Python numpy.ptp() Examples
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Example #1
Source File: old_test_plotting.py From DABEST-python with BSD 3-Clause Clear License | 6 votes |
def test_swarmspan(): print('Testing swarmspans') base_mean = np.random.randint(10, 101) seed, ptp, df = create_dummy_dataset(base_mean=base_mean) print('\nSeed = {}; base mean = {}'.format(seed, base_mean)) for c in df.columns[1:-1]: print('{}...'.format(c)) f1, swarmplt = plt.subplots(1) sns.swarmplot(data=df[[df.columns[0], c]], ax=swarmplt) sns_yspans = [] for coll in swarmplt.collections: sns_yspans.append(get_swarm_yspans(coll)) f2, b = _api.plot(data=df, idx=(df.columns[0], c)) dabest_yspans = [] for coll in f2.axes[0].collections: dabest_yspans.append(get_swarm_yspans(coll)) for j, span in enumerate(sns_yspans): assert span == pytest.approx(dabest_yspans[j])
Example #2
Source File: test_statistics_execute.py From mars with Apache License 2.0 | 6 votes |
def testPtpExecution(self): x = arange(4, chunk_size=1).reshape(2, 2) t = ptp(x, axis=0) res = self.executor.execute_tensor(t, concat=True)[0] expected = np.ptp(np.arange(4).reshape(2, 2), axis=0) np.testing.assert_equal(res, expected) t = ptp(x, axis=1) res = self.executor.execute_tensor(t, concat=True)[0] expected = np.ptp(np.arange(4).reshape(2, 2), axis=1) np.testing.assert_equal(res, expected) t = ptp(x) res = self.executor.execute_tensor(t)[0] expected = np.ptp(np.arange(4).reshape(2, 2)) np.testing.assert_equal(res, expected)
Example #3
Source File: histograms.py From recruit with Apache License 2.0 | 6 votes |
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)
Example #4
Source File: histograms.py From recruit with Apache License 2.0 | 6 votes |
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)
Example #5
Source File: histograms.py From recruit with Apache License 2.0 | 6 votes |
def _hist_bin_rice(x, range): """ Rice histogram bin estimator. Another simple estimator with no normality assumption. It has better performance for large data than Sturges, but tends to overestimate the number of bins. The number of bins is proportional to the cube root of data size (asymptotically optimal). 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() / (2.0 * x.size ** (1.0 / 3))
Example #6
Source File: collision.py From ratcave with MIT License | 6 votes |
def __init__(self, parent=None, visible=True, drawmode=gl.GL_LINES, position=(0, 0, 0), **kwargs): """Calculates collision by checking if a point is inside a sphere around the mesh vertices.""" # kwargs['scale'] = np.ptp(parent.vertices, axis=0).max() / 2 if 'scale' not in kwargs else kwargs['scale'] # kwargs[''] from .wavefront import WavefrontReader from .resources import obj_primitives reader = WavefrontReader(obj_primitives) body = reader.bodies[self.primitive_shape] vertices, normals, texcoords = body['v'], body['vn'], body['vt'] super(ColliderBase, self).__init__(arrays=[vertices, normals, texcoords], drawmode=drawmode, visible=visible, position=position, **kwargs) self.uniforms['diffuse'] = 1., 0, 0 # Changes Scenegraph.parent execution order so self.scale can occur in the CollisionChecker parent property. if parent: self.parent = parent
Example #7
Source File: visualizer.py From kits19.MIScnn with GNU General Public License v3.0 | 6 votes |
def overlay_segmentation(vol, seg): # Scale volume to greyscale range vol_greyscale = (255*(vol - np.min(vol))/np.ptp(vol)).astype(int) # Convert volume to RGB vol_rgb = np.stack([vol_greyscale, vol_greyscale, vol_greyscale], axis=-1) # Initialize segmentation in RGB shp = seg.shape seg_rgb = np.zeros((shp[0], shp[1], shp[2], 3), dtype=np.int) # Set class to appropriate color seg_rgb[np.equal(seg, 1)] = [255, 0, 0] seg_rgb[np.equal(seg, 2)] = [0, 0, 255] # Get binary array for places where an ROI lives segbin = np.greater(seg, 0) repeated_segbin = np.stack((segbin, segbin, segbin), axis=-1) # Weighted sum where there's a value to overlay alpha = 0.3 vol_overlayed = np.where( repeated_segbin, np.round(alpha*seg_rgb+(1-alpha)*vol_rgb).astype(np.uint8), np.round(vol_rgb).astype(np.uint8) ) # Return final volume with segmentation overlay return vol_overlayed
Example #8
Source File: train.py From Generative-Adversarial-Networks-Cookbook with MIT License | 6 votes |
def plot_checkpoint(self,e): filename = "/data/sample_"+str(e)+".png" noise = self.sample_latent_space(16) images = self.generator.Generator.predict(noise) plt.figure(figsize=(10,10)) for i in range(images.shape[0]): plt.subplot(4, 4, i+1) if self.C==1: image = images[i, :, :] image = np.reshape(image, [self.H,self.W]) image = (255*(image - np.min(image))/np.ptp(image)).astype(int) plt.imshow(image,cmap='gray') elif self.C==3: image = images[i, :, :, :] image = np.reshape(image, [self.H,self.W,self.C]) image = (255*(image - np.min(image))/np.ptp(image)).astype(int) plt.imshow(image) plt.axis('off') plt.tight_layout() plt.savefig(filename) plt.close('all') return
Example #9
Source File: histograms.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
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)
Example #10
Source File: histograms.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
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)
Example #11
Source File: histograms.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def _hist_bin_rice(x, range): """ Rice histogram bin estimator. Another simple estimator with no normality assumption. It has better performance for large data than Sturges, but tends to overestimate the number of bins. The number of bins is proportional to the cube root of data size (asymptotically optimal). 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() / (2.0 * x.size ** (1.0 / 3))
Example #12
Source File: tritools.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def scale_factors(self): """ Factors to rescale the triangulation into a unit square. Returns *k*, tuple of 2 scale factors. Returns ------- k : tuple of 2 floats (kx, ky) Tuple of floats that would rescale the triangulation : ``[triangulation.x * kx, triangulation.y * ky]`` fits exactly inside a unit square. """ compressed_triangles = self._triangulation.get_masked_triangles() node_used = (np.bincount(np.ravel(compressed_triangles), minlength=self._triangulation.x.size) != 0) return (1 / np.ptp(self._triangulation.x[node_used]), 1 / np.ptp(self._triangulation.y[node_used]))
Example #13
Source File: tritools.py From GraphicDesignPatternByPython with MIT License | 6 votes |
def scale_factors(self): """ Factors to rescale the triangulation into a unit square. Returns *k*, tuple of 2 scale factors. Returns ------- k : tuple of 2 floats (kx, ky) Tuple of floats that would rescale the triangulation : ``[triangulation.x * kx, triangulation.y * ky]`` fits exactly inside a unit square. """ compressed_triangles = self._triangulation.get_masked_triangles() node_used = (np.bincount(np.ravel(compressed_triangles), minlength=self._triangulation.x.size) != 0) return (1 / np.ptp(self._triangulation.x[node_used]), 1 / np.ptp(self._triangulation.y[node_used]))
Example #14
Source File: univariate.py From mne-features with BSD 3-Clause "New" or "Revised" License | 6 votes |
def compute_ptp_amp(data): """Peak-to-peak (PTP) amplitude of the data (per channel). Parameters ---------- data : ndarray, shape (n_channels, n_times) Returns ------- output : ndarray, shape (n_channels,) Notes ----- Alias of the feature function: **ptp_amp** """ return np.ptp(data, axis=-1)
Example #15
Source File: stat_ydensity.py From plotnine with GNU General Public License v2.0 | 6 votes |
def compute_group(cls, data, scales, **params): n = len(data) if n == 0: return pd.DataFrame() weight = data.get('weight') if params['trim']: range_y = data['y'].min(), data['y'].max() else: range_y = scales.y.dimension() dens = compute_density(data['y'], weight, range_y, **params) dens['y'] = dens['x'] dens['x'] = np.mean([data['x'].min(), data['x'].max()]) # Compute width if x has multiple values if len(np.unique(data['x'])) > 1: dens['width'] = np.ptp(data['x']) * 0.9 return dens
Example #16
Source File: prep_ascii.py From ASR33 with MIT License | 6 votes |
def load_image(filename): if filename in loaded_files: return loaded_files[filename] img = imageio.imread(filename, as_gray=True).astype(np.float) # Normalize the whole image # img *= 1.0/(img.max() - img.min()) img = (img - np.min(img))/np.ptp(img) # Normalize on a sigmoid curve to better separate ink from paper k = 10 img = np.sqrt(1 / (1 + np.exp(k * (img - 0.5)))) loaded_files[filename] = img return img # Pull out the image of a single character. # Each character has multiple images, specify index (0-5) to choose one
Example #17
Source File: autoreject.py From autoreject with BSD 3-Clause "New" or "Revised" License | 6 votes |
def _vote_bad_epochs(self, epochs, picks): """Each channel votes for an epoch as good or bad. Parameters ---------- epochs : instance of mne.Epochs The epochs object for which bad epochs must be found. picks : array-like The indices of the channels to consider. """ labels = np.zeros((len(epochs), len(epochs.ch_names))) labels.fill(np.nan) bad_sensor_counts = np.zeros((len(epochs),)) this_ch_names = [epochs.ch_names[p] for p in picks] deltas = np.ptp(epochs.get_data()[:, picks], axis=-1).T threshes = [self.threshes_[ch_name] for ch_name in this_ch_names] for ch_idx, (delta, thresh) in enumerate(zip(deltas, threshes)): bad_epochs_idx = np.where(delta > thresh)[0] labels[:, picks[ch_idx]] = 0 labels[bad_epochs_idx, picks[ch_idx]] = 1 bad_sensor_counts = np.sum(labels == 1, axis=1) return labels, bad_sensor_counts
Example #18
Source File: autoreject.py From autoreject with BSD 3-Clause "New" or "Revised" License | 6 votes |
def fit(self, X, y=None): """Fit it. Parameters ---------- X : array, shape (n_epochs, n_times) The data for one channel. y : None Redundant. Necessary to be compatible with sklearn API. """ deltas = np.ptp(X, axis=1) self.deltas_ = deltas keep = deltas <= self.thresh # XXX: actually go over all the folds before setting the min # in skopt. Otherwise, may confuse skopt. if self.thresh < np.min(np.ptp(X, axis=1)): assert np.sum(keep) == 0 keep = deltas <= np.min(np.ptp(X, axis=1)) self.mean_ = _slicemean(X, keep, axis=0) return self
Example #19
Source File: histograms.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
def _hist_bin_rice(x, range): """ Rice histogram bin estimator. Another simple estimator with no normality assumption. It has better performance for large data than Sturges, but tends to overestimate the number of bins. The number of bins is proportional to the cube root of data size (asymptotically optimal). 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() / (2.0 * x.size ** (1.0 / 3))
Example #20
Source File: histograms.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
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)
Example #21
Source File: histograms.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
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)
Example #22
Source File: tritools.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 6 votes |
def scale_factors(self): """ Factors to rescale the triangulation into a unit square. Returns *k*, tuple of 2 scale factors. Returns ------- k : tuple of 2 floats (kx, ky) Tuple of floats that would rescale the triangulation : ``[triangulation.x * kx, triangulation.y * ky]`` fits exactly inside a unit square. """ compressed_triangles = self._triangulation.get_masked_triangles() node_used = (np.bincount(np.ravel(compressed_triangles), minlength=self._triangulation.x.size) != 0) return (1 / np.ptp(self._triangulation.x[node_used]), 1 / np.ptp(self._triangulation.y[node_used]))
Example #23
Source File: lucidDream.py From pyLucid with MIT License | 6 votes |
def spline_transform_multi(img, mask): bimask=mask>0 M,N=np.where(bimask) w=np.ptp(N)+1 h=np.ptp(M)+1 kernel=cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)) bound=cv2.dilate(bimask.astype('uint8'),kernel)-bimask y,x=np.where(bound>0) if x.size>4: newxy=thin_plate_transform(x,y,w,h,mask.shape[:2],num_points=5) new_img=cv2.remap(img,newxy,None,cv2.INTER_LINEAR) new_msk=cv2.remap(mask,newxy,None,cv2.INTER_NEAREST) elif x.size>0: new_img=img new_msk=mask return new_img,new_msk
Example #24
Source File: histograms.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
def _hist_bin_doane(x, range): """ Doane's histogram bin estimator. Improved version of Sturges' formula which works better for non-normal data. See stats.stackexchange.com/questions/55134/doanes-formula-for-histogram-binning 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 if x.size > 2: sg1 = np.sqrt(6.0 * (x.size - 2) / ((x.size + 1.0) * (x.size + 3))) sigma = np.std(x) if sigma > 0.0: # These three operations add up to # g1 = np.mean(((x - np.mean(x)) / sigma)**3) # but use only one temp array instead of three temp = x - np.mean(x) np.true_divide(temp, sigma, temp) np.power(temp, 3, temp) g1 = np.mean(temp) return x.ptp() / (1.0 + np.log2(x.size) + np.log2(1.0 + np.absolute(g1) / sg1)) return 0.0
Example #25
Source File: collision.py From ratcave with MIT License | 5 votes |
def _fit_to_parent_vertices(self, vertices, scale_gain=1e5): axes = [a for a in range(3) if a != self.ignore_axis] x, z = np.ptp(vertices[:, axes], axis=0) / 2 return x, scale_gain, z # scale_gain makes it clear in the display that one dimension is being ignored.
Example #26
Source File: utils.py From BigGAN-TPU-TensorFlow with MIT License | 5 votes |
def imwrite(file, data): # Normalised [0,255] as integer d = 255 * (data - np.min(data)) / np.ptp(data) d = d.astype(np.uint8) imageio.imwrite(file, d, format="png")
Example #27
Source File: autoreject.py From autoreject with BSD 3-Clause "New" or "Revised" License | 5 votes |
def fit(self, X, y=None): """Fit it.""" if self.n_channels is None or self.n_times is None: raise ValueError('Cannot fit without knowing n_channels' ' and n_times') X = X.reshape(-1, self.n_channels, self.n_times) deltas = np.array([np.ptp(d, axis=1) for d in X]) epoch_deltas = deltas.max(axis=1) keep = epoch_deltas <= self.thresh self.mean_ = _slicemean(X, keep, axis=0) return self
Example #28
Source File: test_numeric.py From pySINDy with MIT License | 5 votes |
def test_ptp(self): a = [3, 4, 5, 10, -3, -5, 6.0] assert_equal(np.ptp(a, axis=0), 15.0)
Example #29
Source File: test_matterport.py From LayoutNetv2 with MIT License | 5 votes |
def get_ini_cor(cor_img, d1=21, d2=3): cor = convolve(cor_img, np.ones((d1, d1)), mode='constant', cval=0.0) cor_id = [] cor_ = cor_img.sum(0) cor_ = (cor_-np.amin(cor_))/np.ptp(cor_) min_v = 0.25#0.05 xs_ = find_N_peaks(cor_, r=26, min_v=min_v, N=None)[0] # spetial case for too less corner if xs_.shape[0] < 4: xs_ = find_N_peaks(cor_, r=26, min_v=0.05, N=None)[0] if xs_.shape[0] < 4: xs_ = find_N_peaks(cor_, r=26, min_v=0, N=None)[0] X_loc = xs_ for x in X_loc: x_ = int(np.round(x)) V_signal = cor[:, max(0, x_-d2):x_+d2+1].sum(1) y1, y2 = find_N_peaks_conv(V_signal, prominence=None, distance=20, N=2)[0] cor_id.append((x, y1)) cor_id.append((x, y2)) cor_id = np.array(cor_id, np.float64) return cor_id
Example #30
Source File: contour.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 5 votes |
def print_label(self, linecontour, labelwidth): "Return *False* if contours are too short for a label." return (len(linecontour) > 10 * labelwidth or (np.ptp(linecontour, axis=0) > 1.2 * labelwidth).any())