Python numpy.percentile() Examples

def _add_bootstrapped_inputs(self, base_algorithm, batch_sample_method,
                                 nsamples, njobs_samples, percentile,
                                 ts_byrow = False, ts_weighted = False):
        assert (batch_sample_method == 'gamma') or (batch_sample_method == 'poisson')
        assert isinstance(nsamples, int)
        assert nsamples >= 1
        self.batch_sample_method = batch_sample_method
        self.nsamples = nsamples
        self.njobs_samples = _check_njobs(njobs_samples)
        if not isinstance(base_algorithm, list):
            self.base_algorithm = self._make_bootstrapped(base_algorithm, percentile,
                                                          ts_byrow, ts_weighted)
        else:
            self.base_algorithm = [ \
                self._make_bootstrapped(alg, percentile, ts_byrow, ts_weighted) \
                for alg in base_algorithm] 

def reset_percentile(self, percentile=80):
        """
        Set the upper confidence bound percentile to a custom number

        Parameters
        ----------
        percentile : int [0,100]
            Percentile of the confidence interval to take.

        Returns
        -------
        self : obj
            This object
        """
        assert (percentile > 0) and (percentile < 100)
        if self.is_fitted:
            self._oracles.reset_attribute("percentile", percentile)
        self.base_algorithm.percentile = percentile
        return self 

def __init__(self, nchoices, percentile=80, fit_intercept=True,
                 lambda_=1.0, ucb_from_empty=False,
                 beta_prior='auto', smoothing=None, noise_to_smooth=True,
                 assume_unique_reward=False,
                 random_state=None, njobs=-1):
        assert (percentile > 0) and (percentile < 100)
        assert lambda_ > 0.
        base = _LogisticUCB_n_TS_single(lambda_=float(lambda_),
                                        fit_intercept=fit_intercept,
                                        alpha=float(percentile),
                                        ts=False)
        self._add_common_params(base, beta_prior, smoothing, noise_to_smooth, njobs, nchoices,
                                False, None, False, assume_unique_reward,
                                random_state, assign_algo=True, prior_def_ucb=True,
                                force_unfit_predict = ucb_from_empty)
        self.percentile = percentile 

def reset_threshold(self, threshold="auto"):
        """
        Set the adaptive threshold to a custom number

        Parameters
        ----------
        threshold : float or "auto"
            New threshold to use. If passing "auto", will set it
            to 1.5/nchoices. Note that this threshold will still be
            decayed if the object was initialized with ``decay_type="threshold"``,
            and will still be updated if initialized with ``percentile != None``.

        Returns
        -------
        self : obj
            This object
        """
        if isinstance(threshold, int):
            threshold = float(threshold)
        elif threshold == "auto":
            threshold = 1.5 / self.nchoices
        assert isinstance(threshold, float)
        self.thr = threshold
        return self 

def reset_percentile(self, percentile=30):
        """
        Set the moving percentile to a custom number

        Parameters
        ----------
        percentile : int between 0 and 100
            The new percentile to set. Note that it will still apply
            decay to it after being set through this method.

        Returns
        -------
        self : obj
            This object
        """
        if self.decay_type == 'threshold':
            raise ValueError("Method is not available when not using percentile decay.")
        assert percentile >= 0
        assert percentile <= 100
        self.percentile = percentile
        return self 

def reset_percentile(self, percentile=80):
        """
        Set the upper confidence bound percentile to a custom number

        Parameters
        ----------
        percentile : int [0,100]
            Percentile of the confidence interval to take.

        Returns
        -------
        self : obj
            This object
        """
        assert (percentile > 0) and (percentile < 100)
        if self.is_fitted:
            self._oracles.reset_attribute("alpha", percentile)
        self.base_algorithm.alpha = percentile
        return self 

def agg_func(request):
    agg_func_name = request.param

    if agg_func_name == "custom":
        # When using custom you assign the function rather than a string.
        agg_func_name = npy_func = custom_test_func
    elif agg_func_name == "percentile":
        agg_func_name = {
            "func": "percentile",
            "args": [95],
            "kwargs": {}
        }
        npy_func = partial(np.percentile, q=95)
    elif agg_func_name == "percentileofscore":
        agg_func_name = {
            "func": "percentileofscore",
            "kwargs": {
                "score": 0.5,
                "kind": "rank"
            }
        }
        npy_func = partial(percentileofscore_with_axis, score=0.5, kind="rank")
    else:
        npy_func = npy_funcs[agg_func_name]
    return agg_func_name, npy_func 

def test_seasonal_fdc_recorder(self):
        """
        Test the FlowDurationCurveRecorder
        """
        model = load_model("timeseries4.json")

        df = pandas.read_csv(os.path.join(os.path.dirname(__file__), 'models', 'timeseries3.csv'),
                             parse_dates=True, dayfirst=True, index_col=0)

        percentiles = np.linspace(20., 100., 5)

        summer_flows = df.loc[pandas.Timestamp("2014-06-01"):pandas.Timestamp("2014-08-31"), :]
        summer_fdc = np.percentile(summer_flows, percentiles, axis=0)

        model.run()

        rec = model.recorders["seasonal_fdc"]
        assert_allclose(rec.fdc, summer_fdc) 

def analyse_percentile(cpdf, col):
        percentile = [1, 5, 25, 50, 75, 95, 99]
        r = [round(d, 3) for d in np.percentile(list(cpdf[col]), percentile)]
        print(
            "\n预测偏差分位:",
            "\n1% 分位: ",
            r[0],
            "\n5% 分位: ",
            r[1],
            "\n25% 分位: ",
            r[2],
            "\n50% 分位: ",
            r[3],
            "\n75% 分位: ",
            r[4],
            "\n95% 分位: ",
            r[5],
            "\n99% 分位: ",
            r[6],
        ) 

def __init__(self, img, percentiles=[1, 99]):
        """Create norm that is linear between lower and upper percentile of img
        Parameters
        ----------
        img: array_like
            Image to normalize
        percentile: array_like, default=[1,99]
            Lower and upper percentile to consider. Pixel values below will be
            set to zero, above to saturated.
        """
        assert len(percentiles) == 2
        vmin, vmax = np.percentile(img, percentiles)
        # solution for beta assumes flat spectrum at vmax
        stretch = vmax - vmin
        beta = stretch / np.sinh(1)
        super().__init__(minimum=vmin, stretch=stretch, Q=beta) 

def _compute_data_weights_topk(self, opts, density_ratios):
        """Put a uniform distribution on K points with largest prob real data.

        This is a naiive heuristic which makes next GAN concentrate on those
        points of the training set, which were classified correctly with
        largest margins. I.e., out current mixture model is not capable of
        generating points looking similar to these ones.
        """
        threshold = np.percentile(density_ratios,
                                  opts["topk_constant"]*100.0)
        # Note that largest prob_real_data corresponds to smallest density
        # ratios.
        mask = density_ratios <= threshold
        data_weights = np.zeros(self._data_num)
        data_weights[mask] = 1.0 / np.sum(mask)
        return data_weights 

def bench(f_):
    timings_fwd = []
    timings_bck = []
    for _ in range(100):

        with f_ as f:
            tic = time.perf_counter()
            f.forward()
            torch.cuda.synchronize()
            toc = time.perf_counter()
            timings_fwd.append(toc - tic)

            tic = time.perf_counter()
            f.backward()
            torch.cuda.synchronize()
            toc = time.perf_counter()
            timings_bck.append(toc - tic)

    return (np.percentile(timings_fwd, [25, 50, 75]),
            np.percentile(timings_bck, [25, 50, 75])) 

def scale_EVI(evi, periods, qmin=10, qmax=90):
    """ Returns EVI scaled to upper and lower quantiles

    Quantiles are calculated based on EVI within some year-to-year interval.
    As part of finding the quantiles, EVI values not within the (0, 1) range
    will be removed.

    Args:
        evi (np.ndarray): EVI values
        periods (np.ndarray): intervals of years to group and scale together
        qmin (float, optional): lower quantile for scaling (default: 10)
        qmax (float, optional): upper quantile for scaling (default: 90)

    Returns:
        np.ndarray: scaled EVI array

    """
    _evi = evi.copy()
    for u in np.unique(periods):
        index = np.where(periods == u)
        evi_min = np.percentile(evi[index], qmin)
        evi_max = np.percentile(evi[index], qmax)
        _evi[index] = (evi[index] - evi_min) / (evi_max - evi_min)

    return _evi 

def _nanquantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False,
                              interpolation='linear'):
    """
    Private function that doesn't support extended axis or keepdims.
    These methods are extended to this function using _ureduce
    See nanpercentile for parameter usage
    """
    if axis is None or a.ndim == 1:
        part = a.ravel()
        result = _nanquantile_1d(part, q, overwrite_input, interpolation)
    else:
        result = np.apply_along_axis(_nanquantile_1d, axis, a, q,
                                     overwrite_input, interpolation)
        # apply_along_axis fills in collapsed axis with results.
        # Move that axis to the beginning to match percentile's
        # convention.
        if q.ndim != 0:
            result = np.moveaxis(result, axis, 0)

    if out is not None:
        out[...] = result
    return result 

def test_keepdims(self):
        d = np.ones((3, 5, 7, 11))
        assert_equal(np.percentile(d, 7, axis=None, keepdims=True).shape,
                     (1, 1, 1, 1))
        assert_equal(np.percentile(d, 7, axis=(0, 1), keepdims=True).shape,
                     (1, 1, 7, 11))
        assert_equal(np.percentile(d, 7, axis=(0, 3), keepdims=True).shape,
                     (1, 5, 7, 1))
        assert_equal(np.percentile(d, 7, axis=(1,), keepdims=True).shape,
                     (3, 1, 7, 11))
        assert_equal(np.percentile(d, 7, (0, 1, 2, 3), keepdims=True).shape,
                     (1, 1, 1, 1))
        assert_equal(np.percentile(d, 7, axis=(0, 1, 3), keepdims=True).shape,
                     (1, 1, 7, 1))

        assert_equal(np.percentile(d, [1, 7], axis=(0, 1, 3),
                                   keepdims=True).shape, (2, 1, 1, 7, 1))
        assert_equal(np.percentile(d, [1, 7], axis=(0, 3),
                                   keepdims=True).shape, (2, 1, 5, 7, 1)) 

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.percentile(mat, 42, axis=1)
        res = np.nanpercentile(nan_mat, 42, axis=1, out=resout)
        assert_almost_equal(res, resout)
        assert_almost_equal(res, tgt)
        # 0-d output:
        resout = np.zeros(())
        tgt = np.percentile(mat, 42, axis=None)
        res = np.nanpercentile(nan_mat, 42, axis=None, out=resout)
        assert_almost_equal(res, resout)
        assert_almost_equal(res, tgt)
        res = np.nanpercentile(nan_mat, 42, axis=(0, 1), out=resout)
        assert_almost_equal(res, resout)
        assert_almost_equal(res, tgt) 

def test_multiple_percentiles(self):
        perc = [50, 100]
        mat = np.ones((4, 3))
        nan_mat = np.nan * mat
        # For checking consistency in higher dimensional case
        large_mat = np.ones((3, 4, 5))
        large_mat[:, 0:2:4, :] = 0
        large_mat[:, :, 3:] *= 2
        for axis in [None, 0, 1]:
            for keepdim in [False, True]:
                with suppress_warnings() as sup:
                    sup.filter(RuntimeWarning, "All-NaN slice encountered")
                    val = np.percentile(mat, perc, axis=axis, keepdims=keepdim)
                    nan_val = np.nanpercentile(nan_mat, perc, axis=axis,
                                               keepdims=keepdim)
                    assert_equal(nan_val.shape, val.shape)

                    val = np.percentile(large_mat, perc, axis=axis,
                                        keepdims=keepdim)
                    nan_val = np.nanpercentile(large_mat, perc, axis=axis,
                                               keepdims=keepdim)
                    assert_equal(nan_val, val)

        megamat = np.ones((3, 4, 5, 6))
        assert_equal(np.nanpercentile(megamat, perc, axis=(1, 2)).shape, (2, 3, 6)) 

def test_quantile_multi(self):

        qs = [.1, .9]
        result = self.ts.quantile(qs)
        expected = pd.Series([np.percentile(self.ts.dropna(), 10),
                              np.percentile(self.ts.dropna(), 90)],
                             index=qs, name=self.ts.name)
        tm.assert_series_equal(result, expected)

        dts = self.ts.index.to_series()
        dts.name = 'xxx'
        result = dts.quantile((.2, .2))
        expected = Series([Timestamp('2000-01-10 19:12:00'),
                           Timestamp('2000-01-10 19:12:00')],
                          index=[.2, .2], name='xxx')
        tm.assert_series_equal(result, expected)

        result = self.ts.quantile([])
        expected = pd.Series([], name=self.ts.name, index=Index(
            [], dtype=float))
        tm.assert_series_equal(result, expected) 

def readLengthUpperQuantile(self):
        if self.hasReadLengthDistribution():
            return numpy.percentile(self.readLengths, 99)
        return None 

def compute_stats(scores):
    """compute_stats."""
    median = np.percentile(scores, 50, axis=1)
    low = np.percentile(scores, 25, axis=1)
    high = np.percentile(scores, 75, axis=1)
    low10 = np.percentile(scores, 10, axis=1)
    high90 = np.percentile(scores, 90, axis=1)
    return median, low, high, low10, high90 

def show_graph(g, vertex_color='typeof', size=15, vertex_label=None):
    """show_graph."""
    degrees = [len(g.neighbors(u)) for u in g.nodes()]

    print(('num nodes=%d' % len(g)))
    print(('num edges=%d' % len(g.edges())))
    print(('num non edges=%d' % len(list(nx.non_edges(g)))))
    print(('max degree=%d' % max(degrees)))
    print(('median degree=%d' % np.percentile(degrees, 50)))

    draw_graph(g, size=size,
               vertex_color=vertex_color, vertex_label=vertex_label,
               vertex_size=200, edge_label=None)

    # display degree distribution
    size = int((max(degrees) - min(degrees)) / 1.5)
    plt.figure(figsize=(size, 3))
    plt.title('Degree distribution')
    _bins = np.arange(min(degrees), max(degrees) + 2) - .5
    n, bins, patches = plt.hist(degrees, _bins,
                                alpha=0.3,
                                facecolor='navy', histtype='bar',
                                rwidth=0.8, edgecolor='k')
    labels = np.array([str(int(i)) for i in n])
    for xi, yi, label in zip(bins, n, labels):
        plt.text(xi + 0.5, yi, label, ha='center', va='bottom')

    plt.xticks(bins + 0.5)
    plt.xlim((min(degrees) - 1, max(degrees) + 1))
    plt.ylim((0, max(n) * 1.1))
    plt.xlabel('Node degree')
    plt.ylabel('Counts')
    plt.grid(linestyle=":")
    plt.show() 

def __call__(self, scenarios, neutral_scenario=0):
        pnls = scenarios - neutral_scenario
        return - np.percentile(pnls, 100-self.level, interpolation='linear'), pnls 

def compute_var(self, scenarios, neutral_scenario, level=99.):
        pnls = scenarios - neutral_scenario
        return - np.percentile(pnls, 100-level, interpolation='linear'), pnls 

def __on_scan_data(self, event):
        levels = numpy.log10(event['l'])
        levels *= 10

        noise = numpy.percentile(levels,
                                 self._dynP)

        for monitor in self._monitors:
            freq = monitor.get_frequency()
            if monitor.get_enabled():
                monitor.set_noise(noise)
                index = numpy.where(freq == event['f'])[0]
                signal = monitor.set_level(levels[index][0],
                                           event['timestamp'],
                                           self._location)

                if signal is not None:
                    signals = 'Signals: {}\r'.format(self.__count_signals() -
                                                     self._signalCount)
                    self.__std_out(signals, False)
                    if signal.end is not None:
                        recording = format_recording(freq, signal)
                        if self._pushUri is not None:
                            self._push.send(self._pushUri,
                                            recording)
                        if self._server is not None:
                            self._server.send(recording)
                        if self._json:
                            sys.stdout.write(recording + '\n') 

def __on_scan_data(self, event):
        levels = numpy.log10(event['l'])
        levels *= 10
        self._levels = levels

        noise = numpy.percentile(levels,
                                 self._toolbar.get_dynamic_percentile())

        updated = False
        for monitor in self._monitors:
            freq = monitor.get_frequency()
            if monitor.get_enabled():
                monitor.set_noise(noise)
                index = numpy.where(freq == event['f'])[0]
                signal = monitor.set_level(levels[index][0],
                                           event['timestamp'],
                                           self._location)
                if signal is not None:
                    updated = True
                    if signal.end is not None:
                        recording = format_recording(freq, signal)
                        if self._settings.get_push_enable():
                            self._push.send(self._settings.get_push_uri(),
                                            recording)
                        if self._server is not None:
                            self._server.send(recording)

        if updated:
            if self._isSaved:
                self._isSaved = False
                self.__set_title()
                self.__set_timeline()

        self.__set_spectrum(noise)
        self._rssi.set_noise(numpy.mean(levels))
        self._rssi.set_level(numpy.max(levels)) 

def distance_to_victim(self, img):
        emb = self.eval_embeddings([img])
        dist = np.dot(emb, self.victim_embeddings.T).flatten()
        stats = np.percentile(dist, [10, 30, 50, 70, 90])
        return stats 

def to_grayscale(cv2im):
    """Convert gradients to grayscale. This gives a saliency map."""
    # How strongly does each position activate the output
    grayscale_im = np.sum(np.abs(cv2im), axis=0)

    # Normalize between min and 99th percentile
    im_max = np.percentile(grayscale_im, 99)
    im_min = np.min(grayscale_im)
    grayscale_im = np.clip((grayscale_im - im_min) / (im_max - im_min), 0, 1)

    grayscale_im = np.expand_dims(grayscale_im, axis=0)
    return grayscale_im 

def _make_bootstrapped(self, base_algorithm, percentile,
                           ts_byrow, ts_weighted):
        if "predict_proba" in dir(base_algorithm):
            return _BootstrappedClassifier_w_predict_proba(
                base_algorithm, self.nsamples, percentile,
                self.batch_train, self.batch_sample_method,
                random_state = 1, ### gets changed later
                njobs = self.njobs_samples,
                ts_byrow = ts_byrow,
                ts_weighted = ts_weighted
                )
        elif "decision_function" in dir(base_algorithm):
            return _BootstrappedClassifier_w_decision_function(
                base_algorithm, self.nsamples, percentile,
                self.batch_train, self.batch_sample_method,
                random_state = 1, ### gets changed later
                njobs = self.njobs_samples,
                ts_byrow = ts_byrow,
                ts_weighted = ts_weighted
                )
        else:
            return _BootstrappedClassifier_w_predict(
                base_algorithm, self.nsamples, percentile,
                self.batch_train, self.batch_sample_method,
                random_state = 1, ### gets changed later
                njobs = self.njobs_samples,
                ts_byrow = ts_byrow,
                ts_weighted = ts_weighted
                ) 

def __init__(self, base_algorithm, nchoices, nsamples=10, percentile=80,
                 beta_prior='auto', smoothing=None, noise_to_smooth=True, batch_train=False,
                 refit_buffer=None, deep_copy_buffer=True,
                 assume_unique_reward=False, batch_sample_method='gamma',
                 random_state=None, njobs_arms=-1, njobs_samples=1):
        assert (percentile > 0) and (percentile < 100)
        assert nsamples >= 2
        self._add_common_params(base_algorithm, beta_prior, smoothing, noise_to_smooth, njobs_arms,
                                nchoices, batch_train, refit_buffer, deep_copy_buffer,
                                assume_unique_reward, random_state,
                                assign_algo = False, prior_def_ucb = True)
        self.percentile = percentile
        self._add_bootstrapped_inputs(base_algorithm, batch_sample_method, nsamples, njobs_samples, self.percentile) 

def _apply_decay(self, nobs):
        if (self.decay is not None) and (self.decay != 1):
            if self.decay_type == 'threshold':
                self.thr *= self.decay ** nobs
            elif self.decay_type == 'percentile':
                self.percentile *= self.decay ** nobs
            else:
                raise ValueError("'decay_type' must be one of 'threshold' or 'percentile'")