Python numpy.histogram方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import histogram [as 別名]
def compareHistograms(image,x,y,w,h, ppl):
	#temporary crop detected target
	tempCrop = image[x:x+w, y:y+h]
	#generate temporary histogram to compare to existant ones
	tempHist = generateHistogram(tempCrop)
	
	if(len(ppl) > 0):
		b = checkSimilarity(tempHist, ppl, image)
		if(b):
			return (b.x, b.y, b.w, b.h, b.color, b.label)
		else:
			return None
	else:
		return None
	
	return None 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import histogram [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 histogram [as 別名]
def test_gen_sma(self):
        r"""Test gen_sma method.

        Approach: Ensures the output is set, of the correct type, length, and units.
        Check that they are in the correct range and follow the distribution.
        """

        plan_pop = self.fixture
        n = 10000
        sma = plan_pop.gen_sma(n)

        # ensure the units are length
        self.assertEqual((sma/u.km).decompose().unit, u.dimensionless_unscaled)
        # sma > 0
        self.assertTrue(np.all(sma.value >= 0))
        # sma >= arange[0], sma <= arange[1]
        self.assertTrue(np.all(sma - plan_pop.arange[0] >= 0))
        self.assertTrue(np.all(plan_pop.arange[1] - sma >= 0))

        h = np.histogram(sma.to('AU').value,100,density=True)
        hx = np.diff(h[1])/2.+h[1][:-1]
        hp = plan_pop.dist_sma(hx)

        chi2 = scipy.stats.chisquare(h[0],hp)
        self.assertGreaterEqual(chi2[1],0.95) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import histogram [as 別名]
def test_gen_plan_params(self):
        r"""Test generated planet parameters:
        Expected: all 1 R_E, all p = 0.67, e = 0, and uniform a in arange
        """

        obj = EarthTwinHabZone1(**self.spec)

        x = 10000

        a, e, p, Rp = obj.gen_plan_params(x)
        
        assert(np.all(e == 0))
        assert(np.all(p == 0.367))
        assert(np.all(Rp == 1.0*u.R_earth))

        h = np.histogram(a.to('AU').value,100,density=True)
        chi2 = scipy.stats.chisquare(h[0],[1.0/np.diff(obj.arange.to('AU').value)[0]]*len(h[0]))
        self.assertGreater(chi2[1], 0.95) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import histogram [as 別名]
def test_gen_plan_params(self):
        r"""Test generated planet parameters:
        Expected: all 1 R_E, all p = 0.67, e = 0, and uniform a,e in arange,erange
        """

        obj = EarthTwinHabZone2(constrainOrbits=False,erange=[0.1,0.5],**self.spec)

        x = 10000

        a, e, p, Rp = obj.gen_plan_params(x)
        
        assert(np.all(p == 0.367))
        assert(np.all(Rp == 1.0*u.R_earth))

        for param,param_range in zip([a.value,e],[obj.arange.value,obj.erange]):
            h = np.histogram(param,100,density=True)
            chi2 = scipy.stats.chisquare(h[0],[1.0/np.diff(param_range)[0]]*len(h[0]))
            self.assertGreater(chi2[1], 0.95) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import histogram [as 別名]
def test_gen_radius(self):
        r"""Test gen_radius method.

        Approach: Ensures the output is set, of the correct type, length, and units.
        Check distributional agreement.
        """
        plan_pop = self.fixture
        n = 10000
        radii = plan_pop.gen_radius(n)

        # ensure the units are length
        self.assertEqual((radii/u.km).decompose().unit, u.dimensionless_unscaled)
        # radius > 0
        self.assertTrue(np.all(radii.value > 0))
        self.assertTrue(np.all(np.isfinite(radii.value)))

        h = np.histogram(radii.to('earthRad').value,bins=plan_pop.Rs)
        np.testing.assert_allclose(plan_pop.Rvals.sum()*h[0]/float(n),plan_pop.Rvals,rtol=0.05) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import histogram [as 別名]
def hist_chars(x, m=None, M=None, width=50):
    '''
    Prints a one-line histogram with one char per bin.  The bin count is
    quantized into only a few values and scaled to create a visual
    representation.  Min and max values are displayed on the ends.
    '''
    (h, hbins, m, M) = _gethist(x, width, m, M)
    nchars = len(_strhist_chars)
    if np.any(h > 0):
        hmin = np.min(h)
        hmax  = np.max(h)
        hchar = np.round((nchars-1)*(h - hmin)/(hmax - hmin))
        hstr = ''.join([_strhist_chars[int(i)] for i in hchar])
    else:
        hstr = ' ' * width
    return '% .5f |%s| %.5f' % (m, hstr, M) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import histogram [as 別名]
def histogram(t, L):
    """
    A: If t is a list of tensors/np.ndarrays, B is executed for all, yielding len(ts) histograms, which are summed
    per bin
    B: convert t to numpy, count bins.
    :param t: tensor or list of tensor, each expected to be in [0, L)
    :param L: number of symbols
    :return: length-L array, containing at l the number of values mapping to to symbol l
    """
    if isinstance(t, list):
        ts = t
        histograms = np.stack((histogram(t, L) for t in ts), axis=0)  # get array (len(ts) x L)
        return np.sum(histograms, 0)
    assert 0 <= t.min() and t.max() < L, (t.min(), t.max())
    a = tensor_to_np(t)
    counts, _ = np.histogram(a, np.arange(L+1))  # +1 because np.histogram takes bin edges, including rightmost edge
    return counts


# Gradients -------------------------------------------------------------------- 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import histogram [as 別名]
def threshold(self, morph):
        """Find the threshold value for a given morphology
        """
        _morph = morph[morph > 0]
        _bins = 50
        # Decrease the bin size for sources with a small number of pixels
        if _morph.size < 500:
            _bins = max(np.int(_morph.size / 10), 1)
            if _bins == 1:
                return 0, _bins
        hist, bins = np.histogram(np.log10(_morph).reshape(-1), _bins)
        cutoff = np.where(hist == 0)[0]
        # If all of the pixels are used there is no need to threshold
        if len(cutoff) == 0:
            return 0, _bins
        return 10 ** bins[cutoff[-1]], _bins 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import histogram [as 別名]
def plot_histograms(file_name, candidate_data_multiple_bands,
                    reference_data_multiple_bands=None,
                    # Default is for Blue-Green-Red-NIR:
                    colour_order=['b', 'g', 'r', 'y'],
                    x_limits=None, y_limits=None):
    logging.info('Display: Creating histogram plot - {}'.format(file_name))
    fig = plt.figure()
    plt.hold(True)
    for colour, c_band in zip(colour_order, candidate_data_multiple_bands):
        c_bh, c_bins = numpy.histogram(c_band, bins=256)
        plt.plot(c_bins[:-1], c_bh, color=colour, linestyle='-', linewidth=2)
    if reference_data_multiple_bands:
        for colour, r_band in zip(colour_order, reference_data_multiple_bands):
            r_bh, r_bins = numpy.histogram(r_band, bins=256)
            plt.plot(
                r_bins[:-1], r_bh, color=colour, linestyle='--', linewidth=2)
    plt.xlabel('DN')
    plt.ylabel('Number of pixels')
    if x_limits:
        plt.xlim(x_limits)
    if y_limits:
        plt.ylim(y_limits)
    fig.savefig(file_name, bbox_inches='tight')
    plt.close(fig) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import histogram [as 別名]
def intersectionAndUnion(imPred, imLab, numClass=150):
    """
    Computes the intersection and Union for all the classes between two images
    :param imPred: Predictions image
    :param imLab: Ground-truth image
    :param numClass: Number of semantic classes. Default:150
    :return: Intersection and union for all the classes
    """
    # Remove classes from unlabeled pixels in gt image.
    # We should not penalize detections in unlabeled portions of the image.
    imPred = imPred * (imLab > 0).long()

    # Compute area intersection:
    intersection = imPred * (imPred == imLab).long()
    (area_intersection, _) = np.histogram(intersection, bins=numClass, range=(1, numClass))

    # Compute area union:
    (area_pred, _) = np.histogram(imPred, bins=numClass, range=(1, numClass))
    (area_lab, _) = np.histogram(imLab, bins=numClass, range=(1, numClass))
    area_union = area_pred + area_lab - area_intersection

    IoU = area_intersection / (area_union + 1e-10)

    return IoU 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import histogram [as 別名]
def read_image(self,im):
    if self.i >= self.k :
      self.i = 0
    try:
      img = Image.open(im)
      osize = img.size
      img.thumbnail((self.resample,self.resample))
      v = [float(p)/float(img.size[0]*img.size[1])*100  for p in np.histogram(np.asarray(img))[0]]
      if self.size :
        v += [osize[0], osize[1]]
      pbar.update(1)
      i = self.i
      self.i += 1
      return [i, v, im]
    except Exception as e:
      print("Error reading ",im,e)
      return [None, None, None] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import histogram [as 別名]
def read_image(self,im):
    if self.i >= self.k :
      self.i = 0
    try:
      img = Image.open(im)
      osize = img.size
      img.thumbnail((self.resample,self.resample))
      v = [float(p)/float(img.size[0]*img.size[1])*100  for p in np.histogram(np.asarray(img))[0]]
      if self.size :
        v += [osize[0], osize[1]]
      i = self.i
      self.i += 1
      return [i, v, im]
    except Exception as e:
      print("Error reading ",im,e)
      return [None, None, None] 

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

    Bin width is inversely proportional to the data size. Used by many
    programs for its simplicity.

    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.sqrt(x.size) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import histogram [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)