, _ = np.histogram(hardness, bins)
  hist = hist.astype(np.float64)
  hist = hist / np.sum(hist)
  return hist 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logical_and [as 別名]
def test_subsample_all_examples(self):
    numpy_labels = np.random.permutation(300)
    indicator = tf.constant(np.ones(300) == 1)
    numpy_labels = (numpy_labels - 200) > 0

    labels = tf.constant(numpy_labels)

    sampler = (balanced_positive_negative_sampler.
               BalancedPositiveNegativeSampler())
    is_sampled = sampler.subsample(indicator, 64, labels)
    with self.test_session() as sess:
      is_sampled = sess.run(is_sampled)
      self.assertTrue(sum(is_sampled) == 64)
      self.assertTrue(sum(np.logical_and(numpy_labels, is_sampled)) == 32)
      self.assertTrue(sum(np.logical_and(
          np.logical_not(numpy_labels), is_sampled)) == 32) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logical_and [as 別名]
def is_earthlike(self, plan_inds, sInd):
        """
        Is the planet earthlike?
        """
        TL = self.TargetList
        SU = self.SimulatedUniverse
        PPop = self.PlanetPopulation

        # extract planet and star properties
        Rp_plan = SU.Rp[plan_inds].value
        L_star = TL.L[sInd]
        if PPop.scaleOrbits:
            a_plan = (SU.a[plan_inds]/np.sqrt(L_star)).value
        else:
            a_plan = (SU.a[plan_inds]).value
        # Definition: planet radius (in earth radii) and solar-equivalent luminosity must be
        # between the given bounds.
        Rp_plan_lo = 0.80/np.sqrt(a_plan)
        # We use the numpy versions so that plan_ind can be a numpy vector.
        return np.logical_and(
           np.logical_and(Rp_plan >= Rp_plan_lo, Rp_plan <= 1.4),
           np.logical_and(a_plan  >= 0.95,     a_plan  <= 1.67)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logical_and [as 別名]
def test_subsample_selection(self):
    # Test random sampling when only some examples can be sampled:
    # 100 samples, 20 positives, 10 positives cannot be sampled
    numpy_labels = np.arange(100)
    numpy_indicator = numpy_labels < 90
    indicator = tf.constant(numpy_indicator)
    numpy_labels = (numpy_labels - 80) >= 0

    labels = tf.constant(numpy_labels)

    sampler = (balanced_positive_negative_sampler.
               BalancedPositiveNegativeSampler())
    is_sampled = sampler.subsample(indicator, 64, labels)
    with self.test_session() as sess:
      is_sampled = sess.run(is_sampled)
      self.assertTrue(sum(is_sampled) == 64)
      self.assertTrue(sum(np.logical_and(numpy_labels, is_sampled)) == 10)
      self.assertTrue(sum(np.logical_and(
          np.logical_not(numpy_labels), is_sampled)) == 54)
      self.assertAllEqual(is_sampled, np.logical_and(is_sampled,
                                                     numpy_indicator)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logical_and [as 別名]
def _do(self, problem, X, **kwargs):
        n_parents, n_matings, n_var = X.shape

        _X = np.full((self.n_offsprings, n_matings, problem.n_var), False)

        for k in range(n_matings):
            p1, p2 = X[0, k], X[1, k]

            both_are_true = np.logical_and(p1, p2)
            _X[0, k, both_are_true] = True

            n_remaining = problem.n_max - np.sum(both_are_true)

            I = np.where(np.logical_xor(p1, p2))[0]

            S = I[np.random.permutation(len(I))][:n_remaining]
            _X[0, k, S] = True

        return _X 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logical_and [as 別名]
def _mandelbrot(size=1000, real_range=(-2, 2), imaginary_range=(-2, 2), iterations=25, threshold=4):

    img, c = _mandelbrot_initialize(size=size, real_range=real_range, imaginary_range=imaginary_range)

    optim = _mandelbrot_optimize(c)

    z = np.copy(c)
    for i in range(1, iterations + 1):  # pylint: disable=W0612
        # Continue only where smaller than threshold
        mask = (z * z.conjugate()).real < threshold
        mask = np.logical_and(mask, optim)

        if np.all(~mask) is True:
            break

        # Increase
        img[mask] += 1

        # Iterate based on Mandelbrot equation
        z[mask] = z[mask] ** 2 + c[mask]

    # Fill otpimized area
    img[~optim] = np.max(img)
    return img 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logical_and [as 別名]
def _mandelbrot_optimize(c):
    # Optimizations: most of the mset points lie within the
    # within the cardioid or in the period-2 bulb. (The two most
    # prominant shapes in the mandelbrot set. We can eliminate these
    # from our search straight away and save alot of time.
    # see: http://en.wikipedia.org/wiki/Mandelbrot_set#Optimizations

    # First eliminate points within the cardioid
    p = (((c.real - 0.25) ** 2) + (c.imag ** 2)) ** 0.5
    mask1 = c.real > p - (2 * p ** 2) + 0.25

    # Next eliminate points within the period-2 bulb
    mask2 = ((c.real + 1) ** 2) + (c.imag ** 2) > 0.0625

    # Combine masks
    mask = np.logical_and(mask1, mask2)
    return mask 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logical_and [as 別名]
def _correct_missed(missed_idcs, peaks):

    corrected_peaks = peaks.copy()
    missed_idcs = np.array(missed_idcs)
    # Calculate the position(s) of new beat(s). Make sure to not generate
    # negative indices. prev_peaks and next_peaks must have the same
    # number of elements.
    valid_idcs = np.logical_and(missed_idcs > 1, missed_idcs < len(corrected_peaks))  # pylint: disable=E1111
    missed_idcs = missed_idcs[valid_idcs]
    prev_peaks = corrected_peaks[[i - 1 for i in missed_idcs]]
    next_peaks = corrected_peaks[missed_idcs]
    added_peaks = prev_peaks + (next_peaks - prev_peaks) / 2
    # Add the new peaks before the missed indices (see numpy docs).
    corrected_peaks = np.insert(corrected_peaks, missed_idcs, added_peaks)

    return corrected_peaks 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logical_and [as 別名]
def _correct_misaligned(misaligned_idcs, peaks):

    corrected_peaks = peaks.copy()
    misaligned_idcs = np.array(misaligned_idcs)
    # Make sure to not generate negative indices, or indices that exceed
    # the total number of peaks. prev_peaks and next_peaks must have the
    # same number of elements.
    valid_idcs = np.logical_and(
        misaligned_idcs > 1, misaligned_idcs < len(corrected_peaks) - 1  # pylint: disable=E1111
    )
    misaligned_idcs = misaligned_idcs[valid_idcs]
    prev_peaks = corrected_peaks[[i - 1 for i in misaligned_idcs]]
    next_peaks = corrected_peaks[[i + 1 for i in misaligned_idcs]]

    half_ibi = (next_peaks - prev_peaks) / 2
    peaks_interp = prev_peaks + half_ibi
    # Shift the R-peaks from the old to the new position.
    corrected_peaks = np.delete(corrected_peaks, misaligned_idcs)
    corrected_peaks = np.concatenate((corrected_peaks, peaks_interp)).astype(int)
    corrected_peaks.sort(kind="mergesort")

    return corrected_peaks 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logical_and [as 別名]
def pyeeg_ap_entropy(X, M, R):
    N = len(X)

    Em = pyeeg_embed_seq(X, 1, M)
    A = np.tile(Em, (len(Em), 1, 1))
    B = np.transpose(A, [1, 0, 2])
    D = np.abs(A - B)  # D[i,j,k] = |Em[i][k] - Em[j][k]|
    InRange = np.max(D, axis=2) <= R

    # Probability that random M-sequences are in range
    Cm = InRange.mean(axis=0)

    # M+1-sequences in range if M-sequences are in range & last values are close
    Dp = np.abs(np.tile(X[M:], (N - M, 1)) - np.tile(X[M:], (N - M, 1)).T)

    Cmp = np.logical_and(Dp <= R, InRange[:-1, :-1]).mean(axis=0)

    Phi_m, Phi_mp = np.sum(np.log(Cm)), np.sum(np.log(Cmp))

    Ap_En = (Phi_m - Phi_mp) / (N - M)

    return Ap_En 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logical_and [as 別名]
def pyeeg_samp_entropy(X, M, R):
    N = len(X)

    Em = pyeeg_embed_seq(X, 1, M)[:-1]
    A = np.tile(Em, (len(Em), 1, 1))
    B = np.transpose(A, [1, 0, 2])
    D = np.abs(A - B)  # D[i,j,k] = |Em[i][k] - Em[j][k]|
    InRange = np.max(D, axis=2) <= R
    np.fill_diagonal(InRange, 0)  # Don't count self-matches

    Cm = InRange.sum(axis=0)  # Probability that random M-sequences are in range
    Dp = np.abs(np.tile(X[M:], (N - M, 1)) - np.tile(X[M:], (N - M, 1)).T)

    Cmp = np.logical_and(Dp <= R, InRange).sum(axis=0)

    # Avoid taking log(0)
    Samp_En = np.log(np.sum(Cm + 1e-100) / np.sum(Cmp + 1e-100))

    return Samp_En


# =============================================================================
# Entropy
# ============================================================================= 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logical_and [as 別名]
def create_pixel_plots(candidate_path, reference_path, base_name,
                       last_band_alpha=False, limits=None, custom_alpha=None):
    c_ds, c_alpha, c_band_count = _open_image_and_get_info(
        candidate_path, last_band_alpha)
    r_ds, r_alpha, r_band_count = _open_image_and_get_info(
        reference_path, last_band_alpha)

    _assert_consistent(c_alpha, r_alpha, c_band_count, r_band_count)

    if custom_alpha != None:
        combined_alpha = custom_alpha
    else:
        combined_alpha = numpy.logical_and(c_alpha, r_alpha)
    valid_pixels = numpy.nonzero(combined_alpha)

    for band_no in range(1, c_band_count + 1):
        c_band = gimage.read_single_band(c_ds, band_no)
        r_band = gimage.read_single_band(r_ds, band_no)
        file_name = '{}_{}.png'.format(base_name, band_no)
        display.plot_pixels(file_name, c_band[valid_pixels],
                            r_band[valid_pixels], limits)