Python numpy.median() Examples

def test_out(self):
        mat = np.random.rand(3, 3)
        nan_mat = np.insert(mat, [0, 2], np.nan, axis=1)
        resout = np.zeros(3)
        tgt = np.median(mat, axis=1)
        res = np.nanmedian(nan_mat, axis=1, out=resout)
        assert_almost_equal(res, resout)
        assert_almost_equal(res, tgt)
        # 0-d output:
        resout = np.zeros(())
        tgt = np.median(mat, axis=None)
        res = np.nanmedian(nan_mat, axis=None, out=resout)
        assert_almost_equal(res, resout)
        assert_almost_equal(res, tgt)
        res = np.nanmedian(nan_mat, axis=(0, 1), out=resout)
        assert_almost_equal(res, resout)
        assert_almost_equal(res, tgt) 

def update_hyperparameters(self):
        """Update hyperparameters like IoU thresholds for assigner and beta for
        SmoothL1 loss based on the training statistics.

        Returns:
            tuple[float]: the updated ``iou_thr`` and ``beta``.
        """
        new_iou_thr = max(self.train_cfg.dynamic_rcnn.initial_iou,
                          np.mean(self.iou_history))
        self.iou_history = []
        self.bbox_assigner.pos_iou_thr = new_iou_thr
        self.bbox_assigner.neg_iou_thr = new_iou_thr
        self.bbox_assigner.min_pos_iou = new_iou_thr
        new_beta = min(self.train_cfg.dynamic_rcnn.initial_beta,
                       np.median(self.beta_history))
        self.beta_history = []
        self.bbox_head.loss_bbox.beta = new_beta
        return new_iou_thr, new_beta 

def _raise_on_mode(self, mode):
        """
        Checks that the provided query mode is one of the accepted values. If
        not, raises a :obj:`ValueError`.
        """
        valid_modes = [
            'random_sample',
            'random_sample_per_pix',
            'samples',
            'median',
            'mean',
            'best',
            'percentile']

        if mode not in valid_modes:
            raise ValueError(
                '"{}" is not a valid `mode`. Valid modes are:\n'
                '  {}'.format(mode, valid_modes)
            ) 

def vote(vec, tol):
    vec = np.sort(vec)
    n = np.arange(len(vec))[::-1]
    n = n[:, None] - n[None, :] + 1.0
    l = squareform(pdist(vec[:, None], 'minkowski', p=1) + 1e-9)

    invalid = (n < len(vec) * 0.4) | (l > tol)
    if (~invalid).sum() == 0 or len(vec) < tol:
        best_fit = np.median(vec)
        p_score = 0
    else:
        l[invalid] = 1e5
        n[invalid] = -1
        score = n
        max_idx = score.argmax()
        max_row = max_idx // len(vec)
        max_col = max_idx % len(vec)
        assert max_col > max_row
        best_fit = vec[max_row:max_col+1].mean()
        p_score = (max_col - max_row + 1) / len(vec)

    l1_score = np.abs(vec - best_fit).mean()

    return best_fit, p_score, l1_score 

def _rsp_findpeaks_outliers(rsp_cleaned, extrema, amplitude_min=0.3):

    # Only consider those extrema that have a minimum vertical distance to
    # their direct neighbor, i.e., define outliers in absolute amplitude
    # difference between neighboring extrema.
    vertical_diff = np.abs(np.diff(rsp_cleaned[extrema]))
    median_diff = np.median(vertical_diff)
    min_diff = np.where(vertical_diff > (median_diff * amplitude_min))[0]
    extrema = extrema[min_diff]

    # Make sure that the alternation of peaks and troughs is unbroken. If
    # alternation of sign in extdiffs is broken, remove the extrema that
    # cause the breaks.
    amplitudes = rsp_cleaned[extrema]
    extdiffs = np.sign(np.diff(amplitudes))
    extdiffs = np.add(extdiffs[0:-1], extdiffs[1:])
    removeext = np.where(extdiffs != 0)[0] + 1
    extrema = np.delete(extrema, removeext)
    amplitudes = np.delete(amplitudes, removeext)

    return extrema, amplitudes 

def test_ParallellFiltersAndStability(self):
        x, y = self.model.sample_path(50)

        shape = 3000

        linear = AffineProcess((f, g), (1., 1.), self.norm, self.norm)
        self.model.hidden = linear

        filt = SISR(self.model, 1000).set_nparallel(shape).initialize().longfilter(y)

        filtermeans = filt.result.filter_means

        x = filtermeans[:, :1]
        mape = ((x - filtermeans[:, 1:]) / x).abs()

        assert mape.median(0)[0].max() < 0.05 

def test_ParallelUnscented(self):
        x, y = self.model.sample_path(50)

        shape = 30

        linear = AffineProcess((f, g), (1., 1.), self.norm, self.norm)
        self.model.hidden = linear

        filt = SISR(self.model, 1000, proposal=Unscented()).set_nparallel(shape).initialize().longfilter(y)

        filtermeans = filt.result.filter_means

        x = filtermeans[:, :1]
        mape = ((x - filtermeans[:, 1:]) / x).abs()

        assert mape.median(0)[0].max() < 0.05 

def makeadmask(cdat,min=True,getsum=False):

	nx,ny,nz,Ne,nt = cdat.shape

	mask = np.ones((nx,ny,nz),dtype=np.bool)

	if min:
		mask = cdat[:,:,:,:,:].prod(axis=-1).prod(-1)!=0
		return mask
	else:
		#Make a map of longest echo that a voxel can be sampled with,
		#with minimum value of map as X value of voxel that has median
		#value in the 1st echo. N.b. larger factor leads to bias to lower TEs
		emeans = cdat[:,:,:,:,:].mean(-1)
		medv = emeans[:,:,:,0] == stats.scoreatpercentile(emeans[:,:,:,0][emeans[:,:,:,0]!=0],33,interpolation_method='higher')
		lthrs = np.squeeze(np.array([ emeans[:,:,:,ee][medv]/3 for ee in range(Ne) ]))
		if len(lthrs.shape)==1: lthrs = np.atleast_2d(lthrs).T
		lthrs = lthrs[:,lthrs.sum(0).argmax()]
		mthr = np.ones([nx,ny,nz,ne])
		for ee in range(Ne): mthr[:,:,:,ee]*=lthrs[ee]
		mthr = np.abs(emeans[:,:,:,:])>mthr
		masksum = np.array(mthr,dtype=np.int).sum(-1)
		mask = masksum!=0
		if getsum: return mask,masksum
		else: return mask 

def fill_and_adj_numeric(df):
	#there are NA for page views, fill median for this == 1
	df.pageviews.fillna(df.pageviews.median(), inplace = True)

	df.hits.fillna(df.hits.median(), inplace = True)
	df.visits.fillna(df.visits.median(), inplace = True)

	#are boolean, fill NaN with zeros, add to categorical
	df.isTrueDirect.fillna(0, inplace = True)
	df.bounces.fillna(0, inplace = True)
	df.newVisits.fillna(0, inplace = True)
	df.visitNumber.fillna(1, inplace = True)

	for col in ['isTrueDirect', 'bounces', 'newVisits']:
		df[col] = df[col].astype(int)

	return df 

def drawNewLocation(ax, image_dict, result, image_scale, radio, sx, sy, event, ar):
    x_offset = 0.0
    y_offset = 0.0
    if sx is not None and sy is not None:
        x_offset = sx.val
        y_offset = sy.val

    vosm = np.copy(image_dict["Visible"])
    vosm = OSMTGC.addOSMToImage(result.ways, vosm, pc, image_scale, x_offset, y_offset)
    image_dict["Visible Golf"] = vosm

    hosm = np.copy(image_dict["Heightmap"]).astype('float32')
    hosm = np.clip(hosm, 0.0, 3.5*np.median( hosm[ hosm >= 0.0 ])) # Limit outlier pixels
    hosm = hosm / np.max(hosm)
    hosm = cv2.cvtColor(hosm, cv2.COLOR_GRAY2RGB)
    hosm = OSMTGC.addOSMToImage(result.ways, hosm, pc, image_scale, x_offset, y_offset)
    image_dict["Heightmap Golf"] = hosm

    # Always set to Visible Golf after drawing new golf features
    ax.imshow(image_dict["Visible Golf"], origin='lower')
    radio.set_active(1) 

def compute_median_rank_at_k(tp_fp_list, k):
  """Computes MedianRank@k, where k is the top-scoring labels.

  Args:
    tp_fp_list: a list of numpy arrays; each numpy array corresponds to the all
        detection on a single image, where the detections are sorted by score in
        descending order. Further, each numpy array element can have boolean or
        float values. True positive elements have either value >0.0 or True;
        any other value is considered false positive.
    k: number of top-scoring proposals to take.

  Returns:
    median_rank: median rank of all true positive proposals among top k by
      score.
  """
  ranks = []
  for i in range(len(tp_fp_list)):
    ranks.append(
        np.where(tp_fp_list[i][0:min(k, tp_fp_list[i].shape[0])] > 0)[0])
  concatenated_ranks = np.concatenate(ranks)
  return np.median(concatenated_ranks) 

def test_laplace_sample():
    """Test whether the mean and the variance of the samples are correct."""
    mu = np.array([-1, 0., 1.])
    beta = np.array([0.5, 1., 2.])
    nSamples = 1000000

    nParameters = mu.shape[0]
    parameters = {"mu": tf.constant(mu),
                  "beta": tf.constant(beta)}
    tfNSamples = tf.constant(nSamples)
    r = LaplaceAlgorithms.sample(parameters=parameters,
                                 nSamples=tfNSamples)

    with tf.Session() as sess:
        r = sess.run(r)

    assert(r.shape == (nSamples, nParameters))
    muHat = np.median(r, axis=0)
    assert(np.allclose(muHat, mu, atol=1e-1))
    betaHat = np.sqrt(np.var(r, axis=0)/2.)
    assert(np.allclose(betaHat, beta, atol=1e-1)) 

def mad(resid, c=0.6745):
    """
    Returns Median-Absolute-Deviation (MAD) for residuals

    Args:
        resid (np.ndarray): residuals
        c (float): scale factor to get to ~standard normal (default: 0.6745)
                 (i.e. 1 / 0.75iCDF ~= 1.4826 = 1 / 0.6745)

    Returns:
        float: MAD 'robust' variance estimate

    Reference:
        http://en.wikipedia.org/wiki/Median_absolute_deviation
    """
    # Return median absolute deviation adjusted sigma
    return np.median(np.fabs(resid - np.median(resid))) / c


# UTILITY FUNCTIONS
# np.any prevents nopython 

def kmeans(self, boxes, k, dist=np.median):
        box_number = boxes.shape[0]
        distances = np.empty((box_number, k))
        last_nearest = np.zeros((box_number,))
        np.random.seed()
        clusters = boxes[np.random.choice(
            box_number, k, replace=False)]  # init k clusters
        while True:

            distances = 1 - self.iou(boxes, clusters)

            current_nearest = np.argmin(distances, axis=1)
            if (last_nearest == current_nearest).all():
                break  # clusters won't change
            for cluster in range(k):
                clusters[cluster] = dist(  # update clusters
                    boxes[current_nearest == cluster], axis=0)

            last_nearest = current_nearest

        return clusters 

def timeDomain(NN):
    
    L = len(NN)    
    ANN = np.mean(NN)
    SDNN = np.std(NN)
    SDSD = np.std(np.diff(NN))    
    NN50 = len(np.where(np.diff(NN) > 0.05)[0])    
    pNN50 = NN50/L    
    NN20 = len(np.where(np.diff(NN) > 0.02)[0])
    pNN20 = NN20/L
    rMSSD = np.sqrt((1/L) * sum(np.diff(NN) ** 2))        
    MedianNN = np.median(NN)
    
    timeDomainFeats = {'ANN': ANN, 'SDNN': SDNN,
                       'SDSD': SDSD, 'NN50': NN50,
                       'pNN50': pNN50, 'NN20': NN20,
                       'pNN20': pNN20, 'rMSSD': rMSSD,
                       'MedianNN':MedianNN}
                       
    return timeDomainFeats 

def test_axis_keyword(self):
        a3 = np.array([[2, 3],
                       [0, 1],
                       [6, 7],
                       [4, 5]])
        for a in [a3, np.random.randint(0, 100, size=(2, 3, 4))]:
            orig = a.copy()
            np.median(a, axis=None)
            for ax in range(a.ndim):
                np.median(a, axis=ax)
            assert_array_equal(a, orig)

        assert_allclose(np.median(a3, axis=0), [3,  4])
        assert_allclose(np.median(a3.T, axis=1), [3,  4])
        assert_allclose(np.median(a3), 3.5)
        assert_allclose(np.median(a3, axis=None), 3.5)
        assert_allclose(np.median(a3.T), 3.5) 

def _debug_save_hardness(self, seed):
    out_path = os.path.join(self.logdir, '{:s}_{:d}_hardness.png'.format(self.building_name, seed))
    batch_size = 4000
    rng = np.random.RandomState(0)
    start_node_ids, end_node_ids, dists, pred_maps, paths, hardnesss, gt_dists = \
      rng_next_goal_rejection_sampling(
          None, batch_size, self.task.gtG, rng, self.task_params.max_dist,
          self.task_params.min_dist, self.task_params.max_dist,
          self.task.sampling_distribution, self.task.target_distribution,
          self.task.nodes, self.task_params.n_ori, self.task_params.step_size,
          self.task.distribution_bins, self.task.rejection_sampling_M)
    bins = self.task.distribution_bins 
    n_bins = self.task.n_bins
    with plt.style.context('ggplot'):
      fig, axes = utils.subplot(plt, (1,2), (10,10))
      ax = axes[0]
      _ = ax.hist(hardnesss, bins=bins, weights=np.ones_like(hardnesss)/len(hardnesss))
      ax.plot(bins[:-1]+0.5/n_bins, self.task.target_distribution, 'g')
      ax.plot(bins[:-1]+0.5/n_bins, self.task.sampling_distribution, 'b')
      ax.grid('on')
      
      ax = axes[1]
      _ = ax.hist(gt_dists, bins=np.arange(self.task_params.max_dist+1))
      ax.grid('on')
      ax.set_title('Mean: {:0.2f}, Median: {:0.2f}'.format(np.mean(gt_dists),
                                                           np.median(gt_dists)))
      with fu.fopen(out_path, 'w') as f:
        fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0) 

def extract_bag_of_characters_features(data, n_val):
    
    characters_to_check = [ '['+  c + ']' for c in string.printable if c not in ( '\n', '\\', '\v', '\r', '\t', '^' )] + ['[\\\\]', '[\^]']
    
    f = OrderedDict()

    f['n_values'] = n_val
    data_no_null = data.dropna()
    all_value_features = OrderedDict()

    all_value_features['length'] = data_no_null.apply(len)

    for c in characters_to_check:
        all_value_features['n_{}'.format(c)] = data_no_null.str.count(c)
        
    for value_feature_name, value_features in all_value_features.items():
        f['{}-agg-any'.format(value_feature_name)] = any(value_features)
        f['{}-agg-all'.format(value_feature_name)] = all(value_features)
        f['{}-agg-mean'.format(value_feature_name)] = np.mean(value_features)
        f['{}-agg-var'.format(value_feature_name)] = np.var(value_features)
        f['{}-agg-min'.format(value_feature_name)] = np.min(value_features)
        f['{}-agg-max'.format(value_feature_name)] = np.max(value_features)
        f['{}-agg-median'.format(value_feature_name)] = np.median(value_features)
        f['{}-agg-sum'.format(value_feature_name)] = np.sum(value_features)
        f['{}-agg-kurtosis'.format(value_feature_name)] = kurtosis(value_features)
        f['{}-agg-skewness'.format(value_feature_name)] = skew(value_features)

    n_none = data.size - data_no_null.size - len([ e for e in data if e == ''])
    f['none-agg-has'] = n_none > 0
    f['none-agg-percent'] = n_none / len(data)
    f['none-agg-num'] = n_none
    f['none-agg-all'] = (n_none == len(data))
    #print(len(f))
    return f 

def make_csv_for_target_predictions(target, predictions):
    return ['%s_test_segment_%.4d.mat,%.10f' % (target, i+1, p) for i, p in enumerate(predictions)]


# Wrapper to return both mean and median-combined predictions in csv submission format 

def test_zero_median_prob(self):
        self.mech.set_sensitivity(1).set_epsilon_delta(0.5, 0.1)
        vals = []

        for i in range(20000):
            vals.append(self.mech.randomise(0.5))

        median = float(np.median(vals))
        self.assertAlmostEqual(np.abs(median), 0.5, delta=0.1) 

def _compile_metrics(metrics_output_directory):
    """ Compiles metrics from given directory and returns
    results as dict.

    :param metrics_output_directory: Directory to get metrics from.
    :returns: Compiled metrics as dict.
    """
    songs = glob(join(metrics_output_directory, 'test/*.json'))
    index = pd.MultiIndex.from_tuples(
        product(_INSTRUMENTS, _METRICS),
        names=['instrument', 'metric'])
    pd.DataFrame([], index=['config1', 'config2'], columns=index)
    metrics = {
        instrument: {k: [] for k in _METRICS}
        for instrument in _INSTRUMENTS}
    for song in songs:
        with open(song, 'r') as stream:
            data = json.load(stream)
        for target in data['targets']:
            instrument = target['name']
            for metric in _METRICS:
                sdr_med = np.median([
                    frame['metrics'][metric]
                    for frame in target['frames']
                    if not np.isnan(frame['metrics'][metric])])
                metrics[instrument][metric].append(sdr_med)
    return metrics 

def train(classifier, training_data, quiet=False):
    X_train = training_data.X_train
    y_train = training_data.y_train
    if not quiet: print 'Training ...',
    start = time.get_seconds()
    classifier.fit(X_train, y_train)
    if not quiet: print '%ds' % (time.get_seconds() - start)


# Make predictions, and then combine the N predictions if using windows using mean and median.
# Returns (mean_predictions, median_predictions, raw_predictions) 

def auto_canny(self, image, sigma=0.33):
        v = np.median(image)
    
        lower = int(max(0, (1.0 - sigma) * v))
        upper = int(min(255, (1.0 + sigma) * v))
        edged = cv2.Canny(image, lower, upper)
    
        # return the edged image
        return edged

    # http://www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/ 

def fit(self, magnitude):
        median = np.median(magnitude)
        devs = abs(magnitude - median)
        return {"MedianAbsDev": np.median(devs)} 

def fit(self, magnitude):
        median_data = np.median(magnitude)

        sorted_data = np.sort(magnitude)
        lc_length = len(sorted_data)
        F_5_index = int(math.ceil(0.05 * lc_length))
        F_95_index = int(math.ceil(0.95 * lc_length))
        F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index]

        percent_difference = F_5_95 / median_data

        return {"PercentDifferenceFluxPercentile": percent_difference} 

def fit(self, magnitude):
        median = np.median(magnitude)
        amplitude = (np.max(magnitude) - np.min(magnitude)) / 10
        n = len(magnitude)

        count = np.sum(
            np.logical_and(
                magnitude < median + amplitude, magnitude > median - amplitude
            )
        )

        return {"MedianBRP": float(count) / n} 

def fit(self, magnitude):
        median_data = np.median(magnitude)
        distance_median = np.abs(magnitude - median_data)
        max_distance = np.max(distance_median)

        percent_amplitude = max_distance / median_data

        return {"PercentAmplitude": percent_amplitude} 

def _median_min_max_5p(self, magnitude):
        N = len(magnitude)
        sorted_mag = np.sort(magnitude)

        max5p = np.median(sorted_mag[-int(math.ceil(0.05 * N)) :])
        min5p = np.median(sorted_mag[0 : int(math.ceil(0.05 * N))])

        return min5p, max5p 

def entrypoint(arguments, params):
    """ Command entrypoint.

    :param arguments: Command line parsed argument as argparse.Namespace.
    :param params: Deserialized JSON configuration file provided in CLI args.
    """
    # Parse and check musdb directory.
    musdb_root_directory = arguments.mus_dir
    if not exists(musdb_root_directory):
        raise IOError(f'musdb directory {musdb_root_directory} not found')
    # Separate musdb sources.
    audio_output_directory = _separate_evaluation_dataset(
        arguments,
        musdb_root_directory,
        params)
    # Compute metrics with musdb.
    metrics_output_directory = _compute_musdb_metrics(
        arguments,
        musdb_root_directory,
        audio_output_directory)
    # Compute and pretty print median metrics.
    metrics = _compile_metrics(metrics_output_directory)
    for instrument, metric in metrics.items():
        get_logger().info('%s:', instrument)
        for metric, value in metric.items():
            get_logger().info('%s: %s', metric, f'{np.median(value):.3f}')

    return metrics 

def is_outlier(points, thresh=3.5):
    """
    Returns a boolean array with True if points are outliers and False
    otherwise.

    modified from nipype:
    https://github.com/nipy/nipype/blob/b62d80/nipype/algorithms/confounds.py#L1129

    Parameters
    ----------
    points: nparray
        an numobservations by numdimensions numpy array of observations
    thresh: float
        the modified z-score to use as a threshold. Observations with
        a modified z-score (based on the median absolute deviation) greater
        than this value will be classified as outliers.

    Returns
    -------
        A bolean mask, of size numobservations-length array.

    .. note:: References
        Boris Iglewicz and David Hoaglin (1993), "Volume 16: How to Detect and
        Handle Outliers", The ASQC Basic References in Quality Control:
        Statistical Techniques, Edward F. Mykytka, Ph.D., Editor.
    """
    import numpy as np

    if len(points.shape) == 1:
        points = points[:, None]
    median = np.median(points, axis=0)
    diff = np.sum((points - median)**2, axis=-1)
    diff = np.sqrt(diff)
    med_abs_deviation = np.median(diff)

    modified_z_score = 0.6745 * diff / med_abs_deviation

    return modified_z_score > thresh